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Article ; Online: TAXN: Translate Align Extract Normalize, a Multilingual Extraction Tool for Clinical Texts.

Neuraz, Antoine / Lerner, Ivan / Birot, Olivier / Arias, Camila / Han, Larry / Bonzel, Clara Lea / Cai, Tianxi / Huynh, Kim Tam / Coulet, Adrien

Studies in health technology and informatics

2024 Volume 310, Page(s) 649–653

Abstract: 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 medical terminologies in languages other than English are limited and restrain the NLP ... ...

Abstract	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 medical terminologies in languages other than English are limited and restrain the NLP tools. We propose here to leverage English based resources in other languages using a combination of translation, word alignment, entity extraction and term normalization (TAXN). We implement this extraction pipeline in an open-source library called "medkit". We demonstrate the interest of this approach through a specific use-case: enriching a phenotypic dictionary for post-acute sequelae in COVID-19 (PASC). TAXN proved to be efficient to propose new synonyms of UMLS terms using a corpus of 70 articles in French with 356 terms enriched with at least one validated new synonym. This study was based on freely available deep-learning models.
MeSH term(s)	Humans ; Multilingualism ; Language ; Disease Progression ; Electronic Health Records
Language	English
Publishing date	2024-01-25
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI231045
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using Deep Learning to Improve Phenotyping from Clinical Reports.

Vincent, Marc / Douillet, Maxime / Lerner, Ivan / Neuraz, Antoine / Burgun, Anita / Garcelon, Nicolas

Studies in health technology and informatics

2022 Volume 290, Page(s) 282–286

Abstract: With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require ...

Abstract	With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children's hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.
MeSH term(s)	Child ; Databases, Factual ; Deep Learning ; Humans ; Natural Language Processing
Language	English
Publishing date	2022-06-08
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI220079
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Machine-learning-derived sepsis bundle of care.

Kalimouttou, Alexandre / Lerner, Ivan / Cheurfa, Chérifa / Jannot, Anne-Sophie / Pirracchio, Romain

Intensive care medicine

2022 Volume 49, Issue 1, Page(s) 26–36

Abstract: Purpose: Compliance to the Surviving Sepsis Campaign (SSC) guidelines is limited. This is known to be associated with increased mortality. The aim of this retrospective cohort study was to identify among the SCC guidelines the optimal bundle of ... ...

Abstract	Purpose: Compliance to the Surviving Sepsis Campaign (SSC) guidelines is limited. This is known to be associated with increased mortality. The aim of this retrospective cohort study was to identify among the SCC guidelines the optimal bundle of recommendations that minimize 28-day mortality. Methods: We used a training cohort to identify, using a least absolute shrinkage and selection operator penalized machine learning model, this bundle. Patients with sepsis/septic shock admitted to the intensive care unit (ICU) were extracted from two US databases, the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training and internal validation cohorts) and the eICU Collaborative Research Database (eICU-CRD) (external validation cohort). In the validation cohorts, we defined a bundle group that includes patients who were treated with at least all the recommendations selected in our bundle and a no-bundle group that includes patients in whom at least one recommendation from our bundle was omitted. Results: All-cause 28-day mortality was the primary outcome measure. A total of 42,735 patients were included. Six recommendations (antimicrobials, balanced crystalloid, insulin therapy, corticosteroids, vasopressin, and bicarbonate therapy) were identified from the training cohort to be included in our bundle. In the propensity score-(PS)-matched internal validation cohort, the bundle group was associated with a lower mortality (OR 0.41 [0.33-0.53]; p < 0.001) compared to the no-bundle group. This was confirmed in the PS-matched external validation cohort (OR 0.75 [0.60-0.94]; p 0.02). Conclusion: Our bundle of six recommendations is associated with a dramatic reduction in mortality in sepsis and septic shock. This bundle needs to be evaluated prospectively.
MeSH term(s)	Humans ; Shock, Septic/therapy ; Retrospective Studies ; Length of Stay ; Guideline Adherence ; Sepsis/therapy ; Intensive Care Units ; Hospital Mortality
Language	English
Publishing date	2022-11-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	80387-x
ISSN	1432-1238 ; 0340-0964 ; 0342-4642 ; 0935-1701
ISSN (online)	1432-1238
ISSN	0340-0964 ; 0342-4642 ; 0935-1701
DOI	10.1007/s00134-022-06928-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

Lerner, Ivan / Serret-Larmande, Arnaud / Rance, Bastien / Garcelon, Nicolas / Burgun, Anita / Chouchana, Laurent / Neuraz, Antoine

JMIR medical informatics

2022 Volume 10, Issue 4, Page(s) e38505

Abstract: This corrects the article DOI: 10.2196/35190.]. ...

Abstract	[This corrects the article DOI: 10.2196/35190.].
Language	English
Publishing date	2022-04-12
Publishing country	Canada
Document type	Published Erratum
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/38505
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Terminologies augmented recurrent neural network model for clinical named entity recognition.

Lerner, Ivan / Paris, Nicolas / Tannier, Xavier

Journal of biomedical informatics

2019 Volume 102, Page(s) 103356

Abstract: Objective: We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities.: Methods: We ... ...

Abstract	Objective: We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. Methods: We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and a hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (Drug names, Signs or symptoms, Diseases or disorders, Diagnostic procedures or lab tests and Therapeutic procedures). We evaluated each NER systems using exact and partial match definition of F-measure for NER. The APcNER contains 4,837 entities, which took 28 h to annotate. The inter-annotator agreement as measured by Cohen's Kappa was substantial for non-exact match (Κ = 0.61) and moderate considering exact match (Κ = 0.42). In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents, and i2b2-small a version reduced to match APcNER number of entities. Results: For drug name recognition on both i2b2-2009 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75% respectively. For i2b2-small and APcNER, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 87.8% versus 85.6% and 86.4% versus 81.9% respectively. On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5% in exact match and 84.1% in non-exact match. Conclusion: APcNER is a French corpus for clinical-NER of five types of entities which covers a large variety of document types. The extension of the supervised model with terminology has allowed an easy increase in performance, especially for rare entities, and established near state of the art results on the i2b2-2009 corpus.
MeSH term(s)	Language ; Natural Language Processing ; Neural Networks, Computer ; Terminology as Topic
Language	English
Publishing date	2019-12-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2019.103356
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study.

Jouffroy, Jordan / Feldman, Sarah F / Lerner, Ivan / Rance, Bastien / Burgun, Anita / Neuraz, Antoine

JMIR medical informatics

2021 Volume 9, Issue 3, Page(s) e17934

Abstract: Background: Information related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural ...

Abstract	Background: Information related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural language processing extracting medical information from unstructured text from French corpora. Objective: We aimed to develop a system to extract medication-related information from clinical text written in French. Methods: We developed a hybrid system combining an expert rule-based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory-conditional random field). The task consisted of extracting drug mentions and their related information (eg, dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes from a French clinical data warehouse to train and evaluate the model. We compared the performance of our approach to those of standard approaches: rule-based or machine learning only and classic word embeddings. We evaluated the models using token-level recall, precision, and F-measure. Results: The overall F-measure was 89.9% (precision 90.8; recall: 89.2) when combining expert rules and contextualized embeddings, compared to 88.1% (precision 89.5; recall 87.2) without expert rules or contextualized embeddings. The F-measures for each category were 95.3% for medication name, 64.4% for drug class mentions, 95.3% for dosage, 92.2% for frequency, 78.8% for duration, and 62.2% for condition of the intake. Conclusions: Associating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.
Language	English
Publishing date	2021-03-16
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/17934
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Automatic screening using word embeddings achieved high sensitivity and workload reduction for updating living network meta-analyses.

Lerner, Ivan / Créquit, Perrine / Ravaud, Philippe / Atal, Ignacio

Journal of clinical epidemiology

2018 Volume 108, Page(s) 86–94

Abstract: Objectives: We aimed to develop and evaluate an algorithm for automatically screening citations when updating living network meta-analysis (NMA).: Study design and setting: Our algorithm learns from the initial screening of citations conducted when ... ...

Abstract	Objectives: We aimed to develop and evaluate an algorithm for automatically screening citations when updating living network meta-analysis (NMA). Study design and setting: Our algorithm learns from the initial screening of citations conducted when creating an NMA to automatically identify eligible citations (i.e., needing full-text consideration) when updating the NMA. We evaluated our algorithm on four NMAs from different medical domains. For each NMA we constructed sets of initially screened citations and citations to screen during an update that took place 2 years after the conduct of the NMA. We encoded free text of citations (title and abstract) using word embeddings. On top of this vectorized representation, we fitted a logistic regression model to the set of initially screened citations to predict the eligibility of citations screened during an update. Results: Our algorithm achieved 100% sensitivity on two NMAs (100% [95% confidence interval 93-100] and 100% [40-100] sensitivity), and 94% (81-99) and 97% (86-100) on the remaining two others. For all NMAs, our algorithm would have spared to manually screen 1,345 of 2,530 citations, decreasing the workload by 53% (51-55), while missing 3 of 124 eligible citations (2% [1-7]), none of which were finally included in the NMAs after full-text consideration. Conclusion: For updating an NMA after 2 years, our algorithm considerably diminished the workload required for screening, and the number of missed eligible citations remained low.
MeSH term(s)	Algorithms ; Confidence Intervals ; Evidence-Based Medicine/methods ; Humans ; Information Storage and Retrieval/methods ; Network Meta-Analysis ; Randomized Controlled Trials as Topic ; Support Vector Machine ; Workload
Language	English
Publishing date	2018-12-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639306-8
ISSN	1878-5921 ; 0895-4356
ISSN (online)	1878-5921
ISSN	0895-4356
DOI	10.1016/j.jclinepi.2018.12.001
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

Lerner, Ivan / Serret-Larmande, Arnaud / Rance, Bastien / Garcelon, Nicolas / Burgun, Anita / Chouchana, Laurent / Neuraz, Antoine

JMIR medical informatics

2022 Volume 10, Issue 3, Page(s) e35190

Abstract: Background: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the ... ...

Abstract	Background: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. Objective: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. Methods: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. Results: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. Conclusions: Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.
Language	English
Publishing date	2022-03-30
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/35190
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Congruence between Meshes and Library Files of Implant Scanbodies: An In Vitro Study Comparing Five Intraoral Scanners.

Mangano, Francesco / Lerner, Henriette / Margiani, Bidzina / Solop, Ivan / Latuta, Nadezhda / Admakin, Oleg

Journal of clinical medicine

2020 Volume 9, Issue 7

Abstract: Purpose: To compare the reliability of five different intraoral scanners (IOSs) in the capture of implant scanbodies (SBs) and to verify the dimensional congruence between the meshes (MEs) of the SBs and the corresponding library file (LF).: Methods: ...

Abstract	Purpose: To compare the reliability of five different intraoral scanners (IOSs) in the capture of implant scanbodies (SBs) and to verify the dimensional congruence between the meshes (MEs) of the SBs and the corresponding library file (LF). Methods: A gypsum cast of a fully edentulous maxilla with six implant analogues and SBs screwed on was scanned with five different IOSs (PRIMESCAN Results: PRIMESCAN Conclusions: Statistically different levels of congruence were found between the SB MEs and the corresponding LF when using different IOSs. Significant differences were also found between different SBs when scanned with the same IOS. Finally, the qualitative evaluation revealed different directions and patterns for the five IOSs.
Language	English
Publishing date	2020-07-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm9072174
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Learning the grammar of drug prescription

Lerner, Ivan / Jouffroy, Jordan / Burgun, Anita / Neuraz, Antoine

recurrent neural network grammars for medication information extraction in clinical texts

2020

Abstract: In this study, we evaluated the RNNG, a neural top-down transition based parser, for medication information extraction in clinical texts. We evaluated this model on a French clinical corpus. The task was to extract the name of a drug (or a drug class), ... ...

Abstract	In this study, we evaluated the RNNG, a neural top-down transition based parser, for medication information extraction in clinical texts. We evaluated this model on a French clinical corpus. The task was to extract the name of a drug (or a drug class), as well as attributes informing its administration: frequency, dosage, duration, condition and route of administration. We compared the RNNG model that jointly identifies entities, events and their relations with separate BiLSTMs models for entities, events and relations as baselines. We call seq-BiLSTMs the baseline models for relations extraction that takes as extra-input the output of the BiLSTMs for entities and events. Similarly, we evaluated seq-RNNG, a hybrid RNNG model that takes as extra-input the output of the BiLSTMs for entities and events. RNNG outperforms seq-BiLSTM for identifying complex relations, with on average 88.1 [84.4-91.6] % versus 69.9 [64.0-75.4] F-measure. However, RNNG tends to be weaker than the baseline BiLSTM on detecting entities, with on average 82.4 [80.8-83.8] versus 84.1 [82.7-85.6] % F- measure. RNNG trained only for detecting relations tends to be weaker than RNNG with the joint modelling objective, 87.4% [85.8-88.8] versus 88.5% [87.2-89.8]. Seq-RNNG is on par with BiLSTM for entities (84.0 [82.6-85.4] % F-measure) and with RNNG for relations (88.7 [87.4-90.0] % F-measure). The performance of RNNG on relations can be explained both by the model architecture, which provides inductive bias to capture the hierarchy in the targets, and the joint modeling objective which allows the RNNG to learn richer representations. RNNG is efficient for modeling relations between entities or/and events in medical texts and its performances are close to those of a BiLSTM for entity and event detection.
Keywords	Computer Science - Computation and Language
Subject code	410
Publishing date	2020-04-24
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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