Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.
Background: Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.Methods: This systematic lite...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 20 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
2/15/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148718890&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148718890 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2/15/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 148718890 148718890 NLM33588830 10.1186/s12911-021-01403-2 NLM33588830 148718890 ppf: 1 ppct: 19 formats: tig: atl: Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. aug: au: Brnabic, Alan Hess, Lisa M. affil: Eli Lilly and Company, Sydney, NSW, Australia sug: subj: Algorithms Decision Making Resource Databases Study Design Scales Ferrans and Powers Quality of Life Index ab: Background: Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.Methods: This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist.Results: A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies.Conclusions: A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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