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Natural Language Processing

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Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.

BMC medical research methodology
BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing ...

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study.

JMIR medical informatics
BACKGROUND: Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial man...

Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease.

JMIR formative research
BACKGROUND: Ischemic heart disease is a leading cause of death globally with a disproportionate burden in low- and middle-income countries (LMICs). Natural language processing (NLP) allows for data enrichment in large datasets to facilitate key clini...

Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Breast cancer relapses are rarely collected by cancer registries because of logistical and financial constraints. Hence, we investigated natural language processing (NLP), enhanced with state-of-the-art deep learning transformer tools and la...

Clinical and research applications of natural language processing for heart failure.

Heart failure reviews
Natural language processing (NLP) is a burgeoning field of machine learning/artificial intelligence that focuses on the computational processing of human language. Researchers and clinicians are using NLP methods to advance the field of medicine in g...

OptimCLM: Optimizing clinical language models for predicting patient outcomes via knowledge distillation, pruning and quantization.

International journal of medical informatics
BACKGROUND: Clinical Language Models (CLMs) possess the potential to reform traditional healthcare systems by aiding in clinical decision making and optimal resource utilization. They can enhance patient outcomes and help healthcare management throug...

How can language models assist with pharmaceuticals manufacturing deviations and investigations?

International journal of pharmaceutics
Large Language Models (LLM) such as the Generative-Pretrained-Transformer (GPT) and Large-Language-Model-Meta-AI (LLaMA) have attracted much attention. There is strong evidence that these models perform remarkably well in various natural language pro...

Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.

BMC emergency medicine
BACKGROUND: Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study de...

Uncertainty-aware automatic TNM staging classification for [F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.

BMC medical informatics and decision making
BACKGROUND: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised ...

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.

JMIR medical informatics
BACKGROUND: Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases do not have specific International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Editio...