Apparatus and a method for generating a diagnostic label
An apparatus for generating a diagnostic label is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of electrocardiogram signals and a plurality of electronic health records from a user. The memory instructs the processor to generate a plurality of structured electronic health records using the plurality of electronic health records. The memory instructs the processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model. The memory instructs the processor to generate a diagnostic label as a function of the plurality of representations. The memory instructs the processor to display the diagnostic label using a display device.
1. An apparatus for generating a diagnostic label, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive a plurality of electrocardiogram signals from a user;
receive a plurality of electronic health records from the user, wherein the plurality of electronic health records includes a plurality of metadata, wherein receiving the plurality of electronic health records further comprises:
utilizing optical character recognition (OCR) to convert the plurality of electronic health records into machine-encoded text; and
extracting features from the plurality of electronic health records to reduce a dimensionality of a representation of the plurality of health records;
generate a plurality of structured electronic health records using the plurality of electronic health records, wherein the plurality of structured electronic health records includes a plurality of diagnostic codes, wherein generating the plurality of structured electronic health records further comprises:
classifying health data in the plurality of diagnostic codes comprising at least a corresponding time code representing a medical condition of the user within a given time period;
generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model, wherein
generating the plurality of representations comprises:
generating representation training data, wherein generating the representation training data comprises:
inputting electrocardiogram signal representations with dummy pixels into the representation machine learning model; and
outputting the representation training data comprising the electrocardiogram signal representations with the dummy pixels replaced with filled-in values;
training the representation machine learning model using representation training data; and
generating the plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using the trained representation machine learning model;
generate a diagnostic label as a function of the plurality of representations; and
display the diagnostic label using a display device.
2. The apparatus of claim 1 , wherein the plurality of representations comprises a first representation.
3. The apparatus of claim 1 , wherein the plurality of representations comprises a second representation.
4. The apparatus of claim 1 , wherein the plurality of representations comprises a third representation.
5. The apparatus of claim 1 , wherein the plurality of metadata comprises a plurality of textual data.
6. The apparatus of claim 1 , wherein generating the plurality of structured electronic health records comprises generating the plurality of structured electronic health records using a structure classifier.
7. The apparatus of claim 1 , wherein the memory further instructs the at least a processor to:
generate a plurality of graphical data as a function of the plurality of representations; and
identify one or more representation clusters as a function of the plurality of graphical data.
8. The apparatus of claim 1 , wherein the memory further instructs the at least a processor to generate a diagnostic report as a function of the diagnostic label.
9. A method for generating a diagnostic label, wherein the method comprises:
receiving, using at least a processor, a plurality of electrocardiogram signals from a user;
receiving, using the at least a processor, a plurality of electronic health records from the user, wherein the plurality of electronic health records includes a plurality of metadata, wherein receiving the plurality of electronic health records further comprises:
utilizing optical character recognition (OCR) to convert the plurality of electronic health records into machine-encoded text; and
extracting features from the plurality of electronic health records to reduce a dimensionality of a representation of the plurality of health records;
generating, using the at least a processor, a plurality of structured electronic health records using the plurality of electronic health records, wherein the plurality of structured electronic health records includes a plurality of diagnostic codes, wherein generating the plurality of structured electronic health records further comprises:
classifying health data in the plurality of diagnostic codes comprising at least a corresponding time code representing a medical condition of the user within a given time period;
generating, using the at least a processor, a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model, wherein generating the plurality of representations comprises:
generating representation training data, wherein generating the representation training data comprises:
inputting electrocardiogram signal representations with dummy pixels into the representation machine learning model; and
outputting the representation training data comprising the electrocardiogram signal representations with the dummy pixels replaced with filled-in values;
training the representation machine learning model using representation training data; and
generating the plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using the trained representation machine learning model;
generating, using the at least a processor, a diagnostic label as a function of the plurality of representations; and
displaying, using the at least a processor, the diagnostic label using a display device.
10. The method of claim 9 , wherein the plurality of representations comprises a first representation.
11. The method of claim 9 , wherein the plurality of representations comprises a second representation.
12. The method of claim 9 , wherein the plurality of representations comprises a third representation.
13. The method of claim 9 , wherein the plurality of metadata comprises a plurality of textual data.
14. The method of claim 9 , wherein the method further comprises generating, using the at least a processor, the plurality of structured electronic health records using a structure classifier.
15. The method of claim 9 , wherein the method further comprises:
generating, using the at least a processor, a plurality of graphical data as a function of the plurality of representations; and
identifying, using the at least a processor, one or more representation clusters as a function of the plurality of graphical data.
16. The method of claim 9 , wherein the method further comprises generating, using the at least a processor, a diagnostic report as a function of the diagnostic label.