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| Exploring the application of machine learning in predicting the effect of acupuncture for insomnia based on brain functional connectivity |
| YIN Xuejiao1 JIANG Tongfei1 CHEN Zhaoyi1 SONG Zhangxiao2 LI Bin1 GUO Jing1 |
1.Department of Acupuncture and Moxibustion, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Key Laboratory of Acupuncture Neuromodulation, Beijing 100010, China;
2.School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing 100105, China
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Abstract Acupuncture is one of the effective ways to treat insomnia, but because it is based on clinical experience, the treatment protocol has yet to be optimized, and the efficacy is mainly subjective assessment, with significant differences in individual efficacy. Therefore, it is important to screen effective populations for individual identification markers to predict efficacy and optimize acupuncture treatment protocols. Machine learning (ML) for classification and prediction algorithms are rapidly becoming tools for disease diagnosis, prediction, and classification. In contrast, magnetic resonance imaging has revealed that the brain functional connectivity (FC) of insomniac individuals is highly specific, with specific patterns of change before and after acupuncture, and can be used as a marker to track treatment response. This paper proposes to explore the feasibility of using the classification algorithm model in ML to predict the efficacy of acupuncture treatment for insomnia based on FC technology, in order to break the bottleneck of “empirical treatment” and optimize the acupuncture treatment plan.
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