An annotation and modeling schema for prescription regimens

Autor: Samuel Bayer, Meredith Keybl, Cheryl Clark, John S. Aberdeen, David Tresner-Kirsch
Rok vydání: 2019
Předmět:
Conditional random field
Time Factors
Semantic HTML
Computer Networks and Communications
Computer science
Annotation
Health Informatics
02 engineering and technology
Medication
lcsh:Computer applications to medicine. Medical informatics
computer.software_genre
Drug Prescriptions
03 medical and health sciences
0302 clinical medicine
Schema (psychology)
0202 electrical engineering
electronic engineering
information engineering
Humans
030212 general & internal medicine
Medical prescription
Data Curation
business.industry
Research
Modeling
Models
Theoretical
Computer Science Applications
Prescriptions
Textual representation
lcsh:R858-859.7
020201 artificial intelligence & image processing
Pairwise comparison
Artificial intelligence
Precision and recall
business
computer
Natural language processing
Information Systems
Zdroj: Journal of Biomedical Semantics
Journal of Biomedical Semantics, Vol 10, Iss 1, Pp 1-11 (2019)
ISSN: 2041-1480
Popis: Background We introduce TranScriptML, a semantic representation schema for prescription regimens allowing various properties of prescriptions (e.g. dose, frequency, route) to be specified separately and applied (manually or automatically) as annotations to patient instructions. In this paper, we describe the annotation schema, the curation of a corpus of prescription instructions through a manual annotation effort, and initial experiments in modeling and automated generation of TranScriptML representations. Results TranScriptML was developed in the process of curating a corpus of 2914 ambulatory prescriptions written within the Partners Healthcare network, and its schema is informed by the content of that corpus. We developed the representation schema as a novel set of semantic tags for prescription concept categories (e.g. frequency); each tag label is defined with an accompanying attribute framework in which the meaning of tagged concepts can be specified in a normalized fashion. We annotated a subset (1746) of this dataset using cross-validation and reconciliation between multiple annotators, and used Conditional Random Field machine learning and various other methods to train automated annotation models based on the manual annotations. The TranScriptML schema implementation, manual annotation, and machine learning were all performed using the MITRE Annotation Toolkit (MAT). We report that our annotation schema can be applied with varying levels of pairwise agreement, ranging from low agreement levels (0.125 F for the relatively rare REFILL tag) to high agreement levels approaching 0.9 F for some of the more frequent tags. We report similarly variable scores for modeling tag labels and spans, averaging 0.748 F-measure with balanced precision and recall. The best of our various attribute modeling methods captured most attributes with accuracy above 0.9. Conclusions We have described an annotation schema for prescription regimens, and shown that it is possible to annotate prescription regimens at high accuracy for many tag types. We have further shown that many of these tags and attributes can be modeled at high accuracy with various techniques. By structuring the textual representation through annotation enriched with normalized values, the text can be compared against the pharmacist-entered structured data, offering an opportunity to detect and correct discrepancies. Electronic supplementary material The online version of this article (10.1186/s13326-019-0201-9) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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