BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to computer mechanisms for training supervised machine learning models to automatically identify information in plain text narratives of electronic medical records.

Decision-support systems exist in many different industries where human experts require assistance in retrieving and analyzing information, such as from patients' electronic medical records (EMRs). Clinical notes, such as plain text narratives created by clinicians (i.e. physicians, nurses, and other care providers), are a rich source of patient care documentation in the patients' EMRs. While the clinical notes are free-form in principle, clinicians write them using the SOAP (subjective, objective, assessment, and plan) format. Typically, clinicians organize content within the clinical notes using section tags, such as “Assessment:” or “Plan:”. For example, the former indicates subsequent text (usually written in multiple lines, sometimes as a bulleted or numbered list) is the clinical assessment and the latter indicates a treatment plan. However, the presence of such tags is not guaranteed and their surface representation may vary considerably. For example, some clinical notes may use the abbreviated form “P:” or synonymous labels such as “Recommendation:” to indicate treatment plans.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described herein in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical information extraction and training mechanism for automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The illustrative embodiment segments each clinical note in a plurality of clinical notes into one or more identified sections using natural language processed plain text narratives of the plurality of clinical notes. The illustrative embodiment labels each identified section with an associated tag through a set of sequential steps. The illustrative embodiment generates a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The illustrative embodiment performs statistical analysis of the identified sections in the plurality of clinical notes that contain tags identified in the tag data structure to identify one or more valid stop/start conditions utilizing the tag data structure. The illustrative embodiment extracts a first set of positive examples of sentences for a selected type of information using associated tags in the tag data structure and the one or more valid stop/start conditions. The illustrative embodiment trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information.

In other illustrative embodiments, a computer program product comprising a computer useable or readable medium having a computer readable program is provided. The computer readable program, when executed on a computing device, causes the computing device to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided. The system/apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

These and other features and advantages of the present invention will be described in, or will become apparent to those of ordinary skill in the art in view of, the following detailed description of the example embodiments of the present invention.

