首页|University of Duisburg-Essen Reports Findings in Cancer (Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patie nts With Cancer)
University of Duisburg-Essen Reports Findings in Cancer (Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patie nts With Cancer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cancer is the subject of a report. According to news reporting originating from Essen, Germany, by New sRx correspondents, research stated, “Palliative care is recommended for patient s with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patient s with cancer and may help distinguish who benefits the most from palliative car e support.” Our news editors obtained a quote from the research from the University of Duisb urg-Essen, “We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality. Between April 1, 2020, and March 31, 2021 , a total of 265 patients with advanced cancer completed a patient-reported outc ome tool. We documented objective and subjective variables collected from electr onic health records, self-reported subjective variables, and all clinical variab les combined. We used logistic regression (LR), 20-fold cross-validation, decisi on trees, and random forests to predict 1-year mortality. We analyzed the receiv er operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR- AUC)-and the feature importance of the ML models. The performance of clinical no npatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predict ive than that of subjective patient-reported variables (LR reaches 0.55 [ROCAUC] and 0.52 [F1 score] ). The results show that objective variables used in this study are much more pr edictive than subjective patient-reported variables, which measure subjective bu rden. These findings indicate that subjective burden cannot be reliably used to predict survival.”
EssenGermanyEuropeCancerCyborgsEmerging TechnologiesHealth and MedicineMachine LearningOncologyPalliat ive and Supportive Care
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