ACADSTAFF UGM
| Title | : | An electronic medical record's keywords detection using supervised learning |
| Author | : |
Dr. Lukman Heryawan, S.T., M.T. (1) FITRA FEBRIANSYAH (2) |
| Date | : | 6 2021 |
| Keyword | : | Electronic medical record,Supervised learning,LSTM,Machine learning,Deep learning Electronic medical record,Supervised learning,LSTM,Machine learning,Deep learning |
| Abstract | : | Electronic medical record (EMR) is an important record, which is documented to describe a medical status of patient in the digital or computerized form. Most of the EMR content contains a textual data or medical notes. One of the important usages of EMR is to support the assignment of medical code by medical coder. The medical code is important to make EMR usable in term of hospital administration and research purpose. One step of medical code assignment was done by detecting keywords inside medical notes. The detection of keywords is done by reading through the medical notes by medical coder, which is time consuming. In order to support the keywords detection without reading through the medical notes, we propose supervised learning algorithms to automatically detect keywords and present the keywords to the medical coder. We use Long short-term memory (LSTM) based supervised learning algorithm to detect keywords. LSTM is able to achieve good performance in term of accuracy in detecting keywords inside medical notes. LSTM achieve 80?curacy. Based on the result, LSTM based supervised learning is promising to be used for keywords detection of medical notes, which is one of the important steps for the medical coding. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
|
| No | Title | Action |
|---|