Machine learning for clinical outcome prediction
Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality an...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2020
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| _version_ | 1797055410754027520 |
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| author | Shamout, F Zhu, T Clifton, DA |
| author_facet | Shamout, F Zhu, T Clifton, DA |
| author_sort | Shamout, F |
| collection | OXFORD |
| description | Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research. |
| first_indexed | 2024-03-06T19:10:19Z |
| format | Journal article |
| id | oxford-uuid:168f35a7-860b-455f-a899-74a25b99b2f9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:10:19Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:168f35a7-860b-455f-a899-74a25b99b2f92022-03-26T10:32:00ZMachine learning for clinical outcome predictionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:168f35a7-860b-455f-a899-74a25b99b2f9EnglishSymplectic ElementsInstitute of Electrical and Electronics Engineers2020Shamout, FZhu, TClifton, DAClinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research. |
| spellingShingle | Shamout, F Zhu, T Clifton, DA Machine learning for clinical outcome prediction |
| title | Machine learning for clinical outcome prediction |
| title_full | Machine learning for clinical outcome prediction |
| title_fullStr | Machine learning for clinical outcome prediction |
| title_full_unstemmed | Machine learning for clinical outcome prediction |
| title_short | Machine learning for clinical outcome prediction |
| title_sort | machine learning for clinical outcome prediction |
| work_keys_str_mv | AT shamoutf machinelearningforclinicaloutcomeprediction AT zhut machinelearningforclinicaloutcomeprediction AT cliftonda machinelearningforclinicaloutcomeprediction |