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http://hdl.handle.net/11434/1072
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DC Field | Value | Language |
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dc.contributor.author | Wickramasinghe, Nilmini | - |
dc.contributor.other | Nguyen, Phuoc | - |
dc.contributor.other | Truyen, Tran | - |
dc.contributor.other | Venkatesh, Svetha | - |
dc.date.accessioned | 2017-05-05T06:21:57Z | - |
dc.date.available | 2017-05-05T06:21:57Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE journal of biomedical and health informatics 21.1 (2017): 22-30. | en_US |
dc.identifier.issn | 2168-2194 | en_US |
dc.identifier.uri | http://hdl.handle.net/11434/1072 | - |
dc.description.abstract | Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space. | en_US |
dc.publisher | Engineering in Medicine and Biology Society | en_US |
dc.relation.uri | https://arxiv.org/pdf/1607.07519.pdf | - |
dc.subject | Feature Engineering | en_US |
dc.subject | Predictive Systems | en_US |
dc.subject | EMR | en_US |
dc.subject | Electronic Medical Records | en_US |
dc.subject | End-to-End Deep Learning Syatem | en_US |
dc.subject | Deep Record | en_US |
dc.subject | Deepr | en_US |
dc.subject | Unplanned Readmission | en_US |
dc.subject | Chair of Health Informatics Management, Epworth HealthCare, Victoria, Australia | en_US |
dc.title | A convolutional net for medical records. | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | 10.1109/JBHI.2016.2633963 | en_US |
dc.identifier.journaltitle | IEEE Journal of Biomedical and Health Informatics | en_US |
dc.description.affiliates | Centre for Pattern Recognition and Data Analytics, Faculty of Science and Technology, Deakin University, Australia | - |
dc.type.contenttype | Text | en_US |
Appears in Collections: | Health Informatics |
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