Integrated Prediction Method for Mental Illness with Multimodal Sleep Function Indicators
| Autor: | Hong Wang, Yu Xiaomei, Wen-tao Tan, Lu-tong Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Sleep quality
010308 nuclear & particles physics Computer science Feature extraction Mental illness medicine.disease 01 natural sciences Mental health Sleep function Test (assessment) 03 medical and health sciences 0302 clinical medicine Ensemble prediction 0103 physical sciences Insomnia medicine medicine.symptom 030217 neurology & neurosurgery Cognitive psychology |
| Zdroj: | Algorithms and Architectures for Parallel Processing ISBN: 9783030050627 ICA3PP (4) |
| Popis: | Sleep quality has great effect on physical and mental health. Severe insomnia will cause autonomic neurological dysfunction. For making good clinical decisions, it is crucial to extract features of sleep quality and accurately predict the mental illness. Prior studies have a number of deficiencies to be overcome. On the one hand, the selected features for sleep quality are not good enough, as they do not account for multisource and heterogeneous features. On the other hand, the mental illness prediction model does not work well and thus needs to be enhanced and improved. This paper presents a multi-dimensional feature extraction method and an ensemble prediction model for mental illness. First, we do correlation analysis for every indicators and sleep quality, and further select the optimal heterogeneous features. Next, we propose a combinational model, which is integrated by basic modules according to their weights. Finally, we perform abundant experiments to test our method. Experimental results demonstrate that our approach outperforms many state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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