Journal of pain and symptom management
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J Pain Symptom Manage · Jun 2018
Multicenter StudyMachine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.
Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. ⋯ We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications.
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J Pain Symptom Manage · Jun 2018
Multicenter Study Observational StudyValidation of the Edmonton Symptom Assessment System: Ascites Modification.
Few patient-reported outcomes are available to measure the symptoms associated with malignant-related ascites in patient care and clinical research. Although the Edmonton Symptom Assessment System: Ascites Modification (ESAS:AM) is a brief tool to measure symptoms associated with malignant-related ascites, it remains to be fully validated. ⋯ The ESAS:AM is a reliable and valid tool for measuring symptoms associated with malignant-related ascites and can be used in daily patient care and future epidemiological studies and clinical trials.