{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(1)"],"submitter":["Mark E"],"funding":["Carlos and Marguerite Mason Trust","Georgia Tech benefactors: William W. George, Andrea Laliberte, Claudia L. and J. Paul Raines, and Richard E. “Rick” and Charlene Zalesky","Laura and John Arnold Foundation"],"pubmed_abstract":["With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant funct"],"journal":["Scientific reports"],"pagination":["6164"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10105757"],"repository":["biostudies-literature"],"pubmed_title":["Predicting a kidney transplant patient's pre-transplant functional status based on information from waitlist registration."],"pmcid":["PMC10105757"],"pubmed_authors":["Sokol J","Mark E","Keskinocak P","Goldsman D","Gurbaxani B"],"additional_accession":[]},"is_claimable":false,"name":"Predicting a kidney transplant patient's pre-transplant functional status based on information from waitlist registration.","description":"With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant funct","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2025-04-04T20:46:10.678Z","creation":"2025-04-04T20:46:10.678Z"},"accession":"S-EPMC10105757","cross_references":{"pubmed":["37061525"],"doi":["10.1038/s41598-023-33117-y"]}}