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| DOI | 10.1145/3288599.3297118 |
| Evaluating the Quality of Word Representation Models for Unstructured Clinical Text based ICU Mortality Prediction | |
| Krishnan, Gokul S.; Kamath, Sowmya S. | |
| 通讯作者 | Krishnan, Gokul S. |
| 会议名称 | 20th International Conference on Distributed Computing and Networking (ICDCN) |
| 会议日期 | JAN 04-07, 2019 |
| 会议地点 | Indian Inst Sci, Bangalore, INDIA |
| 英文摘要 | In modern hospitals, the role of Clinical Decision Support Systems (CDSS) in assisting care providers is well-established. Most conventional CDSS systems are built on the availability of patient data in the form of structured Electronic Health Records. However, a significant percentage of patient data is still stored in the form of unstructured clinical text notes, especially in developing countries. These contain abundant patient-specific information, which has so far remained largely under-utilized in powering CDSS applications. In this paper, we attempt to build one such CDSS system for patient mortality prediction, using unstructured clinical records. Effectiveness of such prediction models largely depends on optimally capturing latent concept features, thus, word representation quality is of utmost importance. We experiment with three popular word embedding models -Word2Vec, FastText and GloVe for generating word embeddings of unstructured nursing notes of patients from a standard, open dataset, MIMIC-III. These word representations are used as features to train machine learning classifiers to build ICU mortality prediction models, a critical CDSS in ICUs of hospitals. Experimental validation showed that a model built on Word2Vec Skipgram based Random Forest classifier was the most optimal word embedding based mortality prediction model, that outperformed traditional severity scores like SAPS-II, SOFA, APS-III and OASIS, by a significant margin of 43-52%. |
| 英文关键词 | Word Embedding Natural Language Processing Machine Learning Healthcare Informatics Mortality Prediction |
| 来源出版物 | ICDCN '19: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING |
| 出版年 | 2019 |
| 页码 | 480-485 |
| ISBN | 978-1-4503-6094-4 |
| EISBN | 978-1-4503-6094-4 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 类型 | Proceedings Paper |
| 语种 | 英语 |
| 国家 | India |
| 收录类别 | CPCI-S |
| WOS记录号 | WOS:000484491600067 |
| WOS关键词 | CHRONIC HEALTH EVALUATION ; ACUTE PHYSIOLOGY SCORE ; CLASSIFICATION ; APACHE ; SEVERITY |
| WOS类目 | Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS研究方向 | Computer Science ; Engineering |
| 资源类型 | 会议论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/308035 |
| 作者单位 | Dept Informat Technol, Mangalore, India |
| 推荐引用方式 GB/T 7714 | Krishnan, Gokul S.,Kamath, Sowmya S.. Evaluating the Quality of Word Representation Models for Unstructured Clinical Text based ICU Mortality Prediction[C]:ASSOC COMPUTING MACHINERY,2019:480-485. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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