Automated data extraction software for medical summary using text mining (T-Library)
- Published
- Accepted
- Subject Areas
- Epidemiology, Evidence Based Medicine
- Keywords
- Text mining, Automated data extraction, Clinical research, Electronic medical record
- Copyright
- © 2019 Yamada et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Automated data extraction software for medical summary using text mining (T-Library) PeerJ Preprints 7:e27685v1 https://doi.org/10.7287/peerj.preprints.27685v1
Abstract
The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive.
This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials.
This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning.
Strengths: 1) Quick and Easy operation
Author Comment
This is a preprint submission to PeerJ Preprints.