In 2018, Butler appeared on the second season of the teen drama series 13 Reasons Why, where he played the role of Scott Reed.īutler was cast to play the main role of Brady Finch in the drama series Trinkets opposite Brianna Hildebrand, Kiana Madeira, Quintessa Swindell and Odiseas Georgiadis, based on the 2013 novel of the same name by Kirsten Smith. īutler appeared in the daytime soap opera General Hospital, where he played the role as Frat guy #1. īutler was cast to play the role of Peter in the 2014 drama film Spoilers: The Movie directed by Connor Williams. At the age of 13, Butler began his acting career in 2009, when he appeared in the 2011 musical comedy film Sister Mary, portraying Choir boy. Life and career īutler was born and raised in Frankfort, Illinois. He is known for portraying Scott Reed in the Netflix series 13 Reasons Why and Brady Finch in the Netflix series Trinkets. A data-driven tool that estimates the probability of 90-day mortality could be leveraged as a powerful supplementary aid to clinicians managing end-of-life care at oncology practices.Brandon Butler (born September 11, 1996) is an American actor. Conclusions: This study builds upon previous work and further establishes the utility of machine learning to predict risk of imminent mortality for advanced cancer patients using available EHR data. Further, external validation conducted using 3 independent holdout datasets demonstrated impressive generalizability marked by stable performance scores across multiple time periods (AUC between 0.84 and 0.85). The performance on the training cohort was given by a cross-validated AUC score of 0.85 (95% CI, 0.84 to 0.86). A logistic regression algorithm using L1 (lasso) regularization yielded the best performance compared to other model candidates. Results: A multivariable model to predict 90-day mortality was developed using a retrospective dataset derived from EHR data and Medicare claims data. To avoid bias, all holdout datasets used for validation were excluded from the model. As external validation, the final model was independently tested on 3 separate holdout datasets including OCM patients between Jand March 31, 2020. The training dataset was also used for internal validation and hyperparameter tuning until the final model was produced. The patients satisfying the selection criterion were used to train and optimize the model. Patients were excluded from the study cohort if they were not enrolled in the OCM program or did not have a diagnosis for metastatic cancer. Patients were required to have at least one record for lab values and vital signs in the EHR database. Methods: A retrospective study cohort was formed using patients with metastatic cancer from US Oncology Network (USON) practices participating in the Oncology Care Model (OCM) between Januand June 30, 2019. An automated algorithmic tool that can incorporate the wealth of available EHR data and rapidly identify patients with a high risk of imminent mortality could be a valuable asset to supplement important clinical decisions and improve timely hospice care. In particular, timely hospice enrollment is a leading quality metric in the Oncology Care Model that has substantial room for improvement. Background: End-of-life management is a well-known challenging aspect of cancer care.
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