Objective: To determine if natural language processing (NLP) with machine learning of unstructured full text documents (a preoperative CT scan) improves the ability to predict postoperative complication and hospital readmission among women with ovarian cancer undergoing surgery when compared with discrete data predictors alone.
Methods: Medical records from two institutions were queried to identify women with ovarian cancer and available preoperative CT scan reports who underwent debulking surgery. Machine learning methods using both discrete data predictors (age, comorbidities, preoperative laboratory values) and natural language processing of full text reports (preoperative CT scans) were used to predict postoperative complication and hospital readmission within 30 days of surgery. Discrimination was measured using the area under the receiver operating characteristic curve (AUC).
Results: We identified 291 women who underwent debulking surgery for ovarian cancer. Mean age was 59, mean preoperative CA125 value was 610 U/ml and albumin was 3.9 g/dl. There were 25 patients (8.6%) who were readmitted and 45 patients (15.5%) who developed postoperative complications within 30 days. Using discrete features alone, we were able to predict postoperative readmission with an AUC of 0.56 (0.54-0.58, 95% CI); this improved to 0.70 (0.68-0.73, 95% CI) (p < 0.001) with the addition of NLP of preoperative CT scans.
Conclusions: Natural language processing with machine learning improved the ability to predict postoperative complication and hospital readmission among women with ovarian cancer undergoing surgery.
Keywords: Hospital readmission; Machine learning; Natural language processing; Ovarian cancer; Postoperative complication.
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