Clinical relevance of deep learning models in predicting the onset timing of breakthrough cancer pain

Abstract Introduction Breakthrough cancer pain (BTcP) is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of deep learning models that predict the of BTcP onset in hospitalized patients. Methods We defined BTcP as the pain with a numerical rating scale (NRS) score of ≥ 4. We investigated the performance of the deep learning models using the Matthews correlation coefficient (MCC) with different input lengths and time binning. All the pain records were obtained from the electronic medical records of the hematology-oncology wards in a Samsung Medical Center between July 2016 and February 2020. The model was externally validated using the holdout method with 20% of the datasets. Results The most common type of cancer was lung cancer (n = 745, 21.7%), and the median BTcP per day was 1.01. The NRS pain records showed circadian patterns that correlated with NRS pain patterns of the previous days. The correlation of the NRS scores showed a positive association with the closeness of the NRS pattern of the day with forecast date and size of time binning. The long short-term memory-based model exhibited a good performance by demonstrating 9 times the best performance and 8 times the second-best performance among 21 different settings. The best performance was achieved with 120 h input and 12 h bin lengths (MCC: 0.4927). Conclusion Our study demonstrated the possibility of predicting BTcP using deep learning models, thereby suggesting that preemptive cancer pain management using deep learning will improve patients’ daily life.