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Review

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

1
Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
2
Department of Public Health and Nursing, Norwegian University of Science and Technology, 7034 Trondheim, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Kamran Sedig
Informatics 2021, 8(3), 56; https://doi.org/10.3390/informatics8030056
Received: 30 June 2021 / Revised: 18 August 2021 / Accepted: 19 August 2021 / Published: 25 August 2021
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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. View Full-Text
Keywords: machine learning; patient-reported outcome measurements; self-reported measures; patient outcomes; outcome prediction; clinical decision making; decision-support systems; health informatics machine learning; patient-reported outcome measurements; self-reported measures; patient outcomes; outcome prediction; clinical decision making; decision-support systems; health informatics
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MDPI and ACS Style

Verma, D.; Bach, K.; Mork, P.J. Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review. Informatics 2021, 8, 56. https://doi.org/10.3390/informatics8030056

AMA Style

Verma D, Bach K, Mork PJ. Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review. Informatics. 2021; 8(3):56. https://doi.org/10.3390/informatics8030056

Chicago/Turabian Style

Verma, Deepika, Kerstin Bach, and Paul Jarle Mork. 2021. "Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review" Informatics 8, no. 3: 56. https://doi.org/10.3390/informatics8030056

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