Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review
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 m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/8/3/56 |
| _version_ | 1797518806401155072 |
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| author | Deepika Verma Kerstin Bach Paul Jarle Mork |
| author_facet | Deepika Verma Kerstin Bach Paul Jarle Mork |
| author_sort | Deepika Verma |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T07:34:42Z |
| format | Article |
| id | doaj.art-492464c879e84432a4803bc67cce1502 |
| institution | Directory Open Access Journal |
| issn | 2227-9709 |
| language | English |
| last_indexed | 2024-03-10T07:34:42Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj.art-492464c879e84432a4803bc67cce15022023-11-22T13:34:51ZengMDPI AGInformatics2227-97092021-08-01835610.3390/informatics8030056Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature ReviewDeepika Verma0Kerstin Bach1Paul Jarle Mork2Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, NorwayDepartment of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, NorwayDepartment of Public Health and Nursing, Norwegian University of Science and Technology, 7034 Trondheim, NorwayThe 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.https://www.mdpi.com/2227-9709/8/3/56machine learningpatient-reported outcome measurementsself-reported measurespatient outcomesoutcome predictionclinical decision making |
| spellingShingle | Deepika Verma Kerstin Bach Paul Jarle Mork Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review Informatics machine learning patient-reported outcome measurements self-reported measures patient outcomes outcome prediction clinical decision making |
| title | Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review |
| title_full | Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review |
| title_fullStr | Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review |
| title_full_unstemmed | Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review |
| title_short | Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review |
| title_sort | application of machine learning methods on patient reported outcome measurements for predicting outcomes a literature review |
| topic | machine learning patient-reported outcome measurements self-reported measures patient outcomes outcome prediction clinical decision making |
| url | https://www.mdpi.com/2227-9709/8/3/56 |
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