Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

Autor: Tanmoy Paul, Humayera Islam, Nitesh Singh, Yaswitha Jampani, Teja Venkat Pavan Kotapati, Preethi Aishwarya Tautam, Md Kamruz Zaman Rana, Vasanthi Mandhadi, Vishakha Sharma, Michael Barnes, Richard D. Hammer, Abu Saleh Mohammad Mosa

Publikováno v: Applied Sciences, Vol 12, Iss 19, p 9976 (2022)

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The perf

Externí odkaz: https://doaj.org/article/e0a2cb981dc5479694e7a9dccdcb2ccd

Evaluation of machine learning algorithms for prediction of 5-year survivability for seven types of cancers

Autor: Teja Venkat Pavan Kotapati

A lot of research on prediction of cancer survivability has been done by implementing various machine learning models and it has always been a challenging task. In this project, the main focus is to perform a comprehensive evaluation of machine learn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::282776eff4aa916994a6addb8abdd90c
https://doi.org/10.32469/10355/86544

Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients.

Autor: Paul T; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States., Rana MKZ; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States., Tautam PA; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States., Kotapati TVP; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States., Jampani Y; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States., Singh N; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States., Islam H; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.; Institute for Data Science and Informatics, University of Missouri, Columbia, MO, United States., Mandhadi V; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States., Sharma V; Roche Diagnostics, F. Hoffmann-La Roche, Santa Clara, CA, United States., Barnes M; Roche Diagnostics, F. Hoffmann-La Roche, Santa Clara, CA, United States., Hammer RD; Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, United States., Mosa ASM; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.; Center for Biomedical Informatics, University of Missouri, Columbia, MO, United States.; Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States.; Institute for Data Science and Informatics, University of Missouri, Columbia, MO, United States.

Publikováno v: Frontiers in digital health [Front Digit Health] 2022 Feb 16; Vol. 4, pp. 728922. Date of Electronic Publication: 2022 Feb 16 (Print Publication: 2022).