AIMC Topic: Patient Reported Outcome Measures
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Guiding principles for the use of knowledge bases and real-world data in clinical decision support systems: report by an international expert workshop at Karolinska Institutet.
Expert review of clinical pharmacology
Sep 29, 2020
INTRODUCTION: Technical and logical breakthroughs have provided new opportunities in medicine to use knowledge bases and large-scale clinical data (real-world) at point-of-care as part of a learning healthcare system to diminish the knowledge-practic...
Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.
Journal of surgical oncology
Sep 24, 2020
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We wil...
Patient reported outcome measures concerning urinary incontinence after robot assisted radical prostatectomy: development and validation of an online prediction model using clinical parameters, lower urinary tract symptoms and surgical experience.
Journal of robotic surgery
Sep 15, 2020
The prediction of post-prostatectomy incontinence (PPI) after robot-assisted radical prostatectomy (RARP) depends on multiple clinical, anatomical and surgical factors. There are only few risk formulas, tables or nomograms predicting PPI that may ass...
Radiographic Indices Are Not Predictive of Clinical Outcomes Among 1735 Patients Indicated for Hip Arthroscopic Surgery: A Machine Learning Analysis.
The American journal of sports medicine
Sep 14, 2020
BACKGROUND: The relationship between the preoperative radiographic indices for femoroacetabular impingement syndrome (FAIS) and postoperative patient-reported outcome measure (PROM) scores continues to be under investigation, with inconsistent findin...
Quality of Care Perceived by Older Patients and Caregivers in Integrated Care Pathways With Interviewing Assistance From a Social Robot: Noninferiority Randomized Controlled Trial.
Journal of medical Internet research
Sep 9, 2020
BACKGROUND: Society is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem.
Clinical and Research Medical Applications of Artificial Intelligence.
Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Aug 21, 2020
Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned fr...
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.
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