2024Activity reportProject-TeamHEKA

3.1.1 Methods

Methods developed in Axis 1 can be associated with three type of tasks: (i) deep phenotyping, (ii) patient representation and (iii) reasoning with clinical knowledge. (i) Deep phenotyping consists in defining algorithms that enable to identify patients with a particular and potentially complex profile within large healthcare databases. It encompasses the development of natural language processing tools capable of extracting complex features and their context out of clinical texts; it also includes the ability to consider simultaneously structured and unstructured data of these databases to identify relevant patients. To this aim our methods rely on expert rules, distant-supervision and deep learning language representations. (ii) Regarding patient representation we focus on two distinct kind of representations. The first one is an explicit representation of patients in the form of knowledge graphs, using Semantic Web standards and tools. The second one is a representation of patients within a latent space, using representation learning methods largely inspired from results obtained by deep learning models to learn language representations. (iii) Tasks concerned with reasoning on clinical knowledge are mutliple. It encompasses methods to measure patients’ similarity between elaborated patients representations (either in the form of knowledge graph or embeddings), hybrid approaches for analogical reasoning and logical and statistical inference.

3.2.1 Methods

During this year, we focused on adapting machine learning based methods on the following challenges: signal detection using spatio-temporal data to be able to build health geographical vigilance system, medical image classification in low sample setting, pharmacovigilance signal detection using large database. We proposed a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data changes based on a new version of the Expectation-Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the same time. We validate this approach on a real data set of positive diagnosed patients to coronavirus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes. We proposed a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size setting using a geometry-based variational autoencoder. We validated this approach on a medical imaging classification task where a small number of 3D brain magnetic resonance images are considered and augmented using the proposed framework. We adapted a very classical pharmacoepidemiological method to high dimensional setting, the Weigthed Cumulative Exposure statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and a side effect, by implementing it using Graphics Processing Unit (GPU) programming. We analysed several real life datasets using this implementation and showed that it could now be scaled to the millions of patients of the National Health Insurance Database (SNDS).

3.3.1 Methods

This year we focused more on the second objective while starting working on the first one. We developed (i) dose-finding methods for first-in-human trials in healthy volunteers using an activity endpoint as surrogate for efficacy; (ii) a framework to include and select several preclinical sources when stepping to clinical trial and selecting the dose-range in human; (iii) methods which allow for analsing disruped clinical trial by COVID pandemic ; (iv) deep learning models that allow to analyse multistate EHR .

4.1.1 PEPR Digital Health

The PEPR ("Programmes et équipements prioritaires de recherche") Digital Health aims at gathering national multidisciplinary community active in digital health for the development and exploitation of the concept of digital twin in health (started in September 2023).

HeKA’s involvement in this PEPR are the following; (i) within project ShareFAIR, to learn protocols from clinical data collected along healthcare activity in Electronic Health Records (EHRs) to explicitize the medical decision processes, (steps to reach a particular diagnosis or therapeutic choice) and the management of particular conditions (steps in the management of a particular condition). Protocols extracted from EHRs provide a view on the real-word clinical practice and may then be compared together or with CPG (clinical practice guidelines) which can be seen as more theoretical protocols in that they provide recommendations, or clinical pathways (CP) to standardize clinical practice. It will be applied within NEUROVASC in the impact reduction of intracranial aneurysm and stroke, in which we will extract and the proposed clinical pathways, (ii) within REWIND, to develop of new mathematical and statistical approaches for the analysis of multimodal multiscale longitudinal data to predict patient’s response. These models will be designed, implemented as prototypes and then transferred to an easy-used-well-documented platform where people from diverse communities, in particular physicians, will be able to use them on their own data set, (iii) within DIGPHAT, to develop Bayesian modelling of meta-models pathways for the development of digital pharmacological twin; it consists in the analysis of data from omics experiments and selection of relevant covariables and to combine meta-models in pathways to select the most reliable twin model, (iv) within project M4DI, to develop a generic method for identifying subgroups of patients with the same phenotype from health databases, using jointly variable correlations and expert data, and to implement it within a computer package, (v) all these previous projects will purpose models or Clinical Decision Support Systems (CDSS) to be translated to clinical practice, however, proof based on data only is not sufficient and it should be evaluated in real life through prospective and interventional clinical trials or studies, this will be done within SMATCH. In this project we will propose new methodological paradigms for the clinical evaluation of Digital Medical Devices (DMD) including CDSS and AI based models and algorithms.

4.1.2 SurvivalGPU – “Using Graphics Processing Units (GPUs) to scale up survival analysis to nation-wide cohorts"

The recent availability of health insurance databases such as the SNDS opens the door to the detection of adverse drug reactions in the general population. The aim is to generalize the survival analyses usually carried out during clinical trials on cohorts of N=1k to 10k patients to the full French population. This line of research is appealing but poses real methodological challenges. Notably, it requires the development of statistical analysis models that meet the robustness and interpretability requirements of public health physicians while taking full advantage of recent hardware accelerators to scale up to millions of patients per cohort. In this context, our team has been working since 2022 on an efficient re-implementation, on Graphics Processing Units (GPUs), of the standard software tool in the field: the R package "survival". The new "survivalGPU" library leverages recent software tools (PyTorch, PyG, KeOps, reticulate) to bridge the gap between high-performance computing and traditional survival analysis. It now provides a complete re-implementation of the Cox proportional hazards model that is around 100 times faster on GPU than the survival package on CPU. Going further, it supports time-varying drug exposures via the Weighted Cumulative Exposures model and is accessible via an R interface which is fully retro-compatible with that of the survival package. We now intend to perform extensive validation and comparison with other models, prior to pharmaco-epidemiological studies on the SNDS data via the Health Data Hub platform.

4.1.3 Messidore-Inserm BEEP - “Bayesian methods for Early Enriched Platform trials"

The recent pandemic has shown the need of speeding up the clinical trial development of novel or repurposed therapies. Indeed, following the usual drug development paradigm, where clinical trial phases are performed sequentially and separately, the time required to the full process easily exceeds a decade. Our objective is to propose innovative Bayesian enriched “platform” designs for early phase trials, which are adapted to the clinical context and go towards precision medicine. Since we are focusing on early phases of clinical trial, in this setting “platform” cannot be linked to classical RCT. Thus, we aim at defining how “platform” trial should be translated into these early phases. As in the original definition, early platform phases will allow for flexibility, such as adding new arm or stopping treatments for futility (and/or safety in our case). The word enriched refers to the use of new information, or at least not usually used in such early trials, such as positron emission tomography (PET) scan, pharmacokinetics/pharmacodynamics (PK/PD) modelling, mathematical modelling of immune responses, and to the enrichment of the enrolled patient based on their biomarkers. The project is built around workpackages (WPs). In WP1, we develop platform trials in phase 0/I, based on PET-scan; microdosing on several (preclinical) animal species and humans will be adaptively compared, added or deleted to better characterize the extrapolation to human. In WP2, we develop phase I/II dose-finding trial using PK/PD or mechanistic PD models. In WP3, enrichment designs for phase I/II, in survival settings, are proposed when selected biomarkers are available, and the design will be extended in case of combination therapies.

4.1.4 ANR AT2TA - “Analogies: from Theory to Tools and Applications"

Analogical reasoning is a kind of reasoning that is based on finding a common relational system between two situations, exemplars, or domains. In computer science, analogical reasoning can be supported by two main axes of artificial intelligence: knowledge representation and reasoning, and machine learning. The AT2TA projet particularly aims at studying the role that machine learning can play in analogical reasoning; and the HeKA team is in charge of exploring the application of their interplay in the healthcare domain. A PhD student, co-supervised with Inria Paris, IHU Imagine and Université de Lorraine, is learning representations of patients, relying on clinical texts, and study how these representations can first compose analogical propositions, and second serve as bricks to a machine learning architecture for analogical reasoning.

4.1.5 HDH BOAS ADHERENCE - “Phenotyping algorithms for the assessment of therapeutic adherence and its clinical consequences in the management of chronic diseases"

Therapeutic adherence is a complex and multifactorial phenomenon that qualifies the degree to which the patient conforms to medical prescriptions. This phenomenon has both detrimental clinical consequences for the patient (in terms of prognosis and quality of life), and negative economic consequences for the society. One difficulty in the study of adherence is that it measurement is mainly indirect and tainted with many bias. Another difficulty is that adherence is not an end, but should be considered through the lens of patient outcomes. The ADHERENCE project, funded under the Health Data Hub BOAS program (Bibliothèque Ouverte d’Algorithmes en Santé) aims at proposing clearly documented measurements for the level of patient adherence and of its potential clinical outcomes in the context of three chronic diseases (cancer, hypertension and transplantation). One challenge here is to enable these measurements in both EHRs from hopsitals and claims from the national insurance database (SNDS).

4.1.6 iDEMO Meditwin - Dassault Systems - "Virtual twin for personalised medicine "(started in 2024)

Meditwin is a collaborative project funded by BPI ("Banque Publique d'Investissement") with Dassault Systems (leader of the projects), Inria, IHU institutes across France and Medtech startups. The aim of the project is to provide a digital platform relying on virtual twins of individuals who faithfully reproduce their state of health and which make it possible to test different therapeutic options. It will promote interdisciplinarity by facilitating interoperability of multimodal medical data. Our team will use AI approaches to propose Clinical Decision Support Systems (CDSS) in cardiovascular diseases and cancer. We will also develop the clinical trial methodology evaluation these CDSS. In particular, HeKA will develop stratification and classification algorithms, synthetics patient's generators, statistical and mathematical models for multi-modal and multidimensional health data and clinical evaluation methods for the resulted CDSS as Digital Medical Devices.

4.1.7 RHU ReBone - “Surgery planning for multiple fractures"

The RHU ReBone is a French consortium led by the orthopedic surgery unit of the Nice hospital. It is funded by the ANR from 2024 to 2029, and aims at producing robust anatomical software to automate the planning of complex fracture reductions. Jean Feydy and Stéphanie Allassonnière work on the image pre-processing and analysis, in close collaboration with Hervé Delingette from the Epione team at Inria Sophia.

4.2.1 SIRIC InsiTu - “Insights into cancer: from inflammation to Tumor"

To turn scientific knowledge into sustainable healthcare, cancer research must identify who is at risk of cancer, when and in whom a new cancer arises, and how best to treat it and gauge response. Aligned with Europe's Beating Cancer Plan, InsiTu takes on the three challenges of cancer prevention, interception, and treatment in digestive, lung, skin cancer, and heme malignancies. Chronic inflammation is a key cancer niche fostering tumor initiation. Leveraging a transformative Tissue-Hub interfacing diagnostics and research, our program ‘From inflammation to clonal emergence and cancer’ will unite experts in chronic diseases damage to monitor patients with chronic tissue inflammation and cancer predisposition, mirrored by animal modelling, to understand the critical transition from chronic tissue damage to cancer progression, opening opportunities for prevention and interception. Such longitudinal (and sometimes invasive) interactions between patients and healthcare practitioners can be improved by empowering patients, taking psychic, social and ethical dimensions in consideration. Our program ‘Imaging cancer and its environment’ will take a different approach to this challenge. Through synergetic interactions with mathematicians and physicists, it will provide novel frameworks for multiscale integration of molecular alterations, cellular processes, and tissue complexity. This effort will result in image- based, non-invasive ‘virtual biopsies’ as proxies of key biological processes underlying tumor heterogeneity and drug resistance. Along with novel biomarkers such as circulating extracellular vesicles, these virtual biopsies will gauge responses to new therapeutic approaches developed in our third program ‘From new targets to new trials’. There, experts in leukemias and skin cancers will use cutting-edge in vivo functional screens and multi-omic interrogation of Tissue-Hub samples to identify new targetable vulnerabilities and develop next-generation cell-based immunotherapies. To fasten the transfer of these innovations into care, new adaptive clinical trial designs will be engineered.

4.2.2 Combo - Sanofi - "Evaluating drug combinations in oncology with Real-World Data and state-of-the-art knowledge"

Combo is a collaborative project with Industry, national health data platforms and cancer institute: Sanofi Pharma, The Health Data Hub, Centre Léon Berard and Inria-Inserm-HeKA. The objective of the project is to identify promising families of drug combinations in oncology using multisource and multi-modal data modelling and prediction, including RWD (cancer patients’ care data from CLCC cancer centre), genomic public databases, literature, clinical trials depository and expert’s opinion. Once these combinations will be identified mechanistic models will be used to determine dose-regimen and build dose-finding trial designs for the combinations to be evaluated through formal clinical trials. In this project we lead the following WPs (1) AI based analysis of the multimodal RWD and subgroup discovery for the identification of relevant combinations, (2) Bayesian multi-modal analysis accounting RWD modelling as well as expert’s opinion and literature and public clinical platforms AI analysis from Sanofi, and (3) proposing candidates and designs for future phase I designs associated with dose, regimen and associated molecule for the combination on selected family of combinations using preclinical PK/PD model.

4.2.3 RHU OPERANDI - “Optimisation and imProved Efficacy of targeted RAdioNuclide therapy in Digestive cancers by Imagomics"

Advanced stage hepatocellular carcinoma (HCC) and gastroenteropancreatic neuroendocrine tumours (GEP-NET) are currently treated with targeted radionuclide therapy (TRT), a highly advanced method that consists of either intra- arterial injections of radioactive microspheres (transarterial radioembolisation - TARE) or targeted peptides radioactively labelled and administered systemically (Peptide Receptor Radionuclide Therapy - PRRT). While highly effective, patient stratification and early identification of responders are currently managed insufficiently due to the lack of pertinent imaging biomarkers, either non-invasive or invasive. Furthermore, therapy-induced DNA damage leads to tumour resistance, reducing TRT efficacy. We aim to overcome those current limits through the OPERANDI project via innovative approaches in engineering, novel imaging biomarkers, and new concepts for DNA repair mechanisms, combined with a fundamental understanding of causal links. Our ambitions go beyond the current state-of-art, embracing even new combinations of drugs and $\alpha$-emitters to enhance dose localization and efficiency. Methodology will try to understand fundamentally whether current patient management using CT/PET/MRI allows to predict response and survival using cutting edge imaging-based artificial intelligence (AI) approaches in combination with data augmentation techniques to reach statistical significance.

4.3.1 EU INVENTS Horizon project - “Innovative designs, extrapolation, simulation methods and evidence-tools for rare diseases addressing regulatory needs" (started in 2024)

The evaluation of new medicines for rare diseases (RD) including rare paediatric RDs is challenging for several reasons, among which are the small patient sample sizes, heterogeneity of patients and diseases and heterogeneity in disease knowledge. Due to these difficulties, access to effective treatments and the number of treatment options are often limited in RDs. INVENTS aims to provide clinical trial trialists, researchers and regulators with a global framework encompassing methods, workflows and evidence assessment tools to be implemented in orphan and paediatric drug development. Our ambition is to significantly improve the evaluation of evidence and regulatory decision-making through the development and validation of: refined longitudinal model-based diseases trajectories and treatment effect, improved extrapolation models, in silico trials (e.g., virtual patient cohorts), optimised model-based clinical trial designs and evidence synthesis methods. These will be evaluated through simulation studies and tested on extensive data from a range of use cases provided by our industrial partners Roche and Novartis and Real World data (RWD) from RD registry. The INVENTS framework will improve consistency and efficiency of the drug evaluation process for RD by augmenting clinical evidence without compromising its scientific integrity and providing regulators assessment credibility criteria. At the end of this 5 years project, the European industry will be able to exploit novel and improved clinical trial designs, in silico trials and RWD analysis approaches supporting drug development in RD. The European Medicine Agency and European national regulators (including Health Technology Assessment bodies) will be supplied with a general framework allowing better informed decision making. Most importantly, RD patients will benefit from an increased and faster access to efficacious and safe treatments.

4.3.2 EU MSCA Doctoral Networks Orgestra project (started 2024)

Organoids experimental models are in vitro 3D cell cultures which can be generated from embryonic stem cells, induced pluripotent stem cells or adult stem cells, and can replicate organs functionally and structurally. Their physiological resemblance to target organs and ability to cryopreserve make organoids a powerful tool for biomedical research and advancing understanding of the mechanisms underlying certain disorders, including rare diseases. The ORGESTRA Joint Doctoral Network will propose innovative organoid technologies for two genetic disorders, i.e., cystic fibrosis and cystinosis. In this project we will supervise 2 PhDs project which will propose statistical development for; (1) linking in silico trials to organoids data and innovative trial design. These designs will incorporate biomarkers-based findings, as organoids, i.e., that reduce unnecessary exposure of patients (screening) or allow drugs to be screened more effectively for non-effectiveness before embarking on human trials. This will be done via a joint doctoral degree with University of Utrecht. (2) Estimand framework involving Bayesian principles on organoids data for clinical trial outcomes and models. The estimands framework will be based on expert’s elicitation to understand which questions are more relevant in term of clinical efficacy/toxicity, to select the proper outcomes, to identify the possible intercurrent events and to provide a robust statistical model whose parameters will be estimated under a Bayesian setting. This will be done via a joint doctoral degree with Katholieke Universiteit Leuven.

4.3.3 CIL LICO- Ciliopathies: group of disorders associated with genetic mutation leading to rare and severe genetic diseases - Projet RHU - Recherche Hospitalo-Universitaire- 2018-24

Ciliopathies are a large group of rare and severe genetic diseases caused by ciliary dysfunction associated with clinical and genetic heterogeneity, as well as a lack of knowledge on patients' natural history (i.e., the evolution of the disease). The aim of this RHU-3 project, funded through the Investissements d'avenir program, is to develop innovative, diagnostic, prognostic and tailored therapeutic approaches for patients suffering from ciliopathies to prevent them from developing renal failure. Following this aim, we will develop a mechanistic stratification of ciliopathies, in order to regroup suspected and already diagnosed ciliopathies in a treatment-orientated classification. One goal is to engineer a ready-to-use bio-kit for assessing both diagnosis and prognosis of developing renal alteration, in ciliopathy patients. Finally, personalized therapeutic approaches will be proposed for patients using predictive approaches. We will use methods developed in Axis 1 as well as methods proposed in Axis 2 to model patient trajectory. This project is in collaboration with Imagine Institute, AP-HP, the Medetia company and Ecole Polytechnique.

4.3.4 BNDMR- Banque Nationale des Maladies Rares

The French National Registry for Rare Diseases (BNDMR) is a national tool for epidemiology and public health purposes in the field of rare diseases. In line with the objectives defined by the 2nd and 3rd French National Plan for Rare Diseases, the BNDMR team develops a secure national information system which gathers anonymized clinical data of patients affected by rare diseases in its BNDMR data warehouse. As medical head of the BNDMR, AS Jannot has several research projects strongly connected with HeKA team including CDE.AI and Dromos project. CDE.ai aims to create a set of natural language processing algorithms that will allow the semi-automatic completion of the rare disease minimal data set that is currently completed manually for all patients followed up in the rare disease expert centres. In this project, we will use methods developped in Axe 1 (collaboration with N Garcelon). The DROMOS project is a project that uses the National Data Bank for Rare Diseases by linking it to health insurance data. This matching will allow the description of the care of rare disease patients at the national level for rare diseases, including the characteristic care of the most frequent rare diseases. We will use methods developped in the from of Axis 2 to model these longitudinal data.

4.4.1 Antibiotic resistance – FAIR project EU Horizon (on going)

The aim of the FAIR project is to evaluate Flagelin aerosol therapy for stimulation of immunity as an alternative treatment against pneumonia with multidrug resistant bacteria. In this project, we are developing a full model using pharmacometrics expertise as well as statistical designs for extrapolation purpose and the design of dose-finding study in healthy volunteers. As written above, in this project, S Zoharvco-lead along with C Kloft (Freie Universitaet Berlin) the WP entitled “Development of a translational modelling and simulation platform for flagellin PK/PD”. The aim of the WP is to propose an optimal design for the first-in-man clinical trials, maximizing knowledge gained from in vitro experimentation, expert knowledge and pre-clinical experiments along the way. By incorporating mechanistic approaches earlier in the development process along with a continuous learning modelling under Bayesian inference, we hope to increase the probability of success of the translation process to the clinical setting and thus, optimizing the statistical design and sample size. This project is in relation with axis 3.

4.4.2 Virtual reality (Ongoing)

Several projects led at the HEGP are currently ongoing to evaluate the analgesia provided by the use of Virtual Reality in different care settings (extracorporeal lithotripsy, after colorectal cancer surgery, and fiberoptic bronchoscopy in critical care). In these projects, not so close but still related to axis-2, we will provide methodological approach and use statistical methods to conclude on the clinical questions, by working closely with all Coordinating Investigators (Prof. D Clausse, G Manceau and A Rastello)

7.1.1 medkit

• Name:
  a toolkit for a learning health system
• Keywords:
  Learning health system, Biomedical data, Decision support, Python, Information extraction, Natural language processing, Audio signal processing, Machine learning
• Scientific Description:
  medkit aims to facilitate information and knowledge extraction from data of various modalities by providing software modules for data preparation or analysis and facilitating their chaining. The development of new modules is motivated either by needs within the core of the library or by application projects. The initial projects of the team that motivated developments were related to knowledge extraction from healthcare data warehouses, particularly their textual content.
• Functional Description:
  This library aims at (1) facilitating the manipulation of healthcare data of various modalities (e.g., structured, text, audio data) for the extraction of relevant features and (2) developing supervized models from these various modalities for decision support in healthcare.
• Release Contributions:

  ## Fixed

  - Use ISO 8601 timestamp for model checkpoint paths - Fix test of iamsystem matcher on Python 3.12

• URL:
  https://­github.­com/­medkit-lib/­medkit
• Contact:
  Adrien Coulet
• Participants:
  Deycy Camila Arias Villamil, Olivier Birot, Kim-Tam Huynh, Antoine Neuraz, Ivan Lerner, Bastien Rance, Adrien Coulet, Ghislain Vaillant

7.1.2 Pythae

• Keywords:
  Generative Models, Benchmarking, Reproducibility
• Functional Description:
  This library implements some of the most common (Variational) Autoencoder models under a unified implementation. In particular, it provides the possibility to perform benchmark experiments and comparisons by training the models with the same autoencoding neural network architecture. The feature make your own autoencoder allows you to train any of these models with your own data and own Encoder and Decoder neural networks. It integrates experiment monitoring tools such wandb, mlflow or comet-ml and allows model sharing and loading from the HuggingFace Hub in a few lines of code.
• URL:
  https://­github.­com/­clementchadebec/­benchmark_VAE
• Contact:
  Clément Chadebec

7.1.3 Pyraug

• Keywords:
  Generative Models, Data augmentation
• Functional Description:
  This library provides a way to perform Data Augmentation using Variational Autoencoders in a reliable way even in challenging contexts such as high dimensional and low sample size data.
• URL:
  https://­github.­com/­clementchadebec/­pyraug
• Contact:
  Clément Chadebec

7.1.4 MultiVae

• Keywords:
  Multimodality, Variational Autoencoder
• Functional Description:
  This library gathers some of the most common multi-modal Variational AutoEncoder (VAE) implementation in PyTorch as well as benchmarking tools (datasets, metrics...).
• Contact:
  Agathe Senellart

7.2.1 ChemoKG

We published ChemoKG-open, a knowledge graph in RDF (Resource Description Framework) of chemotherapy protocols. It encompasses 513 protocols, which provides the necessary classes and relations to represent the various dimensions of protocols. The protocols themselves have been extracted from a local database of the Pharmacy Service of the European Georges Pompidou Hospital of the AP-HP, Paris. A description of ChemoKG-open and its implementation is available in 64. We implemented this knowledge graph by reusing as much as possible elsewhere defined entites and by respecting the FAIR principles of Open Science.

10.1.1 Horizon Europe

INVENTS - Sarah Zohar is the PI of this EU Horizon project. Please see section 4 for further details.

10.1.2 H2020 projects

FAIR - Sarah Zohar is a WP leader in this H2020 project. Please see section 4 for further details.

10.1.3 Other european programs/initiatives

The European taskforce lead by EIT Health and French Ministry of Solidarity and Health, for the “harmonization of clinical studies criteria andmethodologies in Europe for the evaluation of digital medical devices”. In this taskforce, Sarah Zohar co-lead the WP2 on “Evidence in clinical evaluation” with Corinne Collignon (Head of the Digital Mission at HAS, France) and Barbara Höfgen (Head of the Unit DiGA-Fast- Track at Bfarm, Germany)

EUMSCA Doctoral Networks - Project ORGESTRA "Organoid technologies for disease modeling, drug discovery and development for rare diseases". Sarah Zohar is the WP leader of methodological design for using organoids outcomes and transfer it to clinic.

11.1.1 Scientific events: organisation and selection

• Stéphanie Allassonière served as a member of the SAB of the AI for Health Summit
• Adrien Coulet co-organized PFIA journée IA & Santé (1 day, $>$70 participants), which brings together researchers interested in healthcare applications of knowledge representation and machine learning.

Members of the group are involved in the program committees of several events such as ML4H, MedInfo, SWAT4HCLS, the OHDSI Symposium.

11.1.2 Journal

11.1.3 Invited talks

HeKA memebers are regularly invited to present their research to both scientific and general public.

11.1.4 Leadership within the scientific community

Antia Burgun serves as the Representative of the French medical informatics community at the IMIA (International Medical Informatics Association). She also serves as a member of the Executive Board of the Imagine Institute.

Stéphanie Allassonière is the vice-president of research economical transfer and industrial partnerships, Université Paris Cité.

11.1.5 Scientific expertise

Sarah Zohar is a voting member at the Cnedimts (“Commission nationale d’évaluation des dispositifs médicaux et des technologies de santé”) at HAS (“Haute Autorité de Santé”). Sarah Zohar was in the review committee of BpiFrance grant evaluation "évaluation DM à base d’IA et de numérique". Members of the HeKA team are involved in several ethic committees, such as the Comité d’Ethique de la Recherche APHP.Centre, the EDS AP-HP Comité Scientifique et Ethique. Members of HeKA the scientific board of the ANR generic call Axe H.14 : Interfaces : mathématiques, sciences du numérique – biologie, santé and of the FC3R (French Center for the 3R).

11.1.6 Research administration

Sandrine Katsahian is responsible for the PRIME department dedicated to the Research, Innovation, Digital Medicine located in the HEGP in APHP.Centre.

David Drummond serves as Secretary of the mHealth/eHealth group at the European Respiratory Society.

International journals

• 7 articleI.Ioana Agache, I.Ian Adcock, C.Cezmi Akdis, M.Mübeccel Akdis, G.Guillermo Bentabol-Ramos, M.Maarten van den Berge, C.Cristina Boccabella, W.Walter Canonica, C.Cristiano Caruso, M.Mariana Couto, I.Ignacio Davila, D.David Drummond, J.José Fonseca, A.Alina Gherasim, S.Stefano del Giacco, D.David Jackson, M.Marek Jutel, A.Amelia Licari, S.Stelios Loukides, A.Andreia Moreira, M.Manali Mukherjee, I.Iñigo Ojanguren, O.Oscar Palomares, A.Alberto Papi, L.Luis Perez de Llano, O.Oliver Price, M.M. Rukhazde, M.Mohamed Shamji, D.Dominick Shaw, S.Silvia Sanchez-Garcia, A.Almudena Testera-Montes, M. J.Maria José Torres and I.Ibon Eguiluz-Gracia. The Bronchodilator and Anti‐Inflammatory Effect of Long‐Acting Muscarinic Antagonists in Asthma: An EAACI Position Paper.AllergyDecember 2024HALDOI
• 8 articleF.Francois Ansart, C.Charles de Ponthaud, C.Camille Buffet, F.Fabrice Menegaux, S.Sébastien Gaujoux and N.Nathalie Chereau. One- or Two-Step Total Thyroidectomy for Cancer Indications: A 20-Year Retrospective Study from a Referral Center.Annals of Surgical OncologyDecember 2024HALDOI
• 9 articleO.Olivier Baud, M.Moreno Ursino, L.Loic Sentilhes, M.Muriel Doret-Dion, C.Corinne Alberti, C.Camille Aupiais and T.Thomas Schmitz. Survival without severe neonatal morbidity after antenatal betamethasone dose reduction: a post hoc analysis of a randomized non-inferiority trial.American Journal of Obstetrics and Gynecology2314February 2024, 458e1-458e16HALDOI
• 10 articleC.Camille Bignier, L.Lucile Havet, M.Margot Brisoux, C.Céline Omeiche, S.Swati Misra, A.Apolline Gonsard and D.David Drummond. Climate change and children’s respiratory health.Paediatric Respiratory ReviewsJuly 2024HALDOI
• 11 articleE. A.Eduard Antonie van Bodegraven, S.Sanne Lof, L.Leia Jones, B.Béatrice Aussilhou, G.Gao Yong, W.Wei Jishu, R.Rosa Klotz, D. M.Dario Missael Rocha-Castellanos, I.Ippei Matsumato, C.Charles de Ponthaud, K.Kimitaka Tanaka, E.Esther Biesel, E.Emmanuele Kauffmann, Y.Yuichi Nagakawa, T.Traian Dumitrascu, P.Pablo Martí-Cruchaga, G.Geert Roeyen, A.Alessandro Zerbi, M.Mara Goetz, V.Vincent de Meijer, P.Patrick Pessaux, P.Povilas Ignatavicius, I. E.Ihsan Ekin Demir, M.Mario Giuffrida, B.Bobby Tingstedt, M. V.Marco Vito Marino, S.Sotiris Mastoridis, M.Maximilian Brunner, I.Isabel Mora-Oliver, C.Cecilia Bortolato, A.Aisté Gulla, T.Thomas Apers, H.Hélène Hermand, Y.Yusuke Mitsuka, I.Irinel Popescu, U.Ugo Boggi, U.Uwe Wittel, S.Satoshi Hirano, S.Sébastien Gaujoux, K.Keiko Kamei, C.Carlos Fernández-del Castillo, T.Thilo Hackert, J.Jiang Kuirong, M.Miao Yi, A.Alain Sauvanet, M.Marc Besselink, M.Mohammad Abu Hilal and S.Safi Dokmak. Tailoring the Use of Central Pancreatectomy Through Prediction Models for Major Morbidity and Postoperative Diabetes.Annals of Surgery2806December 2024, 993-998HALDOI
• 12 articleS.Sandrine Boulet, E.Emmanuelle Comets, A.Antoine Guillon, L. B.Linda B.S. Aulin, R.Robin Michelet, C.Charlotte Kloft, S.Sarah Zohar and M.Moreno Ursino. Straightforward Phase I Dose-Finding Design for Healthy Volunteers Accounting for Surrogate Activity Biomarkers.Statistics in Biopharmaceutical ResearchDecember 2024, 1-10HALDOI
• 13 articleS.Sandrine Boulet, M.Moreno Ursino, R.Robin Michelet, L.Linda Bs Aulin, C.Charlotte Kloft, E.Emmanuelle Comets and S.Sarah Zohar. Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies.Statistical Methods in Medical Research2024HALDOI
• 14 articleM.Maciej Buze, J.Jean Feydy, S. M.Steven M. Roper, K.Karo Sedighiani and D. P.David P. Bourne. Anisotropic power diagrams for polycrystal modelling: efficient generation of curved grains via optimal transport.Computational Materials Science245May 2024, 113317HALDOI
• 15 articleA.Alexandre Challine, M.Mehdi Kirouani, S.Sheraz Markar, S.Stylianos Tzedakis, R.Romain Jaquet, G.Guillaume Piessen, T. S.Tienhan Sandrine Dabakoyo-Yonli, J.Jérémie Lefèvre, A.Andrea Lazzati and T.Thibault Voron. MIRO study: Do the results of a randomized controlled trial apply in a real population?Surgery1754April 2024, 1055-1062HALDOI
• 16 articleX.Xiaoyi Chen, J.Junyuan Wang, C.Carole Faviez, X.Xiaomeng Wang, M.Marc Vincent, R.Rosy Tsopra, A.Anita Burgun and N.Nicolas Garcelon. An Integrated Pipeline for Phenotypic Characterization, Clustering and Visualization of Patient Cohorts in a Rare Disease-Oriented Clinical Data Warehouse.Studies in Health Technology and Informatics316August 2024, 1785-1789HALDOI
• 17 articleA.Aziliz Cottin, M.Marine Zulian, N.Nicolas Pécuchet, A.Agathe Guilloux and S.Sandrine Katsahian. MS-CPFI: A model-agnostic Counterfactual Perturbation Feature Importance algorithm for interpreting black-box Multi-State models.Artificial Intelligence in Medicine1471January 2024, 102741HALDOI
• 18 articleJ.Julie Deyrat, A.Alexandre Challine, T.Thibault Voron, L.Lauren O'Connell, M.Maxime Collard, S.Stylianos Tzedakis, R.Romain Jaquet, A.Andrea Lazzati, Y.Yann Parc and J.Jeremie Lefèvre. What is the rate of definitive stoma after subtotal colectomy for inflammatory bowel disease? A nationwide study of 1860 patients.Colorectal Disease266May 2024, 1203-1213HALDOI
• 19 articleJ.Julie Deyrat, D.David Fuks, J.Juliette Murris, E.Emilie Lanoy, A.Alexandra Nassar, A.Alix Dhote, U.Ugo Marchese, V.Vincent Mallet, S.Sandrine Katsahian, M.Martin Gaillard and S.Stylianos Tzedakis. Evolution of minimally invasive liver surgery in France over the last decade.Surgical Endoscopy388June 2024, 4457-4467HALDOI
• 20 articleD.David Drummond, I.Ireti Adejumo, K.Kjeld Hansen, V.Vitalii Poberezhets, G.Gregory Slabaugh and C. Y.Chi Yan Hui. Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges.Breathe203December 2024, 230189HALDOI
• 21 articleD.David Drummond and A.Apolline Gonsard. Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.Journal of Medical Internet Research26November 2024, e58504HALDOI
• 22 articleD.David Drummond, F.Fabienne Marquant, E.Elisa Zanelli, C.Cécile Lozach, N.Nathalie Boddaert, R. A.Rola Abou Taam, B.Bénédicte Neven, M.Muriel Le Bourgeois, A.Alice Hadchouel, S.Sabine Sarnacki, C.Caroline Elie, C.Christophe Delacourt and L.Laureline Berteloot. Radiation-free and injection-free imaging of the paediatric chest using a magnetic resonance imaging protocol including zero time echo sequence (3D-ZTE).European Journal of Pediatrics18310July 2024, 4297-4308HALDOIback to text
• 23 articleC.Constance Dubois, D.David Eigen, E.Emmanuel Delmas, M.Margot Einfalt, C.Clara Lemaçon, L.Laureline Berteloot, P.Patrick Bossuyt, D.David Drummond, P.Pauline Scherdel, F.François Simon, H.Héloïse Torchin, Y.Yasaman Vali, I.Isabelle Bloch and J.Jérémie Cohen. Deep learning in medical image analysis: introduction to underlying principles and reviewer guide using diagnostic case studies in paediatrics.BMJ - British Medical Journal387November 2024, e076703HALDOI
• 24 articleO.Olivier Espitia, A.Alizée Raimbeau, B.Benjamin Planquette, S.Sandrine Katsahian, O.Olivier Sanchez, B.Benjamin Espinasse, A.Antoine Bénichou and J.Juliette Murris. A systematic review and meta-analysis of the incidence of post-thrombotic syndrome, recurrent thromboembolism, and bleeding after upper extremity vein thrombosis.Journal of Vascular Surgery: Venous and Lymphatic Disorders121January 2024, 101688HALDOI
• 25 articleC.Carole Faviez, M.Marc Vincent, N.Nicolas Garcelon, O.Olivia Boyer, B.Bertrand Knebelmann, L.Laurence Heidet, S.Sophie Saunier, X.Xiaoyi Chen and A.Anita Burgun. Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.Orphanet Journal of Rare Diseases191February 2024, 55HALDOI
• 26 articleA.Apolline Gonsard, M.Martin Genet and D.David Drummond. Digital twins for chronic lung diseases.European Respiratory Review33174October 2024, 240159HALDOI
• 27 articleA.Apolline Gonsard, E.Evgeni Mekov, S.Sarah Barron, G.Giorgio Castellana, E.Elene Khurtsidze, A.Angelos Vontetsianos, F.Francesca Pennati, P.Pradeesh Sivapalan, L.Lorna Latimer, M.Mathieu Marillier, C.-Y.Chi-Yan Hui, G.Georgios Kaltsakas, S.Shailesh Kolekar, G.Guido Vagheggini, C.Cláudia Vicente, D.David Drummond, V.Vitalii Poberezhets, S.Sam Bayat, F. M.Frits M.E. Franssen, I.Ioannis Vogiatzis and T.Thomas Gille. ERS International Congress 2023: highlights from the Respiratory Clinical Care and Physiology Assembly.ERJ Open Research103May 2024, 00178-2024HALDOI
• 28 articleS.Seydou Goro, A.Alexandre Challine, J. H.Jérémie H Lefèvre, S.Salomé Epaud and A.Andrea Lazzati. Impact of interhospital competition on mortality of patients operated on for colorectal cancer faced to hospital volume and rurality: A cross-sectional study.PLoS ONE191January 2024, e0291672HALDOI
• 29 articleA.Anna Gueiderikh, J.Jérémy Baude, D.David Baron, R.Renaud Schiappa, S.Sandrine Katsahian, D.Damien Moreau, M.Marc Laurans, J.-E.Jean-Emmanuel Bibault, S.Sarah Kreps, P.-Y.Pierre-Yves Bondiau, M.Magali Quivrin, A.Alexis Lépinoy, D.David Pasquier, J.-M.Jean-Michel Hannoun-Levi and P.Philippe Giraud. Nodal radiotherapy for prostate adenocarcinoma recurrence: predictive factors for efficacy.Frontiers in Oncology1410October 2024, e2441874HALDOI
• 30 articleA.Armelle Guénégou-Arnoux, J.Juliette Murris, S.Stephane Béchet, C.Camille Jung, J.Johann Auchabie, J.Julien Dupeyrat, N.Nadia Anguel, P.Pierre Asfar, J.Julio Badie, D.Dorothée Carpentier, B.Benjamin Chousterman, J.Jeremy Bourenne, A.Agathe Delbove, J.Jérôme Devaquet, N.Nicolas Deye, G.Guillaume Dumas, A.-F.Anne-Florence Dureau, J.-B.Jean-Baptiste Lascarrou, S.Stephane Legriel, C.Christophe Guitton, C.Caroline Jannière-Nartey, J.-P.Jean-Pierre Quenot, J.-C.Jean-Claude Lacherade, J.Julien Maizel, A.Armand Mekontso Dessap, B.Bruno Mourvillier, P.Philippe Petua, G.Gaetan Plantefeve, J.-C.Jean-Christophe Richard, A.Alexandre Robert, C.Clément Saccheri, L. v.Ly van Phach Vong, S.Sandrine Katsahian and F.Frédérique Schortgen. Protocol for fever control using external cooling in mechanically ventilated patients with septic shock: SEPSISCOOL II randomised controlled trial.BMJ Open141January 2024, e069430HALDOI
• 31 articleR.Romain Jaquet, A.Alexandre Challine, S.Stylianos Tzedakis, C.Charles de Ponthaud, S.Sandrine Katsahian, J.Jérémie Lefevre and A.Andrea Lazzati. Impact of the thresholds of surgical volume on mortality in oncological digestive surgery: A French National Cohort.EJSO - European Journal of Surgical Oncology5010October 2024, 108581HALDOI
• 32 articleE.Enora Laas, E.Elise Dumas, A.-S.Anne-Sophie Hamy, T.Thomas Gaillard, P.Paul Gougis, F.Fabien Reyal, F.François Husson and A.-S.Anne-Sophie Jannot. The Influence of treatment intervals on prognosis in young breast cancer patients: Insights from the French national cohort.EJSO - European Journal of Surgical Oncology5112024, 109373HALDOI
• 33 articleE.Enora Le Roux, M.Moreno Ursino, I.Ivana Milovanovic, P.Paul Picq, J.Jeremie Haignere, G.Gilles Rault, D.Dominique Pougheon Bertrand and C.Corinne Alberti. Home-Based Connected Devices Combined With Statistical Process Control for the Early Detection of Respiratory Exacerbations by Patients With Cystic Fibrosis: Pilot Interventional Study With a Pre-Post Design.JMIR Formative Research8October 2024, e51753HALDOI
• 34 articleU.Ugo Marchese, J.-F.Jean-François Desbiens, X.Xavier Lenne, G.Gaanan Naveendran, S.Stylianos Tzedakis, M.Martin Gaillard, A.Amelie Bruandet, D.Didier Theis, L.Laurent Boyer, S.Stéphanie Truant, D.David Fuks and M.Mehdi El Amrani. Study of Risk Factors for Readmission After Pancreatectomy for Cancer.Annals of Surgery2793March 2024, 486-492HALDOI
• 35 articleO.Omar Masrour, J.Johan Personnic, F.Flore Amat, R.Rola Abou Taam, B.Blandine Prevost, G.Guillaume Lezmi, A.Apolline Gonsard, N.Nadia Nathan, A.Alexandra Pirojoc, C.Christophe Delacourt, S.Stéphanie Wanin and D.David Drummond. Objectives for algorithmic decision-making systems in childhood asthma: Perspectives of children, parents, and physicians.Digital Health10February 2024HALDOI
• 36 articleP.Philippe Menasché, N. K.Nisa K Renault, A.Albert Hagège, T.Tania Puscas, V.Valérie Bellamy, C.Camille Humbert, L.Laetitia Le, H.Hélène Blons, C.Clémence Granier, N.Nadine Benhamouda, A.Anne Bacher, G.Guillaume Churlaud, B.Brigitte Sabatier and J.Jérôme Larghero. First-in-man use of a cardiovascular cell-derived secretome in heart failure. Case report.EBioMedicine103May 2024, 105145HALDOI
• 37 articleL.Lillian Muyama, A.Antoine Neuraz and A.Adrien Coulet. Deep reinforcement learning for personalized diagnostic decision pathways using electronic health records: A comparative study on anemia and systemic lupus erythematosus.Artificial Intelligence in Medicine157October 2024, 102994HALDOI
• 38 articleL.Lillian Muyama, A.Antoine Neuraz and A.Adrien Coulet. Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review.Journal of Biomedical InformaticsNovember 2024, 104746HALDOI
• 39 articleA.Alexandra Nassar, S.Stylianos Tzedakis, U.Ugo Marchese, G.Gaanan Naveendran, R.Remy Sindayigaya, M.Martin Gaillard, F.François Cauchy, M.Mickael Lesurtel, B.Brice Gayet, O.Olivier Soubrane and D.David Fuks. Factors of oncological failure in two stage hepatectomy for colorectal liver metastases.EJSO - European Journal of Surgical Oncology501January 2024, 107267HALDOI
• 40 articleA.Antoine Neuraz, G.Ghislain Vaillant, C.Camila Arias, O.Olivier Birot, K.-T.Kim-Tam Huynh, T.Thibaut Fabacher, A.Alice Rogier, N.Nicolas Garcelon, I.Ivan Lerner, B.Bastien Rance and A.Adrien Coulet. Facilitating phenotyping from clinical texts: the medkit library.BioinformaticsNovember 2024, btae681HALDOI
• 41 articleV. T.Van Tuan Nguyen, A.Adeline Fermanian, A.Antoine Barbieri, S.Sarah Zohar, A.-S.Anne-Sophie Jannot, S.Simon Bussy and A.Agathe Guilloux. An efficient joint model for high dimensional longitudinal and survival data via generic association features.Biometrics804December 2024HALDOI
• 42 articleM.Maxime Pattou, D.David Fuks, T.Theophile Guilbaud, B.Bastien Le Floch, O.Oceane Lelièvre, E.Ecoline Tribillon, H.Heithem Jeddou, U.Ugo Marchese, D. J.David Jeremie Birnbaum, O.Olivier Soubrane, L.Laurent Sulpice and S.Stylianos Tzedakis. Predictive value of C-reactive protein for postoperative liver-specific surgical site infections.Surgery1755May 2024, 1337-1345HALDOI
• 43 articleC.Clément Poirault, A.Alice Hadchouel, C.Charlotte Roy, S.Sophie Schmartz, A.Apolline Gonsard, N.Nicolas Garcelon, C.Christophe Delacourt and D.David Drummond. Inflammatory rebound and postinfectious inflammatory response in children with pleural infection: A single‐center retrospective study.Pediatric Pulmonology594January 2024, 974-981HALDOI
• 44 articleJ.Johanne Poisson, C.Carine El-Sissy, A.Arnaud Serret-Larmande, N.Nikaïa Smith, M.Morgane Lebraud, J.-L.Jean-Loup Augy, C.Catherine Conti, C.Cécile Gonnin, B.Benjamin Planquette, J.-B.Jean-Benoît Arlet, B.Bertrand Hermann, B.Bruno Charbit, J.Jean Pastre, F.Floriane Devaux, C.Cyrielle Ladavière, L.Lydie Lim, P.Pauline Ober, J.Johanna Cannovas, L.Lucie Biard, M.-C.Marie-Christelle Gulczynski, N.Noémie Blumenthal, H.Hélène Péré, C.Camille Knosp, A.Alain Gey, N.Nadine Benhamouda, J.Juliette Murris, D.David Veyer, E.Eric Tartour, J.-L.Jean-Luc Diehl, D.Darragh Duffy, E.Elena Paillaud and C.Clémence Granier. Increased levels of GM-CSF and CXCL10 and low CD8+ memory stem T Cell count are markers of immunosenescence and severe COVID-19 in older people.Immunity and Ageing211May 2024, 28HALDOI
• 45 articleC.Charles de Ponthaud, S.Solafah Abdalla, M.-P.Marie-Pierre Belot, X.Xiaojian Shao, C.Christophe Penna, A.Antoine Brouquet and P.Pierre Bougnères. Correction: Increased CpG methylation at the CDH1 locus in inflamed ileal mucosa of patients with Crohn disease.Clinical Epigenetics161March 2024, 43HALDOI
• 46 articleC.Charles de Ponthaud, S.Solafah Abdalla, M.-P.Marie-Pierre Belot, X.Xiaojian Shao, C.Christophe Penna, A.Antoine Brouquet and P.Pierre Bougnères. Increased CpG methylation at the CDH1 locus in inflamed ileal mucosa of patients with Crohn disease.Clinical Epigenetics161February 2024, 28HALDOI
• 47 articleC.Charles de Ponthaud, S.Soumaya Bekada, C.Camille Buffet, M.Malanie Roy, A.Anne Bachelot, A.Amine Ayed, F.Fabrice Menegaux and S.Sébastien Gaujoux. Which lymphadenectomy for adrenocortical carcinoma?Surgery1766December 2024, 1635-1644HALDOI
• 48 articleC.Charles de Ponthaud, B.Bahadir Bozkirli, G. E.Giacomo Emanuele Maria Rizzo, S.Stuart Robinson, F.Filipe Vilas-Boas, G.Gabriele Capurso, S.Sébastien Gaujoux and G.Giuseppe Vanella. Management of malignant Gastric Outlet Obstruction (mGOO) due to pancreatic cancer in the era of EUS-Gastrojejunostomy: an international practice survey and case vignette study by Pancreas 2000 from the European Pancreatic Club.Surgical Endoscopy386April 2024, 3231-3240HALDOI
• 49 articleC.Charles de Ponthaud, M.Malanie Roy and S.Sébastien Gaujoux. Adrenocortical carcinoma: what you at least should know.British Journal of Surgery1118August 2024HALDOI
• 50 articleC.Charles de Ponthaud, T.Thibault Voron and F.François Paye. Laparoscopic floppy Nissen fundoplication with valve calibration: a safe and efficient procedure.Surgery Today549April 2024, 1041-1050HALDOI
• 51 articleA.Alexis Rybak, N.Naïm Ouldali, E.Emmanuelle Varon, M.-K.Muhamed-Kheir Taha, S.Stéphane Bonacorsi, S.Stéphane Béchet, F.François Angoulvant, R.Robert Cohen and C.Corinne Levy. Vaccine-preventable Pediatric Acute Bacterial Meningitis in France: A Time Series Analysis of a 19-Year Prospective National Surveillance Network.Pediatric Infectious Disease Journal431January 2024, 74-83HALDOI
• 52 articleL.-M.Louis-Marie Sauvage, R.Rita Bentahila, Y.Yohan Tran, A.Armelle Guénégou-Arnoux, E.Emmanuelle Fabiano, A.-S.Anne-Sophie Bats, B.Bruno Borghese and C.Catherine Durdux. Radiation Proctitis in Patients With Locally Advanced Cervical Cancer Treated by Chemoradiation.American Journal of Clinical Oncology477July 2024, 311-316HALDOI
• 53 articleA.Anaïs Tribolet, M.Maxime Barat, D.David Fuks, M.Mathilde Aissaoui, P.Philippe Soyer, U.Ugo Marchese, M.Martin Gaillard, A.Alexandra Nassar, J.Jean Hardwigsen and S.Stylianos Tzedakis. Does portal vein anatomy influence intrahepatic distribution of metastases from colorectal cancer?Radiology and Oncology583September 2024, 376-385HALDOI
• 54 articleS.Stylianos Tzedakis, A.Alexandre Challine, S.Sandrine Katsahian, D.David Malka, R.Romain Jaquet, U.Ugo Marchese, M.Martin Gaillard, R.Romain Coriat, A.Alix Dhote, V.Vincent Mallet, H.Heithem Jeddou, K.Karim Boudjema, D.David Fuks and A.Andrea Lazzati. Clinical care pathways of patients with biliary tract cancer: A French nationwide longitudinal cohort study.European Journal of Cancer2024May 2024, 114018HALDOI
• 55 articleZ.Zaba Valtuille, V.Vincent Trebossen, N.Naim Ouldali, A.Aurélie Bourmaud, C.Coralie Gandré, C.Camille Aupiais, S.Sandrine Katsahian, R.Richard Delorme, H.Hugo Peyre and F.Florentia Kaguelidou. Pediatric Hospitalizations and Emergency Department Visits Related to Mental Health Conditions and Self-Harm.JAMA Network Open7102024HALDOI
• 56 articleA.Antoine Vanier, J.Judith Fernandez, S.Sophie Kelley, L.Lise Alter, P.Patrick Semenzato, C.Corinne Alberti, S.Sylvie Chevret, D.Dominique Costagliola, M.Michel Cucherat, B.Bruno Falissard, F.François Gueyffier, J.Jérôme Lambert, E.Etienne Lengliné, C.Clara Locher, F.Florian Naudet, R.Raphaël Porcher, R.Rodolphe Thiébaut, M.Muriel Vray, S.Sarah Zohar, P.Pierre Cochat and D.Dominique Le Guludec. Rapid access to innovative medicinal products while ensuring relevant health technology assessment. Position of the French National Authority for Health.BMJ Evidence-Based Medicine291February 2024, 1-5HALDOI
• 57 articleM.Marina Vignot, C.Camille Jung, S.Sarah Bathaei, A.Andrea Lazzati, V.Valérie Gateau, F.Frederica Angeli and C.Christian Delorenzo. Patient experience in bariatric surgery: protocol of a French narrative inquiry and qualitative analysis.BMJ Open148August 2024, e082528HALDOI
• 58 articleF.Filipe Vilas-Boas, G. E.Giacomo Emanuele Maria Rizzo, C.Charles de Ponthaud, S.Stuart Robinson, S.Sebastien Gaujoux, G.Gabriele Capurso, G.Giuseppe Vanella and B.Bahadır Bozkırlı. Unveiling hidden outcomes in malignant gastric outlet obstruction research – insights from a "Pancreas 2000" review.World Journal of Gastrointestinal Endoscopy168August 2024, 451-461HALDOI

International peer-reviewed conferences

• 59 inproceedingsL.Linus Bleistein, V.-T.Van-Tuan Nguyen, A.Adeline Fermanian and A.Agathe Guilloux. Dynamic Survival Analysis with Controlled Latent States.ICML 2024 - Forty-First International Conference on Machine LearningVienna, AustriaJuly 2024HAL
• 60 inproceedingsS.Sandrine Boulet, M.Moreno Ursino, R.Robin Michelet, L.Linda Bs Aulin, C.Charlotte Kloft, E.Emmanuelle Comets and S.Sarah Zohar. Bayesian framework for multi-source data integration - application to Human extrapolation from preclinical studies.PAGE 2024 - Annual Meeting of the Population Approach Group in EuropeRome, ItalyJune 2024HAL
• 61 inproceedingsE.Elisa Castagnari, L.Lillian Muyama and A.Adrien Coulet. Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia.To appearLLMs4MI 2024 @FLLM 2024 - First International Workshop on Large Language Models Applications in Medical Informatics In conjunction with the The Second International Conference on Foundation and Large Language ModelsDubai, United Arab Emirates2024HAL
• 62 inproceedingsA.Archibald Fraikin, A.Adrien Bennetot and S.Stéphanie Allassonnière. T-Rep: Representation Learning for Time Series using Time-Embeddings.T-Rep: Representation Learning for Time Series using Time-EmbeddingsICLR 2024 - Twelfth International Conference on Learning RepresentationsVienna & Online, AustriaMay 2024HAL
• 63 inproceedingsT.Thibault Hubert, G.Ghislain Vaillant, O.Olivier Birot, C.Camila Arias, A.Antoine Neuraz and A.Adrien Coulet. Comparing NER approaches on French clinical text, with easy-to-reuse pipelines.MIE 2024 - 34th Medical Informatics Europe ConferenceAthens, GreeceAugust 2024HAL
• 64 inproceedingsJ. H.Jong Ho Jhee, A.Alice Rogier, D.Dune Giraud, E.Emma Pinet, B.Brigitte Sabatier, B.Bastien Rance and A.Adrien Coulet. Representation and comparison of chemotherapy protocols with ChemoKG and graph embeddings.SWAT4HCLS 2024 - 15th International Semantic Web Applications and Tools for Health Care and Life Sciences ConferenceLeiden, Netherlands2024HALback to text
• 65 inproceedingsE.Emma Le Priol, M.Manissa Talmatkadi, S.Stéphane Schück, N.Nathalie Texier and A.Anita Burgun. Evaluation of BERT-Based Models on Patient Data from French Social Media.Studies in Health Technology and InformaticsMedical Informatics Europe 2024Volume 316: Digital Health and Informatics Innovations for Sustainable Health Care Systems316Athens, GreeceAugust 2024, 894HALDOI
• 66 inproceedingsM. A.Mina Abd Nikooie Pour, A.Alsayed Algergawy, E.Eva Blomqvist, P.Patrice Buche, J.Jiaoyan Chen, P. G.Pedro Giesteira Cotovio, A.Adrien Coulet, J.Julien Cufi, H.Hang Dong, D.Daniel Faria, L.Lucas Ferraz, S.Sven Hertling, Y.Yuan He, I.Ian Horrocks, L.Liliana Ibanescu, D. S.Dr Sarika Jain, E.Ernesto Jiménez-Ruiz, N.Naouel Karam, F.Felix Kraus, P.Patrick Lambrix, H.Huanyu Li, Y.Ying Li, P.Pierre Monnin, H.Heiko Paulheim, C.Catia Pesquita, A.Abhisek Sharma, P.Pavel Shvaiko, M.Marta Silva, G.Guilherme Sousa, C.Cassia Trojahn, J.Jana Vataščinová, B.Beyza Yaman, O.Ondrej Zamazal and L.Lu Zhou. Results of the Ontology Alignment Evaluation Initiative 2024.CEUR workshop proceedingsOM 2024 - 19th International Workshop on Ontology Matching (collocated with ISWC-2024)CEUR-3897Proceedings of the 19th International Workshop on Ontology Matching co-located with the 23rd International Semantic Web Conference (ISWC 2024)Baltimore, United States2024, 1-34HAL
• 67 inproceedingsT.Thibault Thubert, G.Ghislain Vaillant, O.Olivier Birot, C.Camila Arias, A.Antoine Neuraz, B.Bastien Rance and A.Adrien Coulet. Des pipelines faciles à réutiliser pour comparer les performances d'outils de reconnaissance d'entités nommées sur les textes cliniques en français.PFIA 2024 - Journée Santé et IA de la Plate-Forme Intelligence ArtificielleLa Rochelle, FranceJuly 2024HAL

Conferences without proceedings

• 68 inproceedingsJ.Juliette Murris, T.Tristan Amadei, T.Tristan Kirscher, A.Antoine Klein, A.-I.Anne-Isabelle Tropeano and S.Sandrine Katsahian. A novel methodological framework for the analysis of health trajectories and survival outcomes in heart failure patients.ICLR 2024 Workshop on Learning from Time Series For HealthVienna, AustriaarXivMarch 2024HAL

Doctoral dissertations and habilitation theses

• 69 thesisA.Aziliz Cottin. Development and interpretation of a deep learning algorithm for multi-state models: application to oncology.Université paris CitéMarch 2024HAL
• 70 thesisB.Benjamin Duputel. Bayesian seamless phase II/III designs for small sample sizes.Université paris CitéDecember 2024, 963 - 977HAL
• 71 thesisI.Ivan Lerner. On the design and learning of invariant structures in clinical time seriesmodels.Université Paris CiteDecember 2024HAL
• 72 thesisJ.Juliette Murris. Predicting recurrent events in a survival framework: development of a machine learning approach and an application in oncology.Université Paris CitéOctober 2024HAL
• 73 thesisv. T.van Tuan Nguyen. Integrating longitudinal data for enhanced survival analysis : methods and applications.Universite Paris CitéDecember 2024HAL
• 74 thesisA.Alice Rogier. Extraction and prediction of chemotherapy responses from electronic health records.Université paris CitéOctober 2024HAL
• 75 thesisM.Moreno Ursino. Enhancing Clinical Development - Bayesian Methods and Mathematical Modeling for Leveraging Available Information.Univerité Paris CitéSeptember 2024HAL

Reports & preprints

• 76 miscJ.-B.Jean-Baptiste Baitairian, B.Bernard Sebastien, R.Rana Jreich, S.Sandrine Katsahian and A.Agathe Guilloux. Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders.2024HALDOI
• 77 miscB.Blandine Derycke, E.Emma Le Priol, P.Pierre Foulquié, T.Tom Marty, A.Anaïs Gedik, S.Stéphane Schück, N.Nathalie Texier, A.Adel Mebarki and P.Paul Loussikian. The use of infodemiology in the development of targeted public health actions on endometriosis in France: a mixed & patient-centric methodology (Preprint).December 2024HALDOI
• 78 miscB.Benjamin Duputel, N.Nigel Stallard, F.François Montestruc, S.Sarah Zohar and M.Moreno Ursino. A Seamless hybrid Phase II/III design with Bayesian interim subgroup selection.September 2024HAL
• 79 miscE.Emma Le Priol, A.Anaïs Gedik, A.Adel Mebarki, S.Stéphane Schück, N.Nathalie Texier and A.Anita Burgun. Exploring rare disease realities: a systematic literature review on harnessing social media patient-generated data.December 2024HALDOI
• 80 miscJ.Juliette Murris, O.Olivier Bouaziz, M.Michal Jakubczak, S.Sandrine Katsahian and A.Audrey Lavenu. Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.June 2024HAL
• 81 miscS.Solange Pruilh and S.Stéphanie Allassonnière. Dynamic Expectation-Maximization algorithms for Mixed-type Data.May 2024HAL
• 82 miscM.Maxime Wack, A.Adrien Coulet, A.Anita Burgun and B.Bastien Rance. Enhancing Clinical Data Warehouses with Provenance and Large File Management: The gitOmmix Approach for Clinical Omics Data.September 2024HAL

Other scientific publications

• 83 inproceedingsL.Léa Comin, S.Solène Marie, M.Moreno Ursino, S.Sarah Zohar, N.Nicolas Tournier and E.Emmanuelle Comets. Modelling the whole-body pharmacokinetics (WBPK) of radiolabeled glyburide in healthy volunteers.PAGE meeting 2024 - Thirty-second meetingRome, ItalyJune 2024HAL
• 84 inproceedingsT.Tifenn Desroziers, Y.Yohann Soreze, V.Valérie NAU, B.Bruno Crestani, D.David Drummond, C.Clairelyne Dupin, R.Raphaël Borie, M.Martine Reynaud-Gaubert, K.Killian Hurley, F.Florence Dastot - Le Moal, S.Serge Amselem, M.Marie Legendre, C.Camille Louvrier and N.Nadia Nathan. Les anomalies de SFTPA1 et SFTPA2 associées aux pneumopathies interstitielles ne sont pas toujours des mutations faux sens..Congrès de Pneumologie de Langue FrançaiseMarseille, FranceJanuary 2024HAL
• 85 inproceedingsA.Axel Vuorinen, E.Emmanuelle Comets and M.Moreno Ursino. A comparative analysis of Phase I dose-finding designs incorporating pharmacokinetics information.PAGE 32 - Annual Meeting of the Population Approach Group in EuropeRome, ItalyJune 2024HAL

Patents

• 86 patentS.Stéphanie Allassonnière, R.Rémi Besson and E.Erwan Le Pennec. Real-time diagnosis aid method and decision-support for medical diagnosis to a user of a medical system.US11881309B2United StatesJanuary 2024HAL