Can Natural Language Processing Improve Adnexal Torsion Predictions?

Abstract

To create a decision support tool based on machine learning algorithms and natural language processing (NLP) technology, to augment clinicians' ability to predict cases of suspected adnexal torsion. Retrospective cohort study SETTING: Gynecology department, university-affiliated teaching medical center, 2014-2022. This study assessed risk-factors for adnexal torsion among women managed surgically for suspected adnexal torsion based on clinical and sonographic data. None. The dataset included demographic, clinical, sonographic, and surgical information obtained from electronic medical records. NLP was used to extract insights from unstructured free text and unlock them for automated reasoning. The machine learning model was a CatBoost classifier that utilizes gradient boosting on decision trees. The study cohort included 433 women who met inclusion criteria and underwent laparoscopy. Among them, 320 (74%) had adnexal torsion diagnosed during laparoscopy, and 113 (26%) did not. The model developed improved prediction of adnexal torsion to 84%, with a recall of 95%. The model ranked several parameters as important for prediction. Age, difference in size between ovaries, and the size of each ovary were the most significant. The precision for the "no torsion" class was 77%, with a recall of 45%. Using machine learning algorithms and NLP technology as a decision-support tool for the diagnosis of adnexal torsion is feasible. It improved true prediction of adnexal torsion to 84% and decreased cases of unnecessary laparoscopy.