NDLI: Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review

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Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review

Content Provider	MDPI
Author	Verma, Deepika Bach, Kerstin Mork, Paul Jarle
Copyright Year	2021
Description	The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with the aim of summarising the existing methodologies that apply machine learning methods on patient-reported outcome measures datasets for predicting clinical outcomes to support further research and development within the field. We identify 15 articles published within the last decade that employ machine learning methods at various stages of exploiting datasets consisting of patient-reported outcome measures for predicting clinical outcomes, presenting promising research and demonstrating the utility of patient-reported outcome measures data for developmental research, personalised treatment and precision medicine with the help of machine learning-based decision-support systems. Furthermore, we identify and discuss the gaps and challenges, such as inconsistency in reporting the results across different articles, use of different evaluation metrics, legal aspects of using the data, and data unavailability, among others, which can potentially be addressed in future studies.
Starting Page	56
e-ISSN	22279709
DOI	10.3390/informatics8030056
Journal	Informatics
Issue Number	3
Volume Number	8
Language	English
Publisher	MDPI
Publisher Date	2021-08-25
Access Restriction	Open
Subject Keyword	Informatics Machine Learning Patient-reported Outcome Measurements Self-reported Measures Patient Outcomes Outcome Prediction Clinical Decision Making Decision-support Systems Health Informatics
Content Type	Text
Resource Type	Article

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