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We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A gradient boosted decision model utilizing cross-sectional survey data from 360 EBC patients was created. Seventeen patient- and treatment-specific variables were considered in the model. The outcome variable was based on the Hot Flush Night Sweats (HFNS) Problem Rating Score, and individual scores were dichotomized around the median to indicate individuals with high and low problem scores. Model accuracy was assessed using the area under the receiver operating curve, and conditional partial dependence plots were constructed to illustrate relationships between variables and the outcome of interest.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The model area under the ROC curve was 0.731 (SD 0.074). The most important variables in the model were as follows: the number of hot flashes per week, age, the prescription, or use of drug interventions to manage VMS, whether patients were asked about VMS in routine follow-up visits, and the presence or absence of changes to breast cancer treatments due to VMS. A threshold of 17 hot flashes per week was identified as being more predictive of severe VMS. Patients between the ages of 49 and 63 were more likely to report severe symptoms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Machine learning is a unique tool for predicting severe VMS. The use of ML to assess other treatment-related toxicities and their management requires further study.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00520-022-07156-6","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T19:02:33Z","timestamp":1653505353000},"page":"7397-7406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using machine learning to predict individual patient toxicities from cancer treatments"],"prefix":"10.1007","volume":"30","author":[{"given":"Katherine Marie","family":"Cole","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Clemons","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-6372","authenticated-orcid":false,"given":"Sharon","family":"McGee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mashari","family":"Alzahrani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gail","family":"Larocque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fiona","family":"MacDonald","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelle","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory R.","family":"Pond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucy","family":"Mosquera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa","family":"Vandermeer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"Hutton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ardelle","family":"Piper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Fernandes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khaled El","family":"Emam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,25]]},"reference":[{"key":"7156_CR1","doi-asserted-by":"publisher","first-page":"237428951987308","DOI":"10.1177\/2374289519873088","volume":"6","author":"HH Rashidi","year":"2019","unstructured":"Rashidi HH, Tran NK, Betts EV, Howell LP, Green R (2019) Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. 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This study was approved by the Ontario Cancer Research Ethics Board (OCREB).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statement of human rights"}},{"value":"This article does not contain any studies with animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statement on the welfare of animals"}},{"value":"Participation and completion of the survey implied consent to participate in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Participation and completion of the survey implied consent to publication of aggregate survey findings.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"SMG reports receipt of honorarium from Novartis for insights on management of breast cancer patients. BH and MC report consulting fees from Cornerstone Research, outside the submitted works. All other authors declare no competing interests. KEE is a co-founder of and SVP at Replica Analytics, a spin-off company from the University of Ottawa that develops synthetic data generation software. LM is an employee of Replica Analytics. GRP reports consulting fees from Merck and Profound Medical, honorarium for DSMB membership from Takeda and has a close family member who works for Roche Canada and owns stock in Roche, all outside the submitted works.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}