Keyvan Sasani1, Helen N Catanese1, Alireza Ghods1, Seyed Ali Rokni1, Hassan Ghasemzadeh1, Robert J Downey2, Armin Shahrokni3. 1. School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA. 2. Department of Surgery, Memorial Sloan Kettering Cancer Center, NY, NY 10065, USA. 3. Department of Medicine/Geriatrics, Memorial Sloan Kettering Cancer Center, NY, NY 10065, USA. Electronic address: shahroka@mskcc.org.
Abstract
PURPOSE: Gait speed in older patients with cancer is associated with mortality risk. One approach to assess gait speed is with the 'Timed Up and Go' (TUG) test. We utilized machine learning algorithms to automatically predict the results of the TUG tests and its association with survival, using patient-generated responses. METHODS: A decision tree classifier was trained based on functional status data, obtained from preoperative geriatric assessment, and TUG test performance of older patients with cancer. The functional status data were used as input features to the decision tree, and the actual TUG data was used as ground truth labels. The decision tree was constructed to assign each patient to one of three categories: "TUG < 10 s", "TUG ≥ 10 s", and "uncertain." RESULTS: In total, 1901 patients (49% women) with a mean age of 80 years were assessed. The most commonly performed operations were urologic, colorectal, and head and neck. The machine learning algorithm identified three features (cane/walker use, ability to walk outside, and ability to perform housework), in predicting TUG results with the decision tree classifier. The overall accuracy, specificity, and sensitivity of the prediction were 78%, 90%, and 66%, respectively. Furthermore, survival rates in each predicted TUG category differed by approximately 1% from the survival rates obtained by categorizing the patients based on their actual TUG results. CONCLUSIONS: Machine learning algorithms can accurately predict the gait speed of older patients with cancer, based on their response to questions addressing other aspects of functional status.
PURPOSE: Gait speed in older patients with cancer is associated with mortality risk. One approach to assess gait speed is with the 'Timed Up and Go' (TUG) test. We utilized machine learning algorithms to automatically predict the results of the TUG tests and its association with survival, using patient-generated responses. METHODS: A decision tree classifier was trained based on functional status data, obtained from preoperative geriatric assessment, and TUG test performance of older patients with cancer. The functional status data were used as input features to the decision tree, and the actual TUG data was used as ground truth labels. The decision tree was constructed to assign each patient to one of three categories: "TUG < 10 s", "TUG ≥ 10 s", and "uncertain." RESULTS: In total, 1901 patients (49% women) with a mean age of 80 years were assessed. The most commonly performed operations were urologic, colorectal, and head and neck. The machine learning algorithm identified three features (cane/walker use, ability to walk outside, and ability to perform housework), in predicting TUG results with the decision tree classifier. The overall accuracy, specificity, and sensitivity of the prediction were 78%, 90%, and 66%, respectively. Furthermore, survival rates in each predicted TUG category differed by approximately 1% from the survival rates obtained by categorizing the patients based on their actual TUG results. CONCLUSIONS: Machine learning algorithms can accurately predict the gait speed of older patients with cancer, based on their response to questions addressing other aspects of functional status.
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