Background and objectives: 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.
Methods: We formulated post-discharge complication prediction as a binary classification task. Features were extracted from clinical variables, PROs (MD Anderson Symptom Inventory [MDASI]), and PGHD (VivoFit) from a cohort of 52 patients with 134 temporal observation points pre- and post-discharge that were collected from two pilot studies. We trained and evaluated supervised learning classifiers via nested cross-validation.
Results: A logistic regression model with L2 regularization trained with clinical data, PROs and PGHD from wearable pedometers achieved an area under the receiver operating characteristic of 0.74.
Conclusions: PROs and PGHDs captured through remote patient telemonitoring approaches have the potential to improve prediction performance for postoperative complications.
Keywords: machine learning; patient-generated health data; patient-reported outcomes; supervised learning; wearable computing.
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