Titlebook: Health Information Science; 11th International C Agma Traina,Hua Wang,Lu Chen Conference proceedings 2022 The Editor(s) (if applicable) and

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書目名稱Health Information Science影響因子(影響力)




書目名稱Health Information Science影響因子(影響力)學(xué)科排名




書目名稱Health Information Science網(wǎng)絡(luò)公開度




書目名稱Health Information Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Health Information Science被引頻次




書目名稱Health Information Science被引頻次學(xué)科排名




書目名稱Health Information Science年度引用




書目名稱Health Information Science年度引用學(xué)科排名




書目名稱Health Information Science讀者反饋




書目名稱Health Information Science讀者反饋學(xué)科排名

行乞

Detection of Obsessive-Compulsive Disorder in Australian Children and Adolescents Using Machine Learis crucial to identify the causes of this mental illness. Making an early and accurate diagnosis of OCD in children and adolescents is essential to preventing the long-term problems. Several studies have looked at ways to recognise OCD in children, but their accuracy was not very high and?they only

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An Anomaly Detection Framework Based on Data Lake for Medical Multivariate Time Series amount of multi-source heterogeneous multivariate time series data is produced. Traditional data platforms have difficulties to organize and explore these data. In addition, the high dimensionality of multivariate time series also makes it difficult for the detection process to capture the correlat

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Medicaid

Combining Process Mining and?Time Series Forecasting to?Predict Hospital Bed Occupancymbine process mining and a Deep Spatial-Temporal Graph Modeling algorithm and show that this improves the performance of the prediction over existing approaches. To improve the model even more it is extended with knowledge available from patient records, like the day of the week, the time of the day

蛤肉

HGCL: Heterogeneous Graph Contrastive Learning for?Traditional Chinese Medicine Prescription Generatthe most crucial components in building intelligent diagnosis systems that provide clinical decision support to physicians. While various machine learning models for prescription generation have been created, they suffer from specific limitations (e.g., data complexity and semantic ambiguity, lack o

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