Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records
Takanori Yamashita, Yoshifumi Wakata, Hidehisa Soejima, Naoki Nakashima, Sachio Hirokawa
Abstract
The number of unstructured medical records kept in hospital information systems is increasing. The conditions of patients are formulated as outcomes in clinical pathway. A variance of an outcome describes deviations from standards of care like a patient’s bad condition. The present paper applied text mining to extract feature words and phrases of the variance from admission records. We report the cases the variances of “pain control” and “no neuropathy worsening” in cerebral infarction.- Anthology ID:
- W16-4212
- Volume:
- Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 86–90
- Language:
- URL:
- https://aclanthology.org/W16-4212
- DOI:
- Cite (ACL):
- Takanori Yamashita, Yoshifumi Wakata, Hidehisa Soejima, Naoki Nakashima, and Sachio Hirokawa. 2016. Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 86–90, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records (Yamashita et al., ClinicalNLP 2016)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/W16-4212.pdf