PHENOTYPING OF CLINICAL NOTES USING NATURAL LANGUAGE PROCESSING MODELS

1 . A method for phenotyping clinical data, the method comprising:

obtaining, in electronic form, a plurality of episodic records, wherein each episodic record in the plurality of episodic records comprises corresponding unstructured clinical data from an electronic medical record (EMR) or electronic health record (EHR) for a respective patient in a plurality of patients;

filtering the plurality of episodic records by language pattern recognition to identify a sub-plurality of episodic records that each includes an expression related to a clinical condition in the corresponding unstructured clinical data;

splitting, for each episodic record in the sub-plurality of episodic records, the corresponding unstructured clinical data into a corresponding plurality of snippets, wherein each snippet in the corresponding plurality of snippets comprises a corresponding set of one or more tokens; and

predicting, for each episodic record in the sub-plurality of episodic records, if the respective episodic record represents an instance of the clinical condition by inputting the corresponding plurality of snippets for the respective episodic record to a classifier comprising a first portion and a second portion, wherein the first portion comprises an aggregation function that aggregates the corresponding plurality of snippets to output a corresponding representation for the respective episodic record, and wherein the second portion interprets the corresponding representation to output a corresponding prediction for whether the respective episodic record represents an instance of the clinical condition.

2 . The method of claim 1 , wherein, for a respective episodic record in the plurality of episodic records, the corresponding unstructured clinical data is obtained from a plurality of medical evaluations memorialized in the EMR or EHR for the respective patient.

3 . The method of claim 2 , wherein the plurality of medical evaluations are selected by (i) clustering all or a portion of medical evaluations memorialized in the EMR or EHR for the respective patient to obtain one or more corresponding medical evaluation clusters, and (ii) aggregating unstructured clinical data corresponding to each medical evaluation in a respective medical evaluation cluster of the one or more corresponding medical evaluation clusters, thereby forming the respective episodic record.

4 . The method of claim 3 , wherein the clustering is, at least in part, temporal based clustering.

5 . The method of claim 3 , wherein the clustering is one-dimensional clustering.

6 . The method of claim 1 , wherein, for a respective episodic record in the plurality of episodic records, the corresponding unstructured clinical data is obtained from a single of medical evaluation memorialized in the EMR or EHR.

7 . The method of claim 1 , wherein each episodic record in the plurality of episodic records does not include corresponding structured clinical data from the EMR or EHR.

8 . The method of claim 1 , wherein the language pattern recognition comprises, for each episodic record in the plurality of episodic records, matching one or more regular expressions against the corresponding unstructured clinical data, thereby identifying the sub-plurality of episodic records.

9 - 10 . (canceled)

11 . The method of claim 1 , wherein the splitting of the corresponding unstructured clinical data is performed prior to the filtering of the plurality of episodic records.

12 . The method of claim 1 , wherein the splitting of the corresponding unstructured clinical data is performed after the filtering of the plurality of episodic records.

13 . The method of claim 1 , wherein each snippet in the corresponding plurality of snippets has approximately a same number of tokens.

14 . (canceled)

15 . The method of claim 1 , wherein, for a respective episodic record in the sub-plurality of episodic records, the splitting the corresponding unstructured clinical data comprises:

tokenizing the corresponding unstructured clinical data to obtain a plurality of tokens, segmenting the plurality of tokens to obtain a plurality of segments, wherein each segment in the plurality of segments has approximately a same number of tokens;

ranking respective segments in the plurality of segments based on values of tokens within each segment, and

removing one or more respective segments from the plurality of segments based on the ranking, thereby generating the corresponding plurality of snippets for the respective episodic record.

16 . The method of claim 1 , wherein, for each a respective episodic record in the sub-plurality of episodic records, the splitting the corresponding unstructured clinical data comprises:

segmenting the corresponding unstructured clinical data to obtain a plurality of segments, wherein each segment in the plurality of segments comprises a respective portion of the corresponding unstructured clinical data,

tokenizing, in each segment in the plurality of segments, the respective portion of the corresponding unstructured clinical data to obtain a plurality of tokenized segments,

splitting respective tokenized segments, in the plurality of tokenized segments, having a corresponding number of tokens exceeding a threshold number of tokens to obtain a second plurality of tokenized segments;

ranking respective segments in the second plurality of tokenized segments based on values of tokens within each tokenized segment, and

removing one or more respective tokenized segments from the second plurality of tokenized segments based on the ranking, thereby generating the corresponding plurality of snippets for the respective episodic record.

17 . The method of claim 1 , wherein, for each a respective episodic record in the sub-plurality of episodic records, the splitting the corresponding unstructured clinical data comprises:

segmenting the corresponding unstructured clinical data by sentence to obtain a plurality of segments, wherein each segment in the plurality of segments comprises a respective portion of the corresponding unstructured clinical data,

tokenizing, in each segment in the plurality of segments, the respective portion of the corresponding unstructured clinical data to obtain a plurality of tokenized segments,

splitting respective tokenized segments, in the plurality of tokenized segments, having a corresponding number of tokens exceeding a first threshold number of tokens to obtain a second plurality of tokenized segments;

merging respective tokenized segments, in the second plurality of tokenized segments, having a corresponding number of tokens falling below a second threshold number of tokens to obtain a third plurality of tokenized segments;

ranking respective segments in the third plurality of tokenized segments based on values of tokens within each tokenized segment, and

removing one or more respective tokenized segments from the third plurality of tokenized segments based on the ranking, thereby generating the corresponding plurality of snippets for the respective episodic record.

18 - 20 . (canceled)

21 . The method of claim 1 , wherein the first portion of the classifier comprises a multi-head encoder that outputs, for each snippet in the plurality of corresponding snippets for each episodic record in the sub-plurality of episodic records, a corresponding contextualized token tensor for each token in the corresponding set of one or more tokens, thereby forming a corresponding plurality of corresponding contextualized token tensors for the respective snippet.

22 . The method of claim 21 , wherein the first portion of the classifier further comprises a multi-headed intra-attention mechanism that aggregates, for each episodic record in the sub-plurality of episodic records, the corresponding plurality of corresponding contextualized token tensors for each snippet in the plurality of corresponding snippets to output a corresponding contextualized snippet tensor, thereby forming a corresponding plurality of corresponding contextualized snippet tensors for the respective episodic record.

23 . The method of claim 22 , wherein the first portion of the classifier further comprises an inter-attention mechanism that aggregates, for each episodic record in the sub-plurality of episodic records, the corresponding plurality of corresponding contextualized snippet tensors to output a corresponding contextualized episodic record tensor for the respective episodic record.

24 . The method of claim 23 , wherein the second portion of the classifier comprises a model that outputs, for each episodic record in the sub-plurality of episodic records, the corresponding prediction for whether the respective episodic record represents an instance of the clinical condition in response to inputting the contextualized episodic record tensor for the respective episodic record to the model.

25 . (canceled)

26 . The method of claim 1 , wherein the second portion of the classifier comprises a linear transform that converts a respective output of the first portion of the classifier, for a respective episodic record in the sub-plurality of episodic records, into a corresponding scalar number that is compared to a threshold to output the corresponding prediction.

27 . The method of claim 26 , wherein the linear transform is an affine transform.

28 - 45 . (canceled)

46 . A computer system comprising:

one or more processors; and

a non-transitory computer-readable medium including computer-executable instructions that, when executed by the one or more processors, cause the processors to perform a method for phenotyping clinical data, the method comprising:

obtaining, in electronic form, a plurality of episodic records, wherein each episodic record in the plurality of episodic records comprises corresponding unstructured clinical data from an electronic medical record (EMR) or electronic health record (EHR) for a respective patient in a plurality of patients;

filtering the plurality of episodic records by language pattern recognition to identify a sub-plurality of episodic records that each includes an expression related to a clinical condition in the corresponding unstructured clinical data;

splitting, for each episodic record in the sub-plurality of episodic records, the corresponding unstructured clinical data into a corresponding plurality of snippets, wherein each snippet in the corresponding plurality of snippets comprises a corresponding set of one or more tokens; and

predicting, for each episodic record in the sub-plurality of episodic records, if the respective episodic record represents an instance of the clinical condition by inputting the corresponding plurality of snippets for the respective episodic record to a classifier comprising a first portion and a second portion, wherein the first portion comprises an aggregation function that aggregates the corresponding plurality of snippets to output a corresponding representation for the respective episodic record, and wherein the second portion interprets the corresponding representation to output a corresponding prediction for whether the respective episodic record represents an instance of the clinical condition.

47 . A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to perform a method for phenotyping clinical data, the method comprising:

obtaining, in electronic form, a plurality of episodic records, wherein each episodic record in the plurality of episodic records comprises corresponding unstructured clinical data from an electronic medical record (EMR) or electronic health record (EHR) for a respective patient in a plurality of patients;

filtering the plurality of episodic records by language pattern recognition to identify a sub-plurality of episodic records that each includes an expression related to a clinical condition in the corresponding unstructured clinical data;

splitting, for each episodic record in the sub-plurality of episodic records, the corresponding unstructured clinical data into a corresponding plurality of snippets, wherein each snippet in the corresponding plurality of snippets comprises a corresponding set of one or more tokens; and

predicting, for each episodic record in the sub-plurality of episodic records, if the respective episodic record represents an instance of the clinical condition by inputting the corresponding plurality of snippets for the respective episodic record to a classifier comprising a first portion and a second portion, wherein the first portion comprises an aggregation function that aggregates the corresponding plurality of snippets to output a corresponding representation for the respective episodic record, and wherein the second portion interprets the corresponding representation to output a corresponding prediction for whether the respective episodic record represents an instance of the clinical condition.