Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries.
Obtaining encoded variables is often a key obstacle to automating clinical guidelines. Frequently the pertinent information occurs as text in patient reports, but text is inadequate for the task. This paper describes a retrospective study that automates determination of severity classes for patients with community-acquired pneumonia (i.e. classifies patients into risk classes 1-5), a common and costly clinical problem. Most of the variables for the automated application were obtained by writing queries based on output generated by MedLEE1, a natural language processor that encodes clinical information in text. Comorbidities, vital signs, and symptoms from discharge summaries as well as information from chest x-ray reports were used. The results were very good because when compared with a reference standard obtained manually by an independent expert, the automated application demonstrated an accuracy, sensitivity, and specificity of 93%, 92%, and 93% respectively for processing discharge summaries, and 96%, 87%, and 98% respectively for chest x-rays. The accuracy for vital sign values was 85%, and the accuracy for determining the exact risk class was 80%. The remaining 20% that did not match exactly differed by only one class.
Friedman
C
C
Department of Computer Science, Queens College CUNY, USA.
Knirsch
C
C
Shagina
L
L
Hripcsak
G
G
D017714:Community-Acquired Infections / Q000145:classification; D005240:Feasibility Studies; D006751:Hospital Information Systems; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010351:Patient Discharge; D011014:Pneumonia / Q000145:classification*; D011379:Prognosis; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index