Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
| Autor: | Nora Hollenstein, Pengfei Ji, Adam Ivankay, Chiara Marchiori, Lorenz Kuhn, An-phi Nguyen, Ivan Girardi, Ce Zhang |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science media_common.quotation_subject Machine Learning (stat.ML) 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network Machine Learning (cs.LG) Task (project management) 03 medical and health sciences Presentation 0302 clinical medicine Statistics - Machine Learning Component (UML) 030212 general & internal medicine 0105 earth and related environmental sciences Point of care media_common Computer Science - Computation and Language Artificial neural network business.industry Perspective (graphical) Triage Artificial intelligence business Computation and Language (cs.CL) computer |
| Zdroj: | Louhi@EMNLP |
| DOI: | 10.18653/v1/w18-5616 |
| Popis: | We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural network architecture trained on 600,000 doctor notes in German. We compare two approaches, one that uses the full text of the medical notes and one that uses only a selected list of medical entities extracted from the text. These approaches achieve 79% and 66% precision, respectively, but on a confidence threshold of 0.6, precision increases to 85% and 75%, respectively. In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective. The method is based on the learning of attention scores and a method of automatic validation using the same data. 10 pages, 2 figures, EMNLP workshop LOUHI 2018 |
| Databáze: | OpenAIRE |
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