https://www.selleckchem.com/products/a-83-01.html
Post-discharge oncologic surgical complications are costly for patients, families, and healthcare systems. The capacity to predict complications and early intervention can improve postoperative outcomes. In this proof-of-concept study, we used a machine learning approach to explore the potential added value of patient-reported outcomes (PROs) and patient-generated health data (PGHD) in predicting post-discharge complications for gastrointestinal (GI) and lung cancer surgery patients. We formulated post-discharge complication prediction
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