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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/138870
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| Title: | Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature |
| Authors: | 黃柏僩
Huang, Po-Hsien
Hsu, Yu-Ching
Wang, Jung-Der
Chien, Yu-Wen
Chiu, Ching-Ju
Lin, Cheng-Yu |
| Contributors: | 心理系 |
| Keywords: | bio-feature;prediction model;prior domain knowledge;self-reported snoring |
| Date: | 2021-09 |
| Issue Date: | 2022-02-10 11:37:46 (UTC+8) |
| Abstract: | Our study’s main purpose is to emphasise the significance of medical knowledge of pathophysiology before machine learning. We investigated whether combining domain knowledge with machine learning results might increase accuracy and minimise the number of bio-features used to detect obstructive sleep apnea (OSA). The present study analysed data on 36 self-reported symptoms and 24 clinical features obtained from 3,495 patients receiving polysomnography at a regional hospital and a medical centre. The area under the receiver operating characteristic (AUC) curve was used to evaluate patients with and without moderate or severe OSA using three prediction models on the basis of various estimation methods: the multiple logistic regression (MLR), support vector machine (SVM), and neural network (NN) methods. Odds ratios stratified by gender and age were also measured to account for clinicians’ common sense. We discovered that adding the self-reported snoring item improved the AUC by 0.01–0.10 and helped us to rapidly achieve the optimum level. The performance of four items (gender, age, body mass index [BMI], and snoring) was comparable with that of adding two or more items (neck and waist circumference) for predicting moderate to severe OSA (Apnea–Hypopnea Index ≥15 events/hr) in all three prediction models, demonstrating the medical knowledge value of pathophysiology. The four-item test sample AUCs were 0.83, 0.84, and 0.83 for MLR, SVM, and NN, respectively. Participants with regular snoring and a BMI of ≥25 kg/m2 had a greater chance of moderate to severe OSA according to the stratified adjusted odds ratios. Combining domain knowledge into machine learning could increase efficiency and enable primary care physicians to refer for an OSA diagnosis earlier. |
| Relation: | Journal of Sleep Research, pp.e13487 |
| Data Type: | article |
| DOI 連結: | https://doi.org/10.1111/jsr.13487 |
| DOI: | 10.1111/jsr.13487 |
| Appears in Collections: | [心理學系] 期刊論文
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