دورية أكاديمية
Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning
| العنوان: | Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning |
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| المؤلفون: | Eunjoo Jeon, Youngsam Kim, Hojun Park, Rae Woong Park, Hyopil Shin, Hyeoun-Ae Park |
| المصدر: | Healthcare Informatics Research, Vol 26, Iss 2, Pp 104-111 (2020) |
| بيانات النشر: | The Korean Society of Medical Informatics, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | drug-related side effects and adverse reactions, electronic health records, machine learning, deep learning, nursing records, Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Objectives Electronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning). Methods Nursing notes of 8,316 patients (4,158 ADR and 4,158 non-ADR cases) admitted to Ajou University Hospital were used for the ADR classification task. A TD(λ) model was used to estimate state values for indicating the ADR risk. For the TD learning, each nursing phrase was encoded into one of seven states, and the state values estimated during training were employed for the subsequent testing phase. We applied logistic regression to the state values from the TD(λ) model for the classification task. Results The overall accuracy of TD-based logistic regression of 0.63 was comparable to that of two machine-learning methods (0.64 for a naïve Bayes classifier and 0.63 for a support vector machine), while it outperformed two deep learning-based methods (0.58 for a text convolutional neural network and 0.61 for a long short-term memory neural network). Most importantly, it was found that the TD-based method can estimate state values according to the context of nursing phrases. Conclusions TD learning is a promising approach because it can exploit contextual, time-dependent aspects of the available data and provide an analysis of the severity of ADRs in a fully incremental manner. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2093-3681 2093-369X |
| Relation: | http://e-hir.org/upload/pdf/hir-26-2-104.pdf; https://doaj.org/toc/2093-3681; https://doaj.org/toc/2093-369X |
| DOI: | 10.4258/hir.2020.26.2.104 |
| URL الوصول: | https://doaj.org/article/7fee2be751ef480182f7b31b0888be6c |
| رقم الأكسشن: | edsdoj.7fee2be751ef480182f7b31b0888be6c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20933681 2093369X |
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| DOI: | 10.4258/hir.2020.26.2.104 |