Methods and systems for medical record searching with transmittable machine learning

Abstract

An artificial intelligence platform system includes at least a server designed and configured to receive training data. Receiving training data includes receiving a first training set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of user data and at least a correlated first constitutional label. At least a server receives at least a user input datum from a user client device. At least a server generates at least an output as a function of the at least a user input datum and the training data. At least a server retrieves at least a stored user datum as a function of the at least a user input datum and the at least an output. At least a server transmits the at least a stored user datum to a user client device.

What is claimed is: 
     
         1 . An artificial intelligence platform apparatus, the apparatus comprising:
 at least a processor; and   a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
 receive training data wherein receiving training data further comprises receiving a first training data set including a plurality of first data entries, each first data entry of the plurality of first data entries includes at least an element of user data and at least a correlated first prognosis specifying a probable future medical condition; and 
 receive a second training data set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a first prognosis and at least a correlated care provider; 
 receive at least a user input datum from a user client device; 
 generate at least an output as a function of the at least a user input datum and the training data, wherein generating the at least an output comprises: 
 calculate a first machine-learning model as a function of the first training set, wherein the at least a user input datum is an input of the first machine-learning model and the output of the first machine-learning model contains a first prognosis; 
 calculate a second machine-learning model as a function of the second training set, wherein the first prognosis is an input of the second machine-learning model and the output of the second machine-learning model contains a care provider. 
   
     
     
         2 . The apparatus of  claim 1  wherein the at least a processor is configured to generate the at least an output by:
 creating a third machine-learning model using the first training set, wherein the third machine-learning model relates user data to constitutional labels and 
 generating the at least an output as a function of the third machine-learning model and the at least a user input datum. 
 
     
     
         3 . The apparatus of  claim 1 , wherein the at least a user input datum is a current medical problem that user is currently experiencing. 
     
     
         4 . The apparatus of  claim 1 , wherein the first prognosis is associated with a physical condition. 
     
     
         5 . The apparatus of  claim 1  further comprising:
 a parsing module configured to:
 generate at least a query using the at least a user input datum; and 
 retrieve from a database at least a stored user datum as a function of the at least a query. 
 
 
     
     
         6 . The apparatus of  claim 5  further comprising generating a language processing model to match data from an expert textual submission to an existing constitutional label in at least an expert database, wherein the language processing model identifies that the matched data relates to a new constitutional label by comparing the matched data to a nearest existent constitutional label using a machine-learning process and determining that the new constitutional label exists as a function of falling below a threshold number. 
     
     
         7 . The apparatus of  claim 1 , wherein the at least a stored user datum is filtered as a function of the at least a user input datum. 
     
     
         8 . The apparatus of  claim 1 , wherein the at least a processor is further configured to populate one or more fields in a biological extraction database using expert information, which is retrieved from an expert knowledge database. 
     
     
         9 . The apparatus of  claim 1 , wherein the first training data set and the second training data set are updated continuously. 
     
     
         10 . The apparatus of  claim 1 , wherein the expert knowledge database organizes the data stored in the expert knowledge database according to one of more database tables. 
     
     
         11 . A method of utilizing an artificial intelligence platform system the method comprising:
 receiving by a processor, training data, wherein receiving training data further comprises:
 receiving a first training data set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of user data and at least a correlated first prognosis specifying a probable future medical condition; and 
 receiving a second training data set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognosis and at least a correlated care provider; 
   receiving at least a user input datum from a user client device; and   generating at least an output as a function of the at least a user input datum and the training data, wherein generating the output comprises:
 calculating a first machine-learning model as a function of the first training set, wherein the at least a user input datum is an input of the first machine-learning model and the output of the first machine-learning model contains a first prognosis; 
 calculating a second machine-learning model as a function of the second training set, wherein the first prognosis is an input of the second machine-learning model and the output of the second machine-learning model contains a care provider. 
   
     
     
         12 . The method of  claim 11 , wherein generating the at least an output further comprises:
 creating a third machine-learning model using the first training set, wherein the third machine-learning model relates user data to constitutional labels and   generating the at least an output using the third machine-learning model and the at least a user input datum.   
     
     
         13 . The method of  claim 11 , wherein the at least a user input datum is a current medical problem that user is currently experiencing. 
     
     
         14 . The method of  claim 11 , wherein the first prognosis is associated with a physical condition. 
     
     
         15 . The method of  claim 11  further comprising:
 generating at least a query using the at least a user input datum; and 
 retrieving from a database at least a stored user datum as a function of the at least a query. 
 
     
     
         16 . The method of  claim 15  further generating a language processing model to extract and match data from expert textual submissions and expert papers to existing constitutional labels in at least an expert database, wherein the language processing model identifies that the data extracted in at least an expert knowledge database relates to a new constitutional label by comparing the extracted data to a nearest existent constitutional label using a machine-learning process and determining that the new constitutional label exists based on the cosine similarity falling below a threshold number. 
     
     
         17 . The method of  claim 11 , wherein the at least a stored user datum is filtered as a function of the at least a user input datum. 
     
     
         18 . The method of  claim 11 , wherein the at least a processor is further configured to populate one or more fields in a biological extraction database using expert information, which is retrieved from an expert knowledge database. 
     
     
         19 . The method of  claim 11 , wherein the first training data set and the second training data set are updated continuously. 
     
     
         20 . The method of  claim 11 , wherein the expert database organizes the data stored in the expert knowledge database according to one of more database tables.