A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

Ingenieria y Universidad

Alexandra Pomares Quimbaya¹  Rafael A. Gonzalez¹  Oscar Muñoz²  Olga Milena García²  Wilson Ricardo Bohorquez² 

[1] Pontificia Universidad Javeriana;Pontificia Universidad JaverianaHospital Universitario San Ignacio

关键词: electronic medical records;    natural language processing;    narrative text;   

学科分类：工程和技术（综合）

来源: Pontificia Universidad Javeriana

【 摘 要 】

Objective : Electronic medical records (EMR) typically contain both structured attributes as well as narrative text. The usefulness of EMR for research and administration is hampered by the difficulty in automatically analyzing their narrative portions. Accordingly, this paper proposes SPIRE, a strategy for prioritizing EMR, using natural language processing in combination with analysis of structured data, in order to identify and rank EMR that match specific queries from clinical researchers and health administrators. Materials and Methods : The resulting software tool was evaluated technically and validated with three cases (heart failure, pulmonary hypertension and diabetes mellitus) compared against expert obtained results. Results and Discussion : Our preliminary results show high sensitivity (70%, 82% and 87% respectively) and specificity (85%, 73.7% and 87.5%) in the resulting set of records. The AUC was between 0.84 and 0.9. Conclusions : SPIRE was successfully implemented and used in the context of a university hospital information system, enabling clinical researchers to obtain prioritized EMR to solve their information needs through collaborative search templates with faster and more accurate results than other existing methods.

【 授权许可】

CC BY