AIMC Topic: Natural Language Processing

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Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.

Nucleic acids research
High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods ...

Lactylation prediction models based on protein sequence and structural feature fusion.

Briefings in bioinformatics
Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread ...

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the ...

Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings.

The British journal of surgery
BACKGROUND: Cancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural lang...

Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Annotated language resources are essential for supervised machine learning methods. In the clinical domain, such data sets can boost use-case specific natural language processing services. In this work, we have analyzed a clinical problem list table ...

Large Language Models Struggle in Token-Level Clinical Named Entity Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large Language Models (LLMs) have revolutionized various sectors, including healthcare where they are employed in diverse applications. Their utility is particularly significant in the context of rare diseases, where data scarcity, complexity, and sp...

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automate...

Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication names, routes, and frequencies from publicly available free-text ophthalmology progress notes of 480 ...

Development of a Flexible Chain of Thought Framework for Automated Routing of Patient Portal Messages.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The increase in utilization of patient portal messages has imposed a considerable burden on healthcare providers, contributing to an increased incidence of provider burnout. This study introduces a framework for leveraging Large Language Models (LLMs...

Large-scale Text Mining of Suicide Attempt improves Identification of Distinct Suicidal Events in Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In this study, we explore a natural language processing (NLP) algorithm's capacity to identify proximal but distinct suicide attempt (SA) events compared to diagnostic code-based approaches. This study used an NLP algorithm with high precision in ide...