Automatic classification of doctor-patient questions for a virtual
            patient record query task

Llanos, Leonardo Campillos; Rosset, Sophie; Zweigenbaum, Pierre

doi:10.18653/v1/w17-2343

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…In the short to medium term, our objectives are updating the annotation module to enhance accuracy, improving the qualitative analysis by enabling users to edit and correct annotations, and expanding the range of signal detection methods available in the statistics module. This method could indeed be beneficial for identifying potential drug misuse and unknown ADEs [40]. By categorizing pathological terms found in web forums based on their presence in the summary of product characteristics, we can distinguish between indications, known ADEs, and potential instances of drug misuse or unexpected ADEs.…”

Section: Perspectivesmentioning

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: Perspectivesmentioning

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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“…Other good works are as follows: Llanos et al [30] presented a model, where they focused on automating the classification of doctor-patient questions by simulating the consultations with virtual patients. They classified questions by looking up data in the clinical record using the computational strategy, and achieved an Average F1-score of 81.2%.…”

Section: Medical Question Classificationmentioning

confidence: 99%

An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

Mutabazi

Tang

et al. 2023

Applied Sciences

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Question Classification (QC) is the fundamental task for Question Answering Systems (QASs) implementation, and is a vital task, as it helps in identifying the question category. It plays a big role in predicting the answer to a question while building a QAS. However, classifying medical questions is still a challenging task due to the complexity of medical terms. Many researchers have proposed different techniques to solve these problems, but some of these problems remain partially solved or unsolved. With the help of deep learning technology, various text-processing problems have become much easier to solve. In this paper, an improved deep learning-based model for Medical Forum Question Classification (MFQC) is proposed to classify medical questions. In the proposed model, feature representation is performed using Word2Vec, which is a word embedding model. Additionally, the features are extracted from the word embedding layer based on Convolutional Neural Networks (CNNs). Finally, a Bidirectional Long Short Term Memory (BiLSTM) network is used to classify the extracted features. The BiLSTM model analyzes the target information of the representation and then outputs the question category via a SoftMax layer. Our model achieves state-of-the-art performance by effectively capturing semantic and syntactic features from the input questions. We evaluate the proposed CNN-BiLSTM model on two benchmark datasets and compare its performance with existing methods, demonstrating its superiority in accurately categorizing medical forum questions.

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“…Recent studies have exposed the importance of biomedical NLP in the well-being of human-beings, analyzing the critical process of medical decisionmaking. However, the dialogue managing tools targeted for medical conversations (Zhang et al, 2020), (Campillos Llanos et al, 2017), (Kazi and Kahanda, 2019) between patients and healthcare providers in assisting diagnosis may generate certain insignificant perturbations (spelling errors, paraphrasing), which when fed to the classifier to determine the type of diagnosis required/detecting adverse drug effects/drug recommendation, might provide unreasonable performance. Insignificant perturbations might also creep in from the casual language expressed in the tweets (Zilio et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification

Mondal

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans. Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the everincreasing biomedical literature poses unique challenges. We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement. Through automatic and human evaluation on two datasets, we demonstrate that BBAEG performs stronger attack with better language fluency, semantic coherence as compared to prior work.

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Automatic classification of doctor-patient questions for a virtual patient record query task

Cited by 7 publications

References 33 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

An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification

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