Deep Neural Learning for Automated Diagnostic Code Group Prediction Using Unstructured Nursing Notes
| Autor: | Tushaar Gangavarapu, S Sowmya Kamath, Gokul S. Krishnan, Aditya Jayasimha |
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| Rok vydání: | 2020 |
| Předmět: |
Multi-label classification
Computer science business.industry Deep learning Machine learning computer.software_genre Clinical decision support system Task (project management) Code (cryptography) Diagnosis code Artificial intelligence Adaptation (computer science) business computer Reliability (statistics) |
| Zdroj: | COMAD/CODS |
| DOI: | 10.1145/3371158.3371176 |
| Popis: | Disease prediction, a central problem in clinical care and management, has gained much significance over the last decade. Nursing notes documented by caregivers contain valuable information concerning a patient's state, which can aid in the development of intelligent clinical prediction systems. Moreover, due to the limited adaptation of structured electronic health records in developing countries, the need for disease prediction from such clinical text has garnered substantial interest from the research community. The availability of large, publicly available databases such as MIMIC-III, and advancements in machine and deep learning models with high predictive capabilities have further facilitated research in this direction. In this work, we model the latent knowledge embedded in the unstructured clinical nursing notes, to address the clinical task of disease prediction as a multi-label classification of ICD-9 code groups. We present EnTAGS, which facilitates aggregation of the data in the clinical nursing notes of a patient, by modeling them independent of one another. To handle the sparsity and high dimensionality of clinical nursing notes effectively, our proposed EnTAGS is built on the topics extracted using Non-negative matrix factorization. Furthermore, we explore the applicability of deep learning models for the clinical task of disease prediction, and assess the reliability of the proposed models using standard evaluation metrics. Our experimental evaluation revealed that the proposed approach consistently exceeded the state-of-the-art prediction model by 1.87% in accuracy, 12.68% in AUPRC, and 11.64% in MCC score. |
| Databáze: | OpenAIRE |
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