| 研究課題/領域番号 |
18H03336
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| 研究機関 | 聖路加国際大学 |
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研究代表者 |
ウォン スイー 聖路加国際大学, 専門職大学院公衆衛生学研究科(公衆衛生大学院), 准教授 (70791599)
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| 研究分担者 |
林 邦好 聖路加国際大学, 専門職大学院公衆衛生学研究科(公衆衛生大学院), 講師 (00793217)
笹野 遼平 名古屋大学, 情報学研究科, 准教授 (70603918)
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| 研究期間 (年度) |
2018-04-01 – 2022-03-31
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| キーワード | WHO Patient Safety / Artificial Intelligence / Incident reports / NLP / Named entity recognition / Adverse drug events / deep learning (DL) |
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| 研究実績の概要 |
We have successfully completed task 1 (annotation guideline development and validation), created annotated data, developed NER models, and explored the next generation patient safety system design. Additionally, we have also published our ongoing findings to various reputable international conferences and journals.including a newly accepted JAMIA publication. Our AI patient safety research also gained local and international attentions. Dr Shin Ushiro communicated our ongoing project outcomes as an example project at Australian College of Health Service Management (ACHSM, Brisbane) on Mar 3 and WHO Global Patient Safety Network Webinar Series on Mar 5.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
This fiscal year the research team completed the validation of medication error annotation method (Task 1) and developed named entity recognition (NER) model framework for incident reports learning (Task 2). Our team validated the proposed annotation guideline for incident reports and created annotated incident reports data for NER model training and validation. We evaluated multiple NER program settings that are suitable for incident report learning, and carried out extensive experiments to enhance model performance. We made a draft design of the prototype NER system (Task 3) by incorporating the NER model prediction as interactive outputs to aid incident reporting. These activities fulfilled the original project progress and plans.
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| 今後の研究の推進方策 |
Using our validated annotation method, we have created additional annotated data using incident report from JQ. Led by Dr Sasano, our team explored the appropriate NER models and evaluated the model performance. In the next fiscal year, we will develop the prototype NER system for patient safety and evaluate the performance of the system. To communicate our outcome with a wider community, we will continue the current efforts in connecting with the local and global patient safety and AI communities (including WHO patient safety). We will also write up research reports as conference/journal papers to document the key research outcomes. We hope our research would make significant impact to the local and global patient safety practice.
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