AIMC Topic: Natural Language Processing

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Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

Science progress
INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. From this perspective, it is noteworthy that formal thought disorder has historically been considered p...

Comparative Evaluation of Chatbot Responses on Coronary Artery Disease.

Turk Kardiyoloji Dernegi arsivi : Turk Kardiyoloji Derneginin yayin organidir
OBJECTIVE: Coronary artery disease (CAD) is the leading cause of morbidity and mortality globally. The growing interest in natural language processing chatbots (NLPCs) has driven their inevitable widespread adoption in healthcare. The purpose of this...

Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China.

The Lancet. Digital health
BACKGROUND: Perimembranous ventricular septal defect (PMVSD) is a prevalent congenital heart disease, presenting challenges in predicting spontaneous closure, which is crucial for therapeutic decisions. Existing models mainly rely on structured echoc...

Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.

Hospital pediatrics
OBJECTIVE: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements fr...

Leveraging Foundational Models in Computational Biology: Validation, Understanding, and Innovation.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Large Language Models (LLMs) have shown significant promise across a wide array of fields, including biomedical research, but face notable limitations in their current applications. While they offer a new paradigm for data analysis and hypothesis gen...

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured elec...

Automated Evaluation of Antibiotic Prescribing Guideline Concordance in Pediatric Sinusitis Clinical Notes.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescrip...

Artificial Allies: Validation of Synthetic Text for Peer Support Tools through Data Augmentation in NLP Model Development.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
This study investigates the potential of using synthetic text to augment training data for Natural Language Processing (NLP) models, specifically within the context of peer support tools. We surveyed 22 participants-13 professional peer supporters an...

Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.

Methods in molecular biology (Clifton, N.J.)
The elucidation of protein structure and function plays a pivotal role in understanding biological processes and facilitating drug discovery. With the exponential growth of protein sequence data, machine learning techniques have emerged as powerful t...

Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.