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Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning
In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities.
A Literature Review and Framework for Human Evaluation of Generative Large Language Models in Healthcare
This review provides a comprehensive overview of the human evaluation approaches used in diverse healthcare applications. This analysis examines the human evaluation of LLMs across various medical specialties, addressing factors such as evaluation dimensions, sample types, and sizes, the selection and recruitment of evaluators, frameworks and metrics, the evaluation process, and statistical analysis of the results.
Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning
We constructed a knowledge graph of 81, 488 unique TF cascades, with the longest cascade consisting of 62 TFs.
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative AI, and we hope that it will inspire and inform future research in this area.
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records
Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features.
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study
Objective: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of post-stroke patients treated at the University of Pittsburgh Medical Center.
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks
Working with smaller annotated datasets is typical in clinical NLP and therefore, ensuring that deep learning models perform well is crucial for the models to be used in real-world applications.
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing
We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts.
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing
The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts.
