@article{chapman_simple_2001,
author = {Chapman, Wendy W. and Bridewell, Will and Hanbury, Paul and Cooper, Gregory F. and Buchanan, Bruce G.},
doi = {10.1006/jbin.2001.1029},
file = {Chapman et al. - 2001 - A Simple Algorithm for Identifying Negated Finding.pdf:/home/basile/Zotero/storage/JP6GC4UM/Chapman et al. - 2001 - A Simple Algorithm for Identifying Negated Finding.pdf:application/pdf},
issn = {15320464},
journal = {Journal of Biomedical Informatics},
language = {en},
month = oct,
number = {5},
pages = {301--310},
title = {A {Simple} {Algorithm} for {Identifying} {Negated} {Findings} and {Diseases} in {Discharge} {Summaries}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1532046401910299},
urldate = {2020-12-31},
volume = {34},
year = {2001}
}
@article{bey_natural_2024,
title = {Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality},
volume = {3},
issn = {2731-4251},
url = {https://doi.org/10.1038/s44184-023-00046-7},
doi = {10.1038/s44184-023-00046-7},
abstract = {There is an urgent need to monitor the mental health of large populations, especially during crises such as the {COVID}-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the {COVID}-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the {COVID}-19 outbreak with an estimated trend variation reaching 3.7 (95\%{CI} 2.1–5.3), mainly driven by an increase among girls aged 8–17 (trend variation 1.8, 95\%{CI} 1.2–2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95\%{CI} 1.16–1.48; 1.3, 95\%{CI} 1.10–1.64 and 1.7, 95\%{CI} 1.48–1.98), fewer patients died (p = 0.007) and stays were shorter (p {\textless} 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the {COVID}-19 pandemic.},
pages = {6},
number = {1},
journaltitle = {npj Mental Health Research},
shortjournal = {npj Mental Health Research},
author = {Bey, Romain and Cohen, Ariel and Trebossen, Vincent and Dura, Basile and Geoffroy, Pierre-Alexis and Jean, Charline and Landman, Benjamin and Petit-Jean, Thomas and Chatellier, Gilles and Sallah, Kankoe and Tannier, Xavier and Bourmaud, Aurelie and Delorme, Richard},
date = {2024-02-14},
year = {2024}
}
@article{cossin:hal-02987843,
TITLE = {{Romedi: An Open Data Source About French Drugs on the Semantic Web}},
AUTHOR = {Cossin, Sébastien and Lebrun, Luc and Lobre, Grégory and Loustau, Romain and Jouhet, Vianney and Griffier, Romain and Mougin, Fleur and Diallo, Gayo and Thiessard, Frantz},
URL = {https://hal.archives-ouvertes.fr/hal-02987843},
JOURNAL = {{Studies in Health Technology and Informatics}},
PUBLISHER = {{IOS Press}},
VOLUME = {264},
PAGES = {79-82},
YEAR = {2019},
MONTH = Aug,
DOI = {10.3233/SHTI190187},
KEYWORDS = {Controlled ; Pharmaceutical Preparations ; Semantics ; Vocabulary},
HAL_ID = {hal-02987843},
HAL_VERSION = {v1},
}
@inproceedings{grabar2018CAS,
address = {Bruxelles, France},
author = {Grabar, Natalia and Claveau, Vincent and Dalloux, Clément},
booktitle = {{LOUHI 2018 - The Ninth International Workshop on Health Text Mining and Information Analysis}},
hal_id = {hal-01937096},
hal_version = {v1},
month = Oct,
pages = {1-7},
pdf = {https://hal.archives-ouvertes.fr/hal-01937096/file/corpus_Louhi_2018.pdf},
series = {Ninth International Workshop on Health Text Mining and Information Analysis (LOUHI) Proceedings of the Workshop},
title = {{CAS: French Corpus with Clinical Cases}},
url = {https://hal.archives-ouvertes.fr/hal-01937096},
year = {2018}
}
@inproceedings{dalloux2017ESSAI,
address = {Toulouse, France},
author = {Dalloux, Clément and Claveau, Vincent and Grabar, Natalia},
booktitle = {{SIIM 2017 - Symposium sur l'Ingénierie de l'Information Médicale}},
hal_id = {hal-01659637},
hal_version = {v1},
month = Nov,
pages = {1-8},
pdf = {https://hal.archives-ouvertes.fr/hal-01659637/file/SIIM2017.pdf},
title = {Détection de la négation : corpus français et apprentissage supervisé},
url = {https://hal.archives-ouvertes.fr/hal-01659637},
year = {2017}
}
@article{wajsburt2021medical,
title={Medical concept normalization in French using multilingual terminologies and contextual embeddings},
author={Wajsbürt, Perceval and Sarfati, Arnaud and Tannier, Xavier},
journal={Journal of Biomedical Informatics},
volume={114},
pages={103684},
year={2021},
url = {https://doi.org/10.1016/j.jbi.2021.103684},
publisher={Elsevier}
}
@phdthesis{wajsburt:tel-03624928,
TITLE = {{Extraction and normalization of simple and structured entities in medical documents}},
AUTHOR = {Wajsbürt, Perceval},
URL = {https://hal.archives-ouvertes.fr/tel-03624928},
SCHOOL = {{Sorbonne Université}},
YEAR = {2021},
MONTH = Dec,
KEYWORDS = {nlp ; structure ; extraction ; normalization ; clinical ; multilingual},
TYPE = {Theses},
PDF = {https://hal.archives-ouvertes.fr/tel-03624928/file/updated_phd_thesis_PW.pdf},
HAL_ID = {tel-03624928},
HAL_VERSION = {v1},
}
@inproceedings{zweigenbaum2016,
abstract = {Dans certains textes bruts, les marques de fin de ligne peuvent marquer ou pas la frontière d{'}une unité textuelle (typiquement un paragraphe). Ce problème risque d{'}influencer les traitements subséquents, mais est rarement traité dans la littérature. Nous proposons une méthode entièrement non-supervisée pour déterminer si une fin de ligne doit être vue comme un simple espace ou comme une véritable frontière d{'}unité textuelle, et la testons sur un corpus de comptes rendus médicaux. Cette méthode obtient une F-mesure de 0,926 sur un échantillon de 24 textes contenant des lignes repliées. Appliquée sur un échantillon plus grand de textes contenant ou pas des lignes repliées, notre méthode la plus prudente obtient une F-mesure de 0,898, valeur élevée pour une méthode entièrement non-supervisée.},
address = {Paris, France},
author = {Zweigenbaum, Pierre and
Grouin, Cyril and
Lavergne, Thomas},
booktitle = {Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)},
language = {French},
month = {7},
pages = {364--371},
publisher = {AFCP - ATALA},
title = {Une catégorisation de fins de lignes non-supervisée (End-of-line classification with no supervision)},
url = {https://aclanthology.org/2016.jeptalnrecital-poster.7},
year = {2016}
}
@article{kempf:hal-03519085,
TITLE = {{Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals}},
AUTHOR = {Kempf, Emmanuelle and Priou, Sonia and Lamé, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, yazid and Bey, Romain and Galula, Gilles and Taright, Namik and Tannier, Xavier and Rance, Bastien and Flicoteaux, Rémi and Hemery, François and Audureau, Etienne and Chatellier, Gilles and Tournigand, Christophe},
URL = {https://hal.archives-ouvertes.fr/hal-03519085},
JOURNAL = {{International Journal of Cancer}},
PUBLISHER = {{Wiley}},
VOLUME = {150},
NUMBER = {10},
PAGES = {1609-1618},
YEAR = {2022},
DOI = {10.1002/ijc.33928},
KEYWORDS = {Delivery of Health Care ; Health Services Research ; Colorectal Neoplasms ; Quality of Health Care ; COVID-19},
PDF = {https://hal.archives-ouvertes.fr/hal-03519085/file/IJC2022%20accepted_vHAL%20%281%29.pdf},
HAL_ID = {hal-03519085},
HAL_VERSION = {v1},
}
@misc{terminologie-adicap,
TITLE = {{Thésaurus de la codification ADICAP - Index raisonné des lésions}},
URL = {http://esante.gouv.fr/terminologie-adicap},
VERSION = {2019-05},
YEAR = {2019},
MONTH = {05},
ID = {1.2.250.1.213.2.11},
AUTHOR = {Agence du numérique en santé},
DETAILS = {https://smt.esante.gouv.fr/wp-json/ans/terminologies/document?terminologyId=terminologie-adicap&fileName=cgts_sem_adicap_fiche-detaillee.pdf},
}
@article{petitjean_2024,
author = {Petit-Jean, Thomas and Gérardin, Christel and Berthelot, Emmanuelle and Chatellier, Gilles and Frank, Marie and Tannier, Xavier and Kempf, Emmanuelle and Bey, Romain},
title = "{Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions}",
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {6},
pages = {1280-1290},
year = {2024},
month = {04},
abstract = "{To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow.The detection pipeline relied both on rule-based and machine learning algorithms, respectively, for named entity recognition and entity qualification, respectively. We used a large language model pre-trained on millions of clinical notes along with annotated clinical notes in the context of 3 cohort studies related to oncology, cardiology, and rheumatology. The overall workflow was conceived to foster collaboration between studies while respecting the privacy constraints of the data warehouse. We estimated the added values of the advanced technologies and of the collaborative setting.The pipeline reached macro-averaged F1-score positive predictive value, sensitivity, and specificity of 95.7 (95\\%CI 94.5-96.3), 95.4 (95\\%CI 94.0-96.3), 96.0 (95\\%CI 94.0-96.7), and 99.2 (95\\%CI 99.0-99.4), respectively. F1-scores were superior to those observed using alternative technologies or non-collaborative settings. The models were shared through a secured registry.We demonstrated that a community of investigators working on a common clinical data warehouse could efficiently and securely collaborate to develop, validate and use sensitive artificial intelligence models. In particular, we provided an efficient and robust NLP pipeline that detects conditions mentioned in clinical notes.}",
issn = {1527-974X},
doi = {10.1093/jamia/ocae069},
url = {https://doi.org/10.1093/jamia/ocae069},
eprint = {https://academic.oup.com/jamia/article-pdf/31/6/1280/57769016/ocae069.pdf},
}
@misc{dozat2017deepbiaffineattentionneural,
title={Deep Biaffine Attention for Neural Dependency Parsing},
author={Timothy Dozat and Christopher D. Manning},
year={2017},
eprint={1611.01734},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1611.01734},
}
@inproceedings{grobol:hal-03223424,
title = {{Analyse en dépendances du français avec des plongements contextualisés}},
author = {Grobol, Loïc and Crabbé, Benoît},
url = {https://hal.archives-ouvertes.fr/hal-03223424},
year = {2021},
booktitle = {{Actes de la 28ème Conférence sur le Traitement Automatique des Langues Naturelles}},
eventtitle = {{TALN-RÉCITAL 2021}},
venue = {Lille, France},
pdf = {https://hal.archives-ouvertes.fr/hal-03223424/file/HOPS_final.pdf},
hal_id = {hal-03223424},
hal_version = {v1},
}
