System and method for identifying transdiagnostic features shared across mental health disorders

1. A system for evaluating a patient for mental health issues, the system comprising:

a display device;

a user interface;

a memory containing machine readable medium comprising machine executable code having stored thereon instructions for performing a method; and

a control system coupled to the memory comprising one or more processors, the control system configured to execute the machine executable code to cause the control system to:

display, on the display device, a series of questions from mental health questionnaires comprising text and answers for each question;

receive, from the user interface, a selection of answers from a patient of each of the series of questions;

receive, unprocessed MRI data corresponding to a set of MRI images of a biological structure associated with the patient;

process, using a machine learning model, the selection of answers and the unprocessed MRI data to output a mental health indication of the patient, and

display, on the display device, the output of the processing, wherein the output is the mental health indication of the patient

wherein the machine learning model was generated by:

receiving labeled training data for a plurality of individuals indicating whether each of the plurality of individuals has one or more mental health disorders, the labeled training data comprising:

MRI data recorded for each of the plurality of individuals; and

a selection of answers to the series of questions for each of the plurality of individuals;

determining a plurality of features from the labeled training data;

training an initial machine learning model in a supervised manner, based on the plurality of features;

extracting importance measures for each of the plurality of features, based on the training of the initial machine learning model;

generating a plurality of subset machine learning models based on the extracted importance measures for the plurality of features;

evaluating a classification performance of the generated plurality of subset machine learning models; and

selecting at least one of the subset machine learning models as the machine learning model.

2. The system of claim 1 , wherein the machine learning model is trained on clinical scales data corresponding to the plurality of individuals.

3. The system of claim 1 , wherein the machine learning model is trained on fMRI full connectivity data corresponding to the plurality of individuals.

4. The system of claim 1 , wherein the machine learning model is trained on sMRI data corresponding to the plurality of individuals, the sMRI data comprising cortical volume data, cortical thickness data, and cortical surface area data.

5. The system of claim 1 , wherein the machine learning model is trained on input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises clinical scales data and fMRI data.

6. The system of claim 1 , wherein the machine learning model is trained on input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises clinical scales data and sMRI data.

7. The system of claim 1 , wherein the machine learning model is trained on input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises fMRI data and sMRI data.

8. The system of claim 1 , wherein the machine learning model is trained on input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises fMRI data, clinical scales data, and sMRI data.

9. A system for evaluating mental health of patients, the system comprising:

a memory containing machine readable medium comprising machine executable code having stored thereon instructions for performing a method; and

a control system coupled to the memory comprising one or more processors, the control system configured to execute the machine executable code to cause the control system to:

receive a selection of answers associated with a patient, the selection of answers corresponding to each question in a series of questions from mental health questionnaires;

receive, unprocessed MRI data corresponding to a set of MRI images of a biological structure associated with the patient;

process the unprocessed MRI data to output a set of MRI features;

process, using a machine learning model, the selection of answers, and the set of MRI features, to output a mental health indication of the patient; and

display, on a display device, the output of the processing, wherein the output is the mental health indication of the patient

wherein the machine learning model was generated by:

receiving labeled training data for a plurality of individuals indicating whether each of the plurality of individuals has one or more mental health disorders, the labeled training data comprising:

MRI data recorded for each of the plurality of individuals; and

a selection of answers to the series of questions for each of the plurality of individuals;

determining a plurality of features from the labeled training data;

training an initial machine learning model in a supervised manner, based on the plurality of features;

extracting importance measures for each of the plurality of features, based on the training of the initial machine learning model;

generating a plurality of subset machine learning models based on the extracted importance measures for the plurality of features;

evaluating a classification performance of the generated plurality of subset machine learning models; and

selecting at least one of the subset machine learning models as the machine learning model.