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:
Bibkey:
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)
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PDF:
https://aclanthology.org/W16-4212.pdf