AIMC Topic: Patient Reported Outcome Measures

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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 ...
Humans Machine Learning Female Young Adult Adult Anxiety Disorders Cohort Studies Logistic Models Inflammation Patient Reported Outcome Measures Male Middle Aged Prevalence Depressive Disorder, Major
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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...
Outcome Assessment, Health Care Multiple Sclerosis Patient Reported Outcome Measures Proof of Concept Study Humans Machine Learning Prognosis Female Adult Male Middle Aged Disease Progression Severity of Illness Index
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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...
Health Services Research General Practice Chronic Disease Patient-Centered Care Netherlands Patient Reported Outcome Measures Humans Research Design Comorbidity Pilot Projects
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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...
Humans Machine Learning Algorithms Prospective Studies Pilot Projects Wearable Electronic Devices Signal Processing, Computer-Assisted Knee Joint Outcome Assessment, Health Care Female Aged Male Middle Aged Postoperative Period Arthroplasty, Replacement, Hip Osteoarthritis, Knee Osteoarthritis, Hip Range of Motion, Articular Arthroplasty, Replacement, Knee Patient Reported Outcome Measures Monitoring, Ambulatory
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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...
Time Cross-Over Studies Netherlands Patient Reported Outcome Measures Patient Satisfaction Humans Female Aged Aged, 80 and over Male Pilot Projects Surveys and Questionnaires Interviews as Topic Robotics
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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...
Supervised Machine Learning Arthroplasty, Replacement, Knee Patient Reported Outcome Measures Arthroplasty, Replacement, Hip Adult Male Middle Aged Models, Theoretical Humans Prognosis Female Aged Aged, 80 and over Young Adult
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Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling.

The spine journal : official journal of the North American Spine Society Nov 16, 2018
BACKGROUND CONTEXT: There is considerable variability in patient-reported outcome measures following surgery for lumbar disc herniation. Individualized prediction tools that are derived from center- or even surgeon-specific data could provide valuabl...
Adult Male Middle Aged Postoperative Complications Deep Learning Lumbar Vertebrae Feasibility Studies Patient Reported Outcome Measures Intervertebral Disc Degeneration Humans Female Intervertebral Disc Displacement Diskectomy
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Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms.

Journal of medical Internet research Nov 12, 2018
BACKGROUND: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor's skills, and bedside manners. Most previous work ...
Patient Reported Outcome Measures Quality Indicators, Health Care Attitude Algorithms Physicians Language Internet Review Literature as Topic Humans Machine Learning
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Robotic-assisted vs. open radical prostatectomy: A machine learning framework for intelligent analysis of patient-reported outcomes from online cancer support groups.

Urologic oncology Sep 17, 2018
BACKGROUND: The advantages of Robot-assisted laparoscopic prostatectomy (RARP) over open radical prostatectomy (ORP) in Prostate cancer perioperatively are well-established, but quality of life is more contentious. Increasingly, patients are utilisin...
Patient Reported Outcome Measures Prostatectomy Follow-Up Studies Prostatic Neoplasms Laparoscopy Robotic Surgical Procedures Aged Adult Male Middle Aged Quality of Life Humans Machine Learning Prognosis Retrospective Studies
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Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features.

Parkinsonism & related disorders May 5, 2018
INTRODUCTION: Technological solutions for quantifying Parkinson's disease (PD) symptoms may provide an objective means to track response to treatment, including side effects such as levodopa-induced dyskinesia. Vision-based systems are advantageous a...
Video Recording Image Interpretation, Computer-Assisted Antiparkinson Agents Levodopa Dyskinesia, Drug-Induced Severity of Illness Index Deep Learning Parkinson Disease Patient Reported Outcome Measures Humans Female Aged Sensitivity and Specificity Biomechanical Phenomena Male Middle Aged
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