GDL

Journal from gdlhub / 2021-08-24 12:06:05
Oleh : Shahzeb Khan, Jawwad Ahmed Shamsi, King Saud University
Dibuat : 2021-08-24, dengan 0 file

Keyword : Clinical decision support system, Electronic Health Record, Deep learning, Natural language processing, GPUs, Big data, Machine learning, High Performance computing, Disease Identification, Big data analytics
Url : http://www.sciencedirect.com/science/article/pii/S1319157818306165
Sumber pengambilan dokumen : Web

We propose Health Quest – a Deep Neural Network based Clinical Decision Support (CDS) system, which helps in diagnosing diseases through a patient’s Electronic Health Record (EHR). Health Quest is unique and distinct from existing CDS systems as it is a generalized system, which is not targeted to diagnosing a specific disease and can detect multiple diseases at a time. It utilizes a patient’s medical and family history and incorporates natural language processing to convert this information in a structured text for further processing. Multi-label classification is used to identify multiple diseases at a time. For improved accuracy and precision, Health Quest has been implemented on GPUs. We evaluate Health Quest on a dataset containing 5000 EHR records, with information of patients suffering from multiple diseases. We present the design and implementation of Health Quest and describe evaluation results. It is anticipated that Health Quest can be highly beneficial in prevention of mis-diagnosis of diseases – a major problem in medical sciences.

Deskripsi Alternatif :

We propose Health Quest – a Deep Neural Network based Clinical Decision Support (CDS) system, which helps in diagnosing diseases through a patient’s Electronic Health Record (EHR). Health Quest is unique and distinct from existing CDS systems as it is a generalized system, which is not targeted to diagnosing a specific disease and can detect multiple diseases at a time. It utilizes a patient’s medical and family history and incorporates natural language processing to convert this information in a structured text for further processing. Multi-label classification is used to identify multiple diseases at a time. For improved accuracy and precision, Health Quest has been implemented on GPUs. We evaluate Health Quest on a dataset containing 5000 EHR records, with information of patients suffering from multiple diseases. We present the design and implementation of Health Quest and describe evaluation results. It is anticipated that Health Quest can be highly beneficial in prevention of mis-diagnosis of diseases – a major problem in medical sciences.

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