BACKGROUND

1. Field of the Invention

Embodiments of the invention relate to generating and customizing summarized notes. Certain embodiments of the invention relate to intelligent generation and customization of summarized notes from patient attributes and history.

2. Description of the Related Art

Some medical systems help physicians identify key information in a patient's medical record, surface relevant evidence, and explore treatment options. Some patient attributes may be extracted using Natural Language Processing (NLP).

However, often users enter attributes manually (“by hand”) through a more traditional form-based User Interface (UI). Manual entry may be prevalent in many international hospitals that do not have standard electronic medical records.

Regardless of whether the information is processed by NLP or entered manually, the result is a set of structured data used to analyze a patient's case and suggest treatment options. While structured data makes it easier to provide treatment options, it may be time consuming for a care team to sort through the various categories of information when trying to understand or share a case.

SUMMARY

In accordance with embodiments, a computer-implemented method is provided for generating and customizing summarized notes. The computer-implemented method comprises operations. A template is selected from a plurality of templates based on a context using a machine learning model. The template includes one or more translatable string resources with variables to represent key attributes extracted from historical notes. A summarized note is generated using values of the key attributes for the variables in the translatable string resources of the template.

In accordance with other embodiments, a computer program product is provided for generating and customizing summarized notes. The computer program product comprises a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform operations. A template is selected from a plurality of templates based on a context using a machine learning model. The template includes one or more translatable string resources with variables to represent key attributes extracted from historical notes. A summarized note is generated using values of the key attributes for the variables in the translatable string resources of the template.

In accordance with yet other embodiments, a computer system is provided for generating and customizing summarized notes. The computer system comprises one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; and program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform operations. A template is selected from a plurality of templates based on a context using a machine learning model. The template includes one or more translatable string resources with variables to represent key attributes extracted from historical notes. A summarized note is generated using values of the key attributes for the variables in the translatable string resources of the template.

Thus, embodiments advantageously generate a summarized note that is based on a template so that the summarized note may be customized (e.g., for a physician, a patient or a care team).

In other embodiments, the historical notes are ingested, sentence composition information is extracted from the historical notes, distribution patterns of the key attributes in the historical notes are identified, and characteristics of the historical notes are identified. The translatable string resources represent the characteristics using the sentence composition information and the distribution patterns. This advantageously allows for the summarized note to include sentences that are customized based on the characteristics, the sentence composition information, and the distribution patterns.

In additional embodiments, the distribution patterns are identified using a distribution machine learning model. This advantageously allows for use of the distribution machine learning model.

In yet additional embodiments, the characteristics are identified using a characteristics machine learning model. This advantageously allows for use of the characteristics machine learning model.

In further embodiments, the one or more translatable string resources are generated using a string machine learning model. This advantageously allows for use of the string machine learning model.

In yet further embodiments, the one or more translatable string resources are selected for the template using rules. This advantageously allows for further customization of the summarized note by using rules, which may be modified, to select particular translatable string resources for the template.

In yet other embodiments, the translatable string resources in the template are translated from a first language to a second language; and the summarized note is generated in the second language. This advantageously allows for generating the summarized note in any language.

Moreover, in embodiments, a Software as a Service (SaaS) is configured to perform operations of the computer-implemented method. This advantageously allows for embodiments in a cloud computing environment.

