AIMC Topic: Patient Reported Outcome Measures
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Machine learning-based automated classification of headache disorders using patient-reported questionnaires.
Scientific reports
Aug 20, 2020
Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the fe...
Digital health technologies: opportunities and challenges in rheumatology.
Nature reviews. Rheumatology
Jul 24, 2020
The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learn...
The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning.
Scientific reports
Jul 1, 2020
The primary goal of this study was to evaluate the major roles of health-related quality of life (HRQOL) in a 5-year lung cancer survival prediction model using machine learning techniques (MLTs). The predictive performances of the models were compar...
Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.
Epilepsia
May 20, 2020
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.
Journal of psychosomatic research
Apr 25, 2020
OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population. We utilized machine-learning to identify patient-reported outcome measures (PROMs) that accurately ...
The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Oct 28, 2019
Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsin...
From protocolized to person-centered chronic care in general practice: study protocol of an action-based research project (COPILOT).
Primary health care research & development
Sep 24, 2019
AIM: To develop a proactive person-centered care approach for persons with (multiple) chronic diseases in general practice, and to explore the impact on 'Quadruple aims': experiences of patients and professionals, patient outcomes and costs of resour...
Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial.
The Journal of arthroplasty
Jul 24, 2019
BACKGROUND: Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PG...
Postoperative Period
Arthroplasty, Replacement, Hip
Osteoarthritis, Knee
Osteoarthritis, Hip
Pilot Projects
Wearable Electronic Devices
Signal Processing, Computer-Assisted
Knee Joint
Outcome Assessment, Health Care
Range of Motion, Articular
Arthroplasty, Replacement, Knee
Patient Reported Outcome Measures
Monitoring, Ambulatory
Prospective Studies
Female
Aged
Male
Middle Aged
Humans
Machine Learning
Algorithms
Robot for health data acquisition among older adults: a pilot randomised controlled cross-over trial.
BMJ quality & safety
Mar 20, 2019
BACKGROUND /OBJECTIVES: Healthcare professionals (HCP) are confronted with an increased demand for assessments of important health status measures, such as patient-reported outcome measurements (PROM), and the time this requires. The aim of this stud...
Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.
BMC medical informatics and decision making
Jan 8, 2019
BACKGROUND: Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient...
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