Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area.

Objectives: To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.

Objectives: Analyze the content of publications within the medical natural language processing (NLP) domain in 2021.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Automatic Prediction of Semantic Labels for French Medical Terms.

We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.

Impact of the Text Simplification on Understanding.

The reduction of the linguistic complexity of medical texts to make them more understandable to a larger population is an important task. The simplification of texts involves several steps, among which our study focuses on the definition of complex constructions and on study of the impact of the simplification. For this study, we selected 20 texts from the medical domain on different topics, namely drugs, diseases, substances, and medical institutions.

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing.

Objectives: To analyze the content of publications within the medical NLP domain in 2020.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Disambiguation of Medical Abbreviations in French with Supervised Methods.

Abbreviations are very frequent in medical and health documents but they convey opaque semantics. The association with their expanded forms, like Chronic obstructive pulmonary disease for COPD, may help their understanding. Yet, several abbreviations are ambiguous and have expanded forms possible.

Fine-Grained Simplification of Medical Documents.

Easy access to medical and health information for children, foreigners and patients is an important issue for the modern society and research. Indeed, misunderstanding of medical and health information by patients may have a negative impact on their healthcare process and health. Even if several simplification guidelines exist, they are difficult to use by medical experts (i.

A Year of Papers Using Biomedical Texts.

Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field.

Methods: We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019.

CAS: corpus of clinical cases in French.

Background: Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing those applications and the corresponding tools. They are also crucial for designing reliable methods and reproducible results. Yet, in some areas, such as the medical area, due to confidentiality or to ethical reasons, it is complicated or even impossible to access representative textual data.

Supervised Learning for the ICD-10 Coding of French Clinical Narratives.

Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automatic methods based on neural network approaches for the automatic detection of the ICD-10 codes.

Parallel Sentence Alignment from Biomedical Comparable Corpora.

Parallel sentences provide semantically similar information which can vary on a given dimension, such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be exploited for the automatic text simplification. The aim of automatic text simplification is to better access and understand a given information.

Generalizability of Readability Models for Medical Terms.

Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms using various features with word embeddings which contain context information of words. Data in French are manually cross-annotated by seven annotators.

Detecting Drug Non-Compliance in Internet Fora Using Information Retrieval and Machine Learning Approaches.

Non-compliance situations happen when patients do not follow their prescriptions and take actions that lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information.

A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook.

Objectives: To analyze the content of publications within the medical Natural Language Processing (NLP) domain in 2018.

Methods: Automatic and manual pre-selection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Expertise in French health forums.

More and more health websites hire medical experts (physicians, medical students, experienced volunteers, etc.) and indicate explicitly their medical role in order to notify that they provide high-quality answers. However, medical experts may participate in forum discussions even when their role is not officially indicated.

Detection and Analysis of Drug Misuses. A Study Based on Social Media Messages.

Drug misuse may happen when patients do not follow the prescriptions and do actions which lead to potentially harmful situations, such as intakes of incorrect dosage (overuse or underuse) or drug use for indications different from those prescribed. Although such situations are dangerous, patients usually do not report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues.

Typology of Drug Misuse Created from Information Available in Health Fora.

Patients seldom report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues. We assume that online health fora can provide such information and propose to exploit them for building a typology of drug misuses.

Drug safety and big clinical data: Detection of drug-induced anaphylactic shock events.

Rationale, Aims, And Objectives: The spontaneous reporting system currently used in pharmacovigilance is not sufficiently exhaustive to detect all adverse drug reactions (ADRs). With the widespread use of electronic health records, biomedical data collected during the clinical care process can be reused and analysed to better detect ADRs. The aim of this study was to assess whether querying a Clinical Data Warehouse (CDW) could increase the detection of drug-induced anaphylaxis.

Acquisition of Expert/Non-Expert Vocabulary from Reformulations.

Technical medical terms are complicated to be correctly understood by non-experts. Vocabulary, associating technical terms with layman expressions, can help in increasing the readability of technical texts and their understanding. The purpose of our work is to build this kind of vocabulary.

Automated Detection of Health Websites' HONcode Conformity: Can N-gram Tokenization Replace Stemming?

Authors evaluated supervised automatic classification algorithms for determination of health related web-page compliance with individual HONcode criteria of conduct using varying length character n-gram vectors to represent healthcare web page documents. The training/testing collection comprised web page fragments extracted by HONcode experts during the manual certification process. The authors compared automated classification performance of n-gram tokenization to the automated classification performance of document words and Porter-stemmed document words using a Naive Bayes classifier and DF (document frequency) dimensionality reduction metrics.

Generating and Executing Complex Natural Language Queries across Linked Data.

With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases.

Health consumer-oriented information retrieval.

While patients can freely access their Electronic Health Records or online health information, they may not be able to correctly understand the content of these documents. One of the challenges is related to the difference between expert and non-expert languages. We propose to investigate this issue within the Information Retrieval field.

Automatic extraction of numerical values from unstructured data in EHRs.

Clinical data recorded in modern EHRs are very rich, although their secondary use research and medical decision may be complicated (eg, missing and incorrect data, data spread over several clinical databases, information available only within unstructured narrative documents). We propose to address the issue related to the processing of narrative documents in order to detect and extract numerical values and to associate them with the corresponding concepts (or themes) and units. We propose to use a CRF supervised categorisation for the detection of segments (themes, numerical sequences and units) and a rules-based system for the association of these segments among them in order to build semantically meaningful sequences.

Discovering the role of morphology on the understanding of biomedical terminology by paramedical students.

The ability to learn specialized languages, such as biomedical language, requires not only specialized knowledge specific to this area, but also linguistic skills. We propose to study this hypothesis on the example of biomedical language as it is learned by advanced paramedical students in Algeria. Two particularities are to be addressed: linguistic specificities of biomedical terms and the fact that learning process is done in French while the native language of students is Arabic.

Semantic distance-based creation of clusters of pharmacovigilance terms and their evaluation.

Background: Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. The detection of adverse drug reactions is performed using statistical algorithms and groupings of ADR terms from the MedDRA (Medical Dictionary for Drug Regulatory Activities) terminology. Standardized MedDRA Queries (SMQs) are the groupings which become a standard for assisting the retrieval and evaluation of MedDRA-coded ADR reports worldwide.