Abstract: Methods and systems for accelerated input data conversion include partially parsing an input data set to convert the data set from a first format to a second format in an intermediate output having at least one unparsed portion to quickly perform a majority of the conversion. The partial parsing operates on portions of the input data set having a size less than a threshold size and leaves portions of the input data having a size greater than the threshold size unparsed. The intermediate output is parsed to convert at least one unparsed portion from the first format to the second format in a final output to complete the conversion such that a combined parsing time of the partial parse of the input data set and the parse of the intermediate output is accelerated relative to a single-stage parsing.

Abstract: Embodiments of the invention include methods, systems, and computer program products for generating a medical problem list. A non-limiting example of the method includes receiving, by a processor, a plurality of disease categories. A disease category set that includes a plurality of top level disease categories is defined using the processor, wherein the disease category set is based at least in part upon the plurality of disease categories. The processor is used to extract a plurality of candidate training problems from an electronic patient record training set. The processor is used to assign each of the candidate training problems to the plurality of top level disease categories. The processor is used to generate a disease category model for each of the top level disease categories from the electronic patient record training set using a machine learning technique.

Abstract: Methods and systems for accelerated input data conversion include partially parsing an input data set to convert the data set from a first format to a second format in an intermediate output having at least one unparsed portion to quickly perform a majority of the conversion. The partial parsing operates on portions of the input data set having a size less than a threshold size and leaves portions of the input data having a size greater than the threshold size unparsed. The intermediate output is parsed to convert at least one unparsed portion from the first format to the second format in a final output to complete the conversion such that a combined parsing time of the partial parse of the input data set and the parse of the intermediate output is accelerated relative to a single-stage parsing.

Abstract: Methods and systems for accelerated input data conversion include partially parsing an input data set to convert the data set from a first format to a second format in an intermediate output having at least one unparsed portion to quickly perform a majority of the conversion. The partial parsing operates on portions of the input data set having a size less than a threshold size and leaves portions of the input data having a size greater than the threshold size unparsed. The intermediate output is parsed to convert at least one unparsed portion from the first format to the second format in a final output to complete the conversion such that a combined parsing time of the partial parse of the input data set and the parse of the intermediate output is accelerated relative to a single-stage parsing.

Abstract: Embodiments of the invention include methods, systems, and computer program products for generating a medical problem list. A non-limiting example of the method includes receiving, by a processor, a plurality of disease categories. A disease category set that includes a plurality of top level disease categories is defined using the processor, wherein the disease category set is based at least in part upon the plurality of disease categories. The processor is used to extract a plurality of candidate training problems from an electronic patient record training set. The processor is used to assign each of the candidate training problems to the plurality of top level disease categories. The processor is used to generate a disease category model for each of the top level disease categories from the electronic patient record training set using a machine learning technique.

Abstract: Methods and systems for accelerated input data conversion include partially parsing an input data set to convert the data set from a first format to a second format in an intermediate output having at least one unparsed portion to quickly perform a majority of the conversion. The partial parsing operates on portions of the input data set having a size less than a threshold size and leaves portions of the input data having a size greater than the threshold size unparsed. The intermediate output is parsed to convert at least one unparsed portion from the first format to the second format in a final output to complete the conversion such that a combined parsing time of the partial parse of the input data set and the parse of the intermediate output is accelerated relative to a single-stage parsing.