Comorbid insomnia among breast cancer survivors and its prediction using machine learning: a nationwide study in Japan

Abstract

Objective Insomnia is an increasingly recognized major symptom of breast cancer which can seriously disrupt the quality of life during and many years after treatment. Sleep problems have also been linked with survival in women with breast cancer. The aims of this study were to estimate the prevalence of insomnia in breast cancers survivors, clarify the clinical characteristics of their sleep difficulties and use machine learning techniques to explore clinical insights. Methods Our analysis of data, obtained in a nationwide questionnaire survey of breast cancer survivors in Japan, revealed a prevalence of suspected insomnia of 37.5%. With the clinical data obtained, we then used machine learning algorithms to develop a classifier that predicts comorbid insomnia. The performance of the prediction model was evaluated using 8-fold cross-validation. Results When using optimal hyperparameters, the L2 penalized logistic regression model and the XGBoost model provided predictive accuracy of 71.5 and 70.6% for the presence of suspected insomnia, with areas under the curve of 0.76 and 0.75, respectively. Population segments with high risk of insomnia were also extracted using the RuleFit algorithm. We found that cancer-related fatigue is a predictor of insomnia in breast cancer survivors. Conclusions The high prevalence of sleep problems and its link with mortality warrants routine screening. Our novel predictive model using a machine learning approach offers clinically important insights for the early detection of comorbid insomnia and intervention in breast cancer survivors.

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

“…Of the 42 included studies, 10 unique symptoms were reported as outcome variables in the predictions. Those included were xerostomia (9/42, 14%) [ 27 , 30 , 34 , 49 - 51 , 55 , 58 , 60 ], depression (8/42, 13%) [ 22 , 33 , 37 , 41 , 43 , 45 , 52 , 59 ], pain (8/42, 13%) [ 20 , 25 , 26 , 35 , 37 , 40 , 42 , 56 ], fatigue (6/42, 10%) [ 23 , 24 , 29 , 38 , 42 , 44 ], anxiety (3/42, 5%) [ 33 , 46 , 52 ], sleep disturbance or insomnia (3/42, 5%) [ 26 , 28 , 52 ], nausea or vomiting (3/42, 5%) [ 17 , 26 , 29 ], weight loss (2/42, 3%) [ 47 , 53 ], cognitive impairment (2/42, 3%) [ 21 , 36 ], and diarrhea (2/42, 3%) [ 29 , 42 ].…”

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

“…43 studies showed current landscape and applied scenario of AI used in cancer survivorship domain. Most of the studies were conducted in Europe ( n = 16) 10 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , Asia ( n = 12) 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , USA ( n = 12) 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 followed by Middle East ( n = 2) 53 , 54 , and UK ( n = 1) 55 Supplement A presents an overview of these studies’ characteristics. Based on the various clinical application and potential functions, the studies can be divided into following categories, they are 1.…”

Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records

“…In this study, the AUROC is considered to have high accuracy when it is ⩾0.9, moderate accuracy when it is ⩾0.7 but <0.9, and low accuracy when it is ⩾0.5 but <0.7. 27 This study used both AUROC and AUPRC because they are considered to be more informative for evaluating imbalanced datasets. 28 All analyses were conducted using Python 3.8 (Python Software Foundation, Wilmington, DE) and R 4.0.2.…”