A Novel Personalized Random Forest Algorithm for Clinical Outcome Prediction.
Machine learning algorithms that derive predictive models are useful in predicting patient outcomes under uncertainty. These are often "population" algorithms which optimize a static model to predict well on average for individuals in the population; however, population models may predict poorly for...
| Published in: | Studies in Health Technology & Informatics Vol. 290; pp. 248 - 253 |
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| Main Authors: | , , |
| Format: | equations & formulas tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157571947&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157571947 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 290 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157571947 157571947 157571947 10.3233/SHTI220072 157571947 ppf: 248 ppct: 5 formats: tig: atl: A Novel Personalized Random Forest Algorithm for Clinical Outcome Prediction. aug: au: Johnson, Adriana Cooper, Gregory F. Visweswaran, Shyam affil: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America sug: subj: Random Forest Algorithms Treatment Outcomes Evaluation Machine Learning Decision Trees ab: Machine learning algorithms that derive predictive models are useful in predicting patient outcomes under uncertainty. These are often "population" algorithms which optimize a static model to predict well on average for individuals in the population; however, population models may predict poorly for individuals that differ from the average. Personalized machine learning algorithms seek to optimize predictive performance for every patient by tailoring a patient-specific model to each individual. Ensembles of decision trees often outperform single decision tree models, but ensembles of personalized models like decision paths have received little investigation. We present a novel personalized ensemble, called Lazy Random Forest (LazyRF), which consists of bagged randomized decision paths optimized for the individual for whom a prediction will be made. LazyRF outperformed single and bagged decision paths and demonstrated comparable predictive performance to a population random forest method in terms of discrimination on clinical and genomic data while also producing simpler models than the population random forest. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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