AIMC Topic: Natural Language Processing

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Participatory Design of a Clinical Trial Eligibility Criteria Simplification Method.

Studies in health technology and informatics
Clinical trial eligibility criteria are important for selecting the right participants for clinical trials. However, they are often complex and not computable. This paper presents the participatory design of a human-computer collaboration method for ...

Consolidated EHR Workflow for Endoscopy Quality Reporting.

Studies in health technology and informatics
Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health recor...

Transfer Learning for Classifying Spanish and English Text by Clinical Specialties.

Studies in health technology and informatics
Transfer learning has demonstrated its potential in natural language processing tasks, where models have been pre-trained on large corpora and then tuned to specific tasks. We applied pre-trained transfer models to a Spanish biomedical document class...

A Deep Learning Framework for Automated ICD-10 Coding.

Studies in health technology and informatics
The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a pati...

Prescreening in Oncology Using Data Sciences: The PreScIOUS Study.

Studies in health technology and informatics
The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to ta...

Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches.

Studies in health technology and informatics
Unstructured medical text labeling technologies are expected to be highly demanded since the interest in artificial intelligence and natural language processing arises in the medical domain. Our study aimed to assess the agreement between experts who...

The Classification of Short Scientific Texts Using Pretrained BERT Model.

Studies in health technology and informatics
Automated text classification is a natural language processing (NLP) technology that could significantly facilitate scientific literature selection. A specific topical dataset of 630 article abstracts was obtained from the PubMed database. We propose...

Clinical Relevance of Pharmacist Intervention: Development of a Named Entity Recognition Model on Unstructured Comments.

Studies in health technology and informatics
We developed a clinical named entity recognition model to predict clinical relevance of pharmacist interventions (PIs) by identifying and labelling expressions from unstructured comments of PIs. Three labels, drug, kidney and dosage, had a great inte...

First Steps to Evaluate an NLP Tool's Medication Extraction Accuracy from Discharge Letters.

Studies in health technology and informatics
INTRODUCTION: The aim of this study is to evaluate the use of a natural language processing (NLP) software to extract medication statements from unstructured medical discharge letters.

Enriching contextualized language model from knowledge graph for biomedical information extraction.

Briefings in bioinformatics
Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have la...