Patient-level temporal aggregation for text-based asthma status ascertainment.
OBJECTIVE
To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.


METHODS
We utilized an existing pediatric asthma cohort with manual annotations. After generating a basic feature set via standard clinical NLP methods, we introduce six methods of aggregating time-distributed features from the document level to the patient level. These aggregation methods are used to classify patients according to their asthma status in two hypothetical settings: retrospective epidemiology and clinical decision support.


RESULTS
In both settings, solid patient classification performance was obtained with machine learning algorithms on a number of evidence aggregation methods, with Sum aggregation obtaining the highest F1 score of 85.71% on the retrospective epidemiological setting, and a probability density function-based method obtaining the highest F1 score of 74.63% on the clinical decision support setting. Multiple techniques also estimated the diagnosis date (index date) of asthma with promising accuracy.


CONCLUSIONS
The clinical decision support setting is a more difficult problem. We rule out some aggregation methods rather than determining the best overall aggregation method, since our preliminary data set represented a practical setting in which manually annotated data were limited.


CONCLUSIONS
Results contrasted the strengths of several aggregation algorithms in different settings. Multiple approaches exhibited good patient classification performance, and also predicted the timing of estimates with reasonable accuracy.
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
Stephen T
ST
Wu
Department of Community Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Young J
YJ
Juhn
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
Sunghwan
S
Sohn
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
Hongfang
H
Liu
D000465:Algorithms; D001185:Artificial Intelligence; D001249:Asthma / Q000145:classification* / Q000175:diagnosis; D002648:Child; D002965:Classification / Q000379:methods; D003658:Decision Making, Computer-Assisted; D006801:Humans; D055641:Mathematical Concepts; D009323:Natural Language Processing; D010372:Pediatrics; D013995:Time
Asthma epidemiology; Information extraction; Natural language processing; Patient classification