A5069383408- Biomedical Text Mining and Ontologies
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
- COVID-19 Clinical Research Studies
- Long-Term Effects of COVID-19
- Sepsis Diagnosis and Treatment
- SARS-CoV-2 and COVID-19 Research
- Pharmacovigilance and Adverse Drug Reactions
- Text Readability and Simplification
- Acute Myocardial Infarction Research
- Misinformation and Its Impacts
- COVID-19 and Mental Health
- Cinema and Media Studies
- Advanced Causal Inference Techniques
- COVID-19 and healthcare impacts
- Meta-analysis and systematic reviews
- Lung Cancer Diagnosis and Treatment
- Lung Cancer Research Studies
- Turkey's Politics and Society
- Health Promotion and Cardiovascular Prevention
- Healthcare Systems and Public Health
- Cardiac Imaging and Diagnostics
- Mental Health Research Topics
- Cardiovascular Syncope and Autonomic Disorders
Assistance Publique – Hôpitaux de Paris
2018-2024
Institut national de recherche en informatique et en automatique
2021-2024
Sorbonne Université
2020-2024
Université Paris Cité
2018-2024
Inserm
2018-2024
Hôpital Necker-Enfants Malades
2020-2024
Centre de Recherche des Cordeliers
2020-2024
Sorbonne Paris Cité
2018-2024
Hôpital Européen Georges-Pompidou
2022-2024
Hôpital Européen
2024
Although sudden cardiac death (SCD) is recognized as a high-priority public health topic, reliable estimates of the incidence SCD or, more broadly, out-of-hospital arrest (OHCA), in population are scarce, especially European Union.The study objective was to determine and OHCA examined 4 large (ie, >2 million inhabitants) population-based prospective registries collecting emergency medical services (EMS)-attended with attempted resuscitation) (OHCA without obvious extracardiac causes) for >5...
Background A novel disease poses special challenges for informatics solutions. Biomedical relies the most part on structured data, which require a preexisting data or knowledge model; however, diseases do not have models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to model. However, although this idea has often been suggested, no opportunity arisen actually test it in real time. The current coronavirus (COVID-19) pandemic presents such...
Abstract Objective To assess the clinical effectiveness of oral hydroxychloroquine (HCQ) with or without azithromycin (AZI) in preventing death leading to hospital discharge. Design Retrospective cohort study. Setting An analysis data from electronic medical records and administrative claim French Assistance Publique - Hôpitaux de Paris (AP-HP) warehouse, 39 public hospitals, Ile-de-France, France. Participants All adult inpatients at least one PCR-documented SARS-CoV-2 RNA a nasopharyngeal...
Information related to patient medication is crucial for health care; however, up 80% of the information resides solely in unstructured text. Manual extraction difficult and time-consuming, there not a lot research on natural language processing extracting medical from text French corpora.
Prior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific are intrinsic risk factors of death in COVID-19 these associations reflect the greater prevalence medical people has yet evaluated.We performed an observational, multicenter, retrospective cohort study examine association between and hospitalized for laboratory-confirmed at 36 Greater Paris University hospitals.Of 15,168 adult patients, 857...
We develop and evaluate a structure learning algorithm for clinical time series. Clinical series are multivariate observed in multiple patients irregularly sampled, challenging existing algorithms. assume that our times realizations of StructGP, k-dimensional multi-output or multi-task stationary Gaussian process (GP), with independent sharing the same covariance function. StructGP encodes ordered conditional relations between series, represented directed acyclic graph. implement an adapted...
With the development of clinical databases and ubiquity EHRs, physicians researchers alike have access to an unprecedented amount data. Complexity available data has also increased since reports are included require frameworks with natural language processing capabilities in order process them extract information not found other types documents. In following work we implement a pipeline performing phenotyping, disambiguation, negation subject prediction on such reports. We compare it...
Several studies have shown that about 80% of the medical information in an electronic health record is only available through unstructured data. Resources such as terminologies languages other than English are limited and restrain NLP tools. We propose here to leverage based resources using a combination translation, word alignment, entity extraction term normalization (TAXN). implement this pipeline open-source library called “medkit”. demonstrate interest approach specific use-case:...
Abstract Summary Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of collection Electronic Health Records (EHRs). Because lot the clinical information EHRs are lying texts, phenotyping from text takes an important role studies that rely on secondary use EHRs. However, heterogeneity and highly specialized aspect both content form texts makes this task particularly tedious, is source time cost...
<sec> <title>BACKGROUND</title> Information related to patient medication is crucial for health care; however, up 80% of the information resides solely in unstructured text. Manual extraction difficult and time-consuming, there not a lot research on natural language processing extracting medical from text French corpora. </sec> <title>OBJECTIVE</title> We aimed develop system extract medication-related clinical written French. <title>METHODS</title> developed hybrid combining an expert...
Patients hospitalized for a given condition may be receiving other treatments contemporary conditions or comorbidities. The use of such observational clinical data pharmacological hypothesis generation is appealing in the context an emerging disease but particularly challenging due to presence drug indication bias.With this study, our main objective was development and validation fully data-driven pipeline that would address challenge. Our secondary generate hypotheses patients with COVID-19...
Background Little is known about the functional consequences of violence when directly assessed as a primary outcome, and even less how consistently these are evaluated in judicial context. The World Health Organization (WHO) highlighted importance approach to health 2001 with release International Classification Functioning, Disability, (ICF). In most European countries, forensic physicians assess individuals exposed evaluate outcomes violence, providing certified medical evidence for...
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL Learning Latent Patient Pathways Clinical Time Series: A and Cohort Scale-Free Dependency Structure for Process Convolution Models 21 Pages Posted: 25 Feb 2024 See all articles by Ivan LernerIvan LernerUniversité Paris CitéJean FeydySorbonne-UniversitéAlexandre KalimouttouUniversité Est Créteil - AP-HPAntoine NeurazSorbonne-UniversitéAnita...
Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of collection Electronic Health Records (EHRs). Because lot the clinical information EHRs are lying texts, phenotyping from text takes an important role studies that rely on secondary use EHRs. However, heterogeneity and highly specialized aspect both content form texts makes this task particularly tedious, is source time cost constraints...
Recent evidence suggests that elevated levels of PD-L1 expression may be linked to early resistance TKI and reduced survival in NSCLC with EGFR mutations. This study aimed characterize the clinical molecular features EGFR-mutated lung adenocarcinomas determine prognostic significance associated high expression.