Unstructured Text in EMR Improves Prediction of Death after Surgery in Children
| Autor: | Max R. Langham, Oguz Akbilgic, Robert L. Davis, Kevin Heinrich, Ramin Homayouni |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
pediatrics Computer Networks and Communications text mining Health outcomes Logistic regression 03 medical and health sciences 0302 clinical medicine Text mining surgery outcome Text messaging medicine 030212 general & internal medicine Framingham Risk Score lcsh:T58.5-58.64 lcsh:Information technology business.industry logistic regression Communication Medical record Unstructured data Surgery Human-Computer Interaction post-operative death 030220 oncology & carcinogenesis Surgery outcome business Clinical risk factor unstructured data |
| Zdroj: | Informatics Volume 6 Issue 1 Informatics, Vol 6, Iss 1, p 4 (2019) |
| ISSN: | 2227-9709 |
| DOI: | 10.3390/informatics6010004 |
| Popis: | Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children&rsquo s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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