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A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

This clinician-confirmed cancer progression in unstructured text (ie, clinical notes) has been shown to serve as a reasonable surrogate for real-world indicators in ascertaining progression endpoints. This is also more practical for real-world studies than purely RECIST-based approaches [6].

Gowtham Varma, Rohit Kumar Yenukoti, Praveen Kumar M, Bandlamudi Sai Ashrit, K Purushotham, C Subash, Sunil Kumar Ravi, Verghese Kurien, Avinash Aman, Mithun Manoharan, Shashank Jaiswal, Akash Anand, Rakesh Barve, Viswanathan Thiagarajan, Patrick Lenehan, Scott A Soefje, Venky Soundararajan

JMIR Cancer 2025;11:e64697

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study

Mc Kenzie et al [11] conducted a retrospective analysis of pneumonia using electronic health records; however, they used rule-based NLP methods for 2 types of documents, clinical notes and radiology reports written by physicians, thus leaving room for further investigation into performance. In addition to physicians’ records, medical institutions have a wide variety of documents from multiple co-medical personnel, including nursing records, pharmacists’ progress notes, and medication orders.

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

JMIR Med Inform 2024;12:e58977

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

The 5 types of commonly recorded notes were extracted, including chief complaint, history of present illness, medical history, admission medications, and physical exam. In Figure S1 in Multimedia Appendix 1, an example of an admission note is shown with highlights.

Zhenyue Gao, Xiaoli Liu, Yu Kang, Pan Hu, Xiu Zhang, Wei Yan, Muyang Yan, Pengming Yu, Qing Zhang, Wendong Xiao, Zhengbo Zhang

J Med Internet Res 2024;26:e54363