Abstract
BackgroundExtracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time.ObjectiveOur goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results.MethodsA clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction.ResultsThree datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports—each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%.ConclusionsIDEAL-X adopts a unique online machine learning–based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable.
Highlights
While immense efforts have been made to enable structured data model for electronic medical record (EMR), a large amount of medical data remain in free-form narrative text, and useful data from individual patients are usually distributed across multiple reports of heterogeneous structures and vocabularies
Our goal is to provide a generic information extraction framework that is adaptable to diverse clinical reports, enables a dynamic interaction between a human and a machine, and produces highly accurate results with minimal human effort
We have developed a system, Information and Data Extraction using Adaptive Online Learning (IDEAL-X), to support adaptive information extraction from diverse clinical reports with heterogeneous structures and vocabularies
Summary
While immense efforts have been made to enable structured data model for electronic medical record (EMR), a large amount of medical data remain in free-form narrative text, and useful data from individual patients are usually distributed across multiple reports of heterogeneous structures and vocabularies This poses major challenges to traditional information extraction systems, as either costly training datasets or manually crafted rules have to be prepared. Our goal is to provide a generic information extraction framework that is adaptable to diverse clinical reports, enables a dynamic interaction between a human and a machine, and produces highly accurate results with minimal human effort. Methods: A clinical information extraction system IDEAL-X has been built on top of online machine learning It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. This may limit its ability to extract certain value types
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