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Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation. | LitMetric

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

Sci Rep

Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, Korea.

Published: July 2023

AI Article Synopsis

  • The study investigates the use of deep learning models to predict cancer pain exacerbation (CPE), defined as a pain score of 4 or higher on a numerical rating scale, in hospitalized patients.
  • Data was analyzed from electronic medical records at a medical center, with lung cancer being the most prevalent type, showing consistent daily pain patterns that could aid in predictions.
  • The best-performing model employed a long short-term memory approach, achieving a high accuracy score and suggesting that deep learning may enhance proactive pain management for cancer patients.

Article Abstract

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 ≥ 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352236PMC
http://dx.doi.org/10.1038/s41598-023-37742-5DOI Listing

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