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Ivan Lerner | email : ivan.lerner [at] inria.fr site internet : Google Scholar page bio:I followed a double curriculum in medicine & science at University of Paris driven by my curiosity for cognition and neuroscience. I did my master 2 in Cognitive neuroscience at ENS and several medical internships in neurology and psychiatry. I finally opted for a specialization in public health, because my interest evolved more towards the understanding and abstraction of learning principles, and their implementation in machines. Indeed, public health addresses medical questions at the population level, and I thought that machine learning would naturally be useful.During my public health residency, I conducted collaborative research projects in medical informatics (2 NLP publications: classifying clinical trials abstracts to be included in reviews, extracting clinical entities from medical records), biostatistics (oral presentation at ISCB: simulation study showing links between non-proportional hazards and composite endpoints in Cox regression) and epidemiology (under submission: prognosis value of troponin in population).To make sense of such diverse experiences, a unifying theoretical framework was needed, which I found in the theory of causality while attending a lecture by Peters.I have now started a part-time PhD on Machine Learning for Health (supervisors Francis Bach and Anita Burgun), where I am working on developing structure learning algorithms for electronic medical record data, which I believe is essential to achieve robust transfer learning. At the same time, I hold a position as a university hospital assistant at the Department of Medical Informatics of the Hôpital Européen Georges Pompidou, where I bring my expertise to local research projects, and I teach medical students at the University of Paris.
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Research projects
Publications
2023
Conference papers
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- Antoine Neuraz, Ivan Lerner, Olivier Birot, Camila Arias, Larry Han, et al.. TAXN: Translate Align Extract Normalize, a multilingual extraction tool for clinical texts. MedInfo 2023 – the 19th world congress on Medical and Health Informatics, International Medical Informatics Association (IMIA), Jul 2023, Syndney, Australia. ⟨hal-04069590⟩
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2022
Journal articles
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- Jean-Philippe Empana, Ivan Lerner, Marie-Cécile Perier, Catherine Guibout, Patricia Jabre, et al.. Ultrasensitive Troponin I and Incident Cardiovascular Disease. Arteriosclerosis, Thrombosis, and Vascular Biology, 2022, 42 (12), pp.1471-1481. ⟨10.1161/ATVBAHA.122.317961⟩. ⟨hal-04244993⟩
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- Jean-Philippe Empana, Ivan Lerner, Eugenie Valentin, Fredrik Folke, Bernd Böttiger, et al.. Incidence of Sudden Cardiac Death in the European Union. Journal of the American College of Cardiology, 2022, 79 (18), pp.1818-1827. ⟨10.1016/j.jacc.2022.02.041⟩. ⟨hal-04245023⟩
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- Ivan Lerner, Arnaud Serret-Larmande, Bastien Rance, Nicolas Garcelon, Anita Burgun, et al.. Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS). JMIR Medical Informatics, 2022, 10 (3), pp.e35190. ⟨10.2196/35190⟩. ⟨hal-03792405v2⟩
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- Alexandre Kalimouttou, Ivan Lerner, Chérifa Cheurfa, Anne-Sophie Jannot, Romain Pirracchio. Machine-learning-derived sepsis bundle of care. Intensive Care Medicine, In press, ⟨10.1007/s00134-022-06928-2⟩. ⟨hal-03911622⟩
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Book sections
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- Marc Vincent, Maxime Douillet, Ivan Lerner, Antoine Neuraz, Anita Burgun, et al.. Using Deep Learning to Improve Phenotyping from Clinical Reports. MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation, IOS Press, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220079⟩. ⟨hal-03880500⟩
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- Marc Vincent, Maxime Douillet, Ivan Lerner, Antoine Neuraz, Anita Burgun, et al.. Using Deep Learning to Improve Phenotyping from Clinical Reports. MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation, IOS Press, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220079⟩. ⟨hal-03887001⟩
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2021
Journal articles
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- Yannis Lombardi, Loris Azoyan, Piotr Szychowiak, Ali Bellamine, Guillaume Lemaitre, et al.. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals. Intensive Care Medicine, 2021, 47 (12), pp.1426-1439. ⟨10.1007/s00134-021-06524-w⟩. ⟨hal-03967472v2⟩
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- Laurent Chouchana, Nathanaël Beeker, Nathanaël Beeker, Nicolas Garcelon, Nicolas Garcelon, et al.. Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19. Cardiovascular Drugs and Therapy, 2021, 36 (3), pp.483-488. ⟨10.1007/s10557-021-07155-5⟩. ⟨hal-04360151⟩
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- Jordan Jouffroy, Sarah Feldman, Ivan Lerner, Bastien Rance, Anita Burgun, et al.. Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study. JMIR Medical Informatics, 2021, 9 (3), pp.e17934. ⟨10.2196/17934⟩. ⟨hal-03476758⟩
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2020
Journal articles
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- Sébastien Czernichow, Nathanael Beeker, Claire Rives‐lange, Emmanuel Guerot, Jean‐luc Diehl, et al.. Obesity Doubles Mortality in Patients Hospitalized for Severe Acute Respiratory Syndrome Coronavirus 2 in Paris Hospitals, France: A Cohort Study on 5,795 Patients. Obesity, 2020, 28 (12), pp.2282-2289. ⟨10.1002/oby.23014⟩. ⟨hal-03677189⟩
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- Sébastien Czernichow, Nathanael Beeker, Claire Rives-Lange, Emmanuel Guerot, Jean‐luc Diehl, et al.. Obesity Doubles Mortality in Patients Hospitalized for Severe Acute Respiratory Syndrome Coronavirus 2 in Paris Hospitals, France: A Cohort Study on 5,795 Patients. Obesity, 2020, 28 (12), pp.2282-2289. ⟨10.1002/oby.23014⟩. ⟨hal-03967463⟩
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- Ivan Lerner, Nicolas Paris, Xavier Tannier. Terminologies augmented recurrent neural network model for clinical named entity recognition. Journal of Biomedical Informatics, 2020, 102, pp.103356. ⟨10.1016/j.jbi.2019.103356⟩. ⟨hal-02428771⟩
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- Antoine Neuraz, Ivan Lerner, William Digan, Nicolas Paris, Rosy Tsopra, et al.. Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic. Journal of Medical Internet Research, 2020, 22 (8), pp.e20773. ⟨10.2196/20773⟩. ⟨hal-03119925⟩
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- Antoine Neuraz, Ivan Lerner, William Digan, Nicolas Paris, Rosy Tsopra, et al.. Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic. Journal of Medical Internet Research, 2020, 22 (8), pp.e20773. ⟨10.2196/20773⟩. ⟨hal-03738905⟩
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Preprints, Working Papers, …
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- Emilie Sbidian, Julie Josse, Guillaume Lemaître, Imke Mayer, Melodie Bernaux, et al.. Hydroxychloroquine with or without azithromycin and in-hospital mortality or discharge in patients hospitalized for COVID-19 infection: a cohort study of 4,642 in-patients in France. 2020. ⟨hal-02995319⟩
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2019
Journal articles
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- Ivan Lerner, Perrine Créquit, Philippe Ravaud, Ignacio Atal. Automatic screening using word embeddings achieved high sensitivity and workload reduction for updating living network meta-analyses. Journal of Clinical Epidemiology, 2019, 108, pp.86 – 94. ⟨10.1016/j.jclinepi.2018.12.001⟩. ⟨hal-03486140⟩
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Preprints, Working Papers, …
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- Xavier Tannier, Nicolas Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne, et al.. Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials. 2019. ⟨hal-02406975⟩
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