Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning

Cossin, Sébastien; Diouf, Serigne; Griffier, Romain; d’Orgeval, Philippine Le Barrois; Diallo, Gayo; Jouhet, Vianney

doi:10.1093/jamiaopen/ooab005

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…However, this pipeline was not intended for routine use. The Domino's interface [17], developed in 2018 by the University of Bordeaux in France and funded by the French Medicines Agency (Agence nationale de sécurité du médicament et des produits de santé [ANSM]), was designed to analyze drug misuses in health-related web forums using NLP methods and the summary of product characteristics. Initially tailored for antidepressant drugs, this tool does not primarily focus on ADE surveillance.…”

Section: Quantitative Approach For Monitoring Adverse Drug Events On ...mentioning

confidence: 99%

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study

Karapetiantz,

Audeh,

Redjdal

et al. 2024

J Med Internet Res

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Background To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media’s potential remains largely untapped in real-world scenarios. Objective The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. Methods To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums’ posts extraction, (2) web forums’ posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Results Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. Conclusions We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

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Section: Quantitative Approach For Monitoring Adverse Drug Events On ...mentioning

confidence: 99%

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study

Karapetiantz,

Audeh,

Redjdal

et al. 2024

J Med Internet Res

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“…In the clinical domain, many temporal expressions have unique characteristics, and this structure provides comprehensive coverage in encoding these expressions. Abbreviations are widely used in medicine and have been studied in French [183] and English [184] clinical texts to better handle medical abbreviations. Recent embedding-based methods such as BERT have made it easier to study negation detection [181] and text similarity [173,174].…”

Section: Context Analysismentioning

confidence: 99%

“…Publications, n (%) Data language [15-17,19-25,27-38,41-48,50,51,53-68,71,72,74,75,78-80,83-88,90-92,96,97,99-112,114,116,119-124,126-135, 137,140,142-149,151-154,156-159,161,162,165-176,179,181,184-187,189-192,194-196,198,200-208] 153 (78.9) English [39,49,52,73,76,77,81,89,93,94,113,115,118,125,136,138,139,155,163,164,177,178,182,183,188,193,197] 27 (13.9) French [18,26,69,95,117,150,160,180,199] 9 (4.6) German [40,65,82] 3 (1.5) Korean…”

Section: Referencesmentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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“…To perform those queries, ontology alignment have been performed on laboratory results, and ad hoc natural language processing tools have been developed, including ROMEDI (ie, a French drug terminology to extract drug information from text), IAMsystem (ie, a dictionary-based approach for name entity recognition), and SmartCRF (ie, a software to visualize and annotate EHR). [18][19][20] Several applied projects were performed using those tools including automatic detection of surgical site infection and transfusion associated circulatory overload. 21,22 Thanks to those previous experiences, the Bordeaux hospital data warehouse was used, during the pandemic, to describe the current state of the epidemic at the hospital level on a daily basis.…”

Section: Ehr Datamentioning

confidence: 99%

The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France

et al. 2022

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Objective To develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data-warehouse of the Bordeaux hospital were extracted from 2020-05-16 to 2022-01-17. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering and machine learning models. Results During the period of 88 weeks, 2561 hospitalizations due to COVID19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data-warehouse improved median relative error at 7 and 14 days by 10.9 and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection. Discussion Forecast model showed overall good performance both at 7 and 14 days which were improved by the addition of the data from Bordeaux Hospital data-warehouse. Conclusion The development of hospital data-warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale. LAY SUMMARY The objective of this work was to develop a forecast algorithm to predict the number of hospitalized patients at Bordeaux Hospital. In addition, we assessed the benefit of the Electronic Health Records (EHRs) information to perform those predictions. To perform this task, we used data between 2020-05-16 and 2022-01-17 from national database on SARS-CoV-2 epidemics, public database on weather and the data-warehouse of the Bordeaux hospital. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. During the period of 88 weeks, 2561 hospitalizations due to COVID19 were recorded at the Bordeaux Hospital. The best model had an error of 13.6% at 7 days and 19.8% at 14 days. EHRs from the hospital data-warehouse improved the performance by 10% at 7 days and 20% at 14 days. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection. Forecast model showed overall good performance which were improved by the addition of EHRs data. The development of hospital data-warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale.

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Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning

Cited by 5 publications

References 13 publications

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France

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