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. 2024 Sep 24;12:e58977. doi: 10.2196/58977

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©Tomohiro Nishiyama, Ayane Yamaguchi, Peitao Han, Lis Weiji Kanashiro Pereira, Yuka Otsuki, Gabriel Herman Bernardim Andrade, Noriko Kudo, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki, Masahiro Takada, Masakazu Toi. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 24.09.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

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Data flow of the proposed system. (A) shows the events from multiple types of documents are extracted. An event timeline (B) is created from each clinical data using the natural language processing method, and then the curve (C) is created based on the aggregated results. The dots in the event timeline indicate the timing at which the description of drug administration or symptom onset is recorded. Based on (B), patients who received the target drug (a taxane drug in this study) are selected, and the Kaplan-Meier curve (C) is generated.