75P Electronic patient-reported outcomes and machine learning in predicting unplanned visits and hospitalisation rates in cancer patients undergoing systemic therapy

Abstract

Electronic patient-reported outcomes (ePRO) have shown the potential to improve clinical practice for cancer patients. The collection of ePRO via smartphone apps is becoming more wide-spread. Such data, comprising of symptom and diary entries, could conceivably be utilised to predict the occurrence of unplanned hospitalisation events via machine learning algorithms. 226 cancer patients (mainly breast cancer) using the consilium care ePRO app under systemic therapy. A diary entry is associated with an “unplanned visit” flag if such a visit or a hospitalization of the patient occurred on or within 3 days. We assume that on these days, an alarm raised to the patient or her/his physician would have been helpful. Out of 16,670 entries, only 166 (1%) were flagged. To implement an early warning system, we trained a rule-based ML algorithm to predict critical situations, which we made more sensitive to the few flagged entries by applying cost-sensitive classification, assigning a 10-fold higher cost to missing a critical situation as compared to a false alarm. Rules are using are based on the strengths of symptoms captured by the patients, their perceived wellbeing, drugs they were taking, as well as keywords derived from free-text notes of patients and from their diagnosis; e.g. one of our rules suggests that an unplanned visit is likely to occur if a patient reports pain on a level of 38 or more and a wellbeing of 60 or less (both out of 100) and uses the word “today” in his/her notes of the given day. We intentionally used algorithms that are potentially inferior in prediction quality to modern deep learning approaches, but have the advantage of being not only human-interpretable, but even directly “human-modifiable” so that we can incorporate medical expertise into the machine-learned model at a later stage. We discovered a set of 55 rules which, as a whole, were able to correctly predict 47 of the 166 critical situations (28.3% recall), while raising 267 false alarms (15.2% precision). Preliminary analysis of this study suggests that ML based prediction models trained on ePRO data can predict unplanned hospitalisation events within cancer patients.