AIMC Topic: Patient Reported Outcome Measures
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Can a Large Language Model Interpret Data in the Electronic Health Record to Infer Minimum Clinically Important Difference Achievement of Knee Osteoarthritis Outcome Score-Joint Replacement Score Following Total Knee Arthroplasty?
The Journal of arthroplasty
Jul 1, 2025
BACKGROUND: Obtaining total knee arthroplasty patient-reported outcomes for quality assessment is costly and difficult. We asked whether a large language model (LLM) could interpret electronic health record notes to differentiate patients attaining a...
Development of a patient reported outcomes based machine learning model to predict recurrences in head and neck cancer.
Oral oncology
Jun 1, 2025
INTRODUCTION: Recurrence rates among Head and Neck Cancer (HNC) patients are high, with earlier detection associated with improved survival. Patient-reported outcomes (PROs) have increasingly been found to predict patient care needs. Here, we examine...
Patient reported outcome measures: from the classics to AI.
The Journal of hand surgery, European volume
Jun 1, 2025
With its basis in the development of intelligence testing, classical test theory paved the way to develop patient-reported outcome measures - tools capable of quantifying otherwise immeasurable traits. In hand surgery, many of the popular outcome mea...
Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.
Studies in health technology and informatics
May 15, 2025
Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML) models identifying patients at risk of emergency admissions are often developed on data lacking pa...
Stakeholders' Perspectives on Medication Adherence Enhancing Interventions.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
May 1, 2025
With an approximately 50% prevalence rate, medication nonadherence is a significant healthcare challenge that increases the risk of potentially avoidable adverse events and associated costs ranging from $949 to $44 190 per person annually. The ISPOR ...
Exploring the Potential of Non-Proprietary Language Models for Analysing Patient-Reported Experiences.
Studies in health technology and informatics
Apr 24, 2025
Large language models (LLMs) are increasingly being explored for various applications in medical language processing. Due to data privacy issues, it is recommended to apply non-proprietary models that can be run locally. Therefore, this study aims to...
Outcome measures in chronic urticaria: A comprehensive review.
Indian journal of dermatology, venereology and leprology
Jan 1, 2025
Chronic urticaria, characterised by pruritic wheals, angioedema or both significantly impacts individuals' quality of life. This review article examines the patient-reported outcome measures (PROMs) in chronic urticaria assessment, aiming to enhance ...
Using voice recognition and machine learning techniques for detecting patient-reported outcomes from conversational voice in palliative care patients.
Japan journal of nursing science : JJNS
Jan 1, 2025
AIM: Patient-reported outcome measures (PROMs) are increasingly used in palliative care to evaluate patients' symptoms and conditions. Healthcare providers often collect PROMs through conversations. However, the manual entry of these data into electr...
Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning.
Studies in health technology and informatics
Nov 22, 2024
Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover g...
Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer.
JCO clinical cancer informatics
Aug 1, 2024
PURPOSE: Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most...
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