Bang, Yeong Hak Choi, Yoon Ho Park, Mincheol Shin, Soo-Yong Kim, Seok Jin 2024-01-19T09:03:51Z 2024-01-19T09:03:51Z 2023-08-24 2023-07 2045-2322 https://pubs.kist.re.kr/handle/201004/113490 Cancer pain 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 onset of cancer pain exacerbation in hospitalized patients. We defined cancer pain exacerbation (CPE) as the pain with a numerical rating scale (NRS) score of & GE; 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. The most common type of cancer was lung cancer (n = 745, 21.7%), and the median CPE 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). Our study demonstrated the possibility of predicting CPE using deep learning models, thereby suggesting that preemptive cancer pain management using deep learning could potentially improve patients' daily life. English Nature Publishing Group Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation Article 10.1038/s41598-023-37742-5 1 Scientific Reports, v.13, no.1 Scientific Reports 13 1 Y scie scopus 001032784200060 2-s2.0-85165059353 Multidisciplinary Sciences Science & Technology - Other Topics Article BREAKTHROUGH PAIN MANAGEMENT RECOMMENDATIONS ASSOCIATION PREVALENCE