Background: In various fields outside of medicine, AI-supported systems have been established that can predict an undesirable event. The purpose of such systems is to detect events earlier and, if necessary, to be able to prevent them. In medicine, it would be particularly interesting to be able to make such predictions based solely on patient observations. Methods: The usage from 323 patients with advanced breast cancer with a total of 78542 documentation days was used. In addition, the premature termination of use was defined as an undesirable event. The data was then processed and annotated. A deep-learning neural network (NN) classifier was trained on this dataset independently on all documented days to predict this target endpoint. The patient classifier score was computed by averaging over daily scores. Overall classifier accuracy and binary cross entropy loss were computed as performance indicators on training and test data sets (2:1 split). Results: After tuning the hyperparameters, the best-performing NN comprised three hidden layers, each with 88 neurons, using ReLU (linear ramp) activation, and an output layer using sigmoid activation. In the test collective, this model achieved a prediction accuracy of 87%. Discussion: The present application shows for the first time that treatment discontinuation can be predicted with a very high degree of accuracy using patient data alone. This opens up new possibilities in the early detection of possible therapy failures and can represent an essential auxiliary tool in medical care in the future.

Citation Format: Timo Schinköthe, Ronald Kates, Benedikt Sprecher, Silja Meyer, Christian Horst Tonk, Nadia Harbeck, Annette Schmidt. Trained Artificial Intelligence (AI) for Predicting Treatment Termination Based on Patient Observations in Advanced Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-03-08.