Text mining electronic health records to identify hospital adverse events.
Manual reviews of health records to identify possible adverse events are time consuming. We are developing a method based on natural language processing to quickly search electronic health records for common triggers and adverse events. Our results agree fairly well with those obtained using manual reviews, and we therefore believe that it is possible to develop automatic tools for monitoring aspects of patient safety.
Centre for Quality, Region of Southern Denmark, Denmark.
Lars Ulrik
LU
Gerdes
Christian
C
Hardahl
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods* / Q000706:statistics & numerical data; D003718:Denmark; D006801:Humans; D019300:Medical Errors / Q000145:classification* / Q000706:statistics & numerical data; D009323:Natural Language Processing; D003668:Pressure Ulcer / Q000145:classification / Q000453:epidemiology*; D015203:Reproducibility of Results; D012308:Risk Management / Q000379:methods*; D012680:Sensitivity and Specificity