{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:20:36Z","timestamp":1777652436769,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682648","type":"print"},{"value":"9781643682655","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Machine learning algorithms that derive predictive models are useful in predicting patient outcomes under uncertainty. These are often \u201cpopulation\u201d 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.<\/jats:p>","DOI":"10.3233\/shti220072","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:31:12Z","timestamp":1654594272000},"source":"Crossref","is-referenced-by-count":4,"title":["A Novel Personalized Random Forest Algorithm for Clinical Outcome Prediction"],"prefix":"10.3233","author":[{"given":"Adriana","family":"Johnson","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory F.","family":"Cooper","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shyam","family":"Visweswaran","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:31:13Z","timestamp":1654594273000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"ISBN":["9781643682648","9781643682655"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220072","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}