Methods and systems for holistic medical student and medical residency matching

1. A method for holistic medical student and medical residency matching, comprising:

generating, by a processor, an applicant profile, wherein generating the applicant profile comprises:

receiving applicant data;

generating a textual query for the applicant data;

applying the textual query to the applicant data,

extracting a textual output based at least on the textual query; and

producing the applicant profile based at least on the textual output of the textual query; and

determining, by the processor, a diversity score, wherein determining the diversity score comprises:

parsing the applicant profile for diversity data associated with predetermined diversity indices;

extracting the diversity data associated with the predetermined diversity indices;

converting the diversity data associated with the predetermined diversity indices into numerical diversity values; and

calculating, based at least on the numerical diversity values, the diversity score; and

determining, by the processor, a competency score, wherein determining the competency score comprises:

parsing the applicant profile for competency data associated with predetermined competency indices;

extracting the competency data associated with the predetermined competency indices;

converting the competency data associated with the predetermined competency indices into numerical competency values; and

calculating, based at least on the numerical competency values, the competency score; and

calculating, by the processor, a representative score for an applicant based at least on the diversity score and the competency score, wherein the calculation comprises:

training a machine learning model using training data, wherein the machine learning model comprises at least a neural network, wherein the training data contains a plurality of data entries containing a plurality of applicant diversity scores and applicant competency scores as inputs correlated to a plurality of representative scores as outputs; and

calculating the representative score for the applicant as a function of the applicant diversity score and the applicant competency score using a trained machine learning model, wherein calculating the representative score comprises:

calculating a cross product of a diversity score vector and a competency score vector, wherein:

the diversity score vector has a first number of indices and the competency score vector has a second number of indices;

the first number of indices and the second number of indices are different; and

the cross product is calculated by adding a number of zero index values equal to the difference between the first number of indices and the second number of indices to:

the diversity score vector if the first number of indices is less than the second number of indices, or

the competency score vector if the first number of indices is greater than the second number of indices; and

outputting the representative score in the form of a vector; and

presenting, on a graphical user interface (GUI), a graphical representation of the representative score.

2. The method of claim 1 , wherein calculating the representative score comprises utilizing a Sullivan's Composite Diversity Index.

3. The method of claim 1 , wherein calculating the representative score comprises utilizing a Simpson's Diversity Index.

4. The method of claim 1 , wherein determining the diversity score comprises utilizing statistical weighting of the predetermined diversity indices.

5. The method of claim 4 , wherein the statistical weighting of the predetermined diversity indices includes a Wilcoxon rank sum test.

6. The method of claim 4 , wherein the statistical weighting of the predetermined diversity indices includes a consensus method to assign the weight of each of the predetermined diversity indices.

7. The method of claim 1 , wherein an applicant ranking score is a function of a maximum possible competency score.

8. The method of claim 1 , wherein an applicant ranking score is a function of a maximum possible diversity score.

9. A system for holistically ranking medical student and medical residency matching, wherein the system 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:

generate an applicant profile, wherein generating the applicant profile causes the processor to comprises a language processing module:

receive applicant data, wherein the applicant data is in a textual format;

generate a textual query for the applicant data;

apply the textual query to the applicant data;

extract a textual output based at least on the textual query; and

produce the applicant profile based at least on the textual output of the textual query; and

determine a diversity score, wherein determining the diversity score causes the processor to:

parse the applicant profile for diversity data associated with predetermined diversity indices;

convert the diversity data into the predetermined diversity indices; and

calculate, based at least on the predetermined diversity indices, the diversity score; and

determine a competency score, wherein determining the competency score causes the processor to:

parse the applicant profile for competency data associated with predetermined competency indices;

convert the competency data into the predetermined competency indices;

calculate, based at least on the predetermined competency indices, the competency score; and

calculate a representative score for an applicant based at least on the diversity score and the competency score, wherein the calculation comprises:

training a machine learning model using training data, wherein the machine learning model comprises at least a neural network, wherein the training data contains a plurality of data entries containing a plurality of applicant diversity scores and competency scores as inputs correlated to a plurality of representative scores as outputs; and

calculating the representative score for the applicant as a function of the diversity score and the competency score, wherein calculating the representative score comprises:

calculating a cross product of a diversity score vector and a competency score vector, wherein:

the diversity score vector has a first number of indices and the competency score vector has a second number of indices;

the first number of indices and the second number of indices are different; and

the cross product is calculated by adding a number of zero index values equal to the difference between the first number of indices and the second number of indices to:

 the diversity score vector if the first number of indices is less than the second number of indices, or

 the competency score vector if the first number of indices is greater than the second number of indices; and

outputting the representative score in the form of a vector; and

present, on a graphical user interface (GUI), a graphical representation of the representative score.

10. The system of claim 9 , wherein the processor utilizes Sullivan's Composite Diversity Index to calculate the representative score.

11. The system of claim 9 , wherein the processor utilizes Simpson's Diversity Index to calculate the representative score.

12. The system of claim 9 , wherein the diversity score comprises statistical weighting of the predetermined diversity indices.

13. The system of claim 12 , wherein the statistical weighting of the predetermined diversity indices includes a Wilcoxon rank sum test.

14. The system of claim 12 , wherein the statistical weighting of the predetermined diversity indices includes a consensus method to assign the weight of each of the predetermined diversity indices.

15. The system of claim 9 , wherein an applicant ranking score is a function of a maximum possible competency score.

16. The system of claim 9 , wherein an applicant ranking score is a function of a maximum possible diversity score.