Bmc Med Inform Decis
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Bmc Med Inform Decis · Dec 2020
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. ⋯ UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.
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Bmc Med Inform Decis · Dec 2020
Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.
The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI). ⋯ Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.
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Bmc Med Inform Decis · Dec 2020
Shared decision making, physicians' explanations, and treatment satisfaction: a cross-sectional survey of prostate cancer patients.
Hormone therapy is one option for some types of prostate cancer. Shared decision making (SDM) is important in the decision making process, but SDM between prostate cancer patients receiving hormone therapy and physicians is not fully understood. This study tested hypotheses: "Patients' perception of SDM is associated with treatment satisfaction, mediated by satisfaction with physicians' explanations and perceived effective decision making" and "The amount of information provided to patients by physicians on diseases and treatment is associated with treatment satisfaction mediated by patients' perceived SDM and satisfaction with physicians' explanations." ⋯ When physicians encourage patients to be actively involved in making decisions about treatment through the SDM process while presenting a wide range of information at the start of hormone therapy, patients' effective decision making and physicians' explanations may be improved; consequently, the patients' overall treatment satisfaction may be improved. Physicians who treat patients with prostate cancer may have underestimated the importance of SDM before starting hormone therapy, even greater extent than patients.