Multimodal biomarkers predictive of transdiagnostic symptom severity
The method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.
1. A system for evaluating mental health of a patient, 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;
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, from the user interface, a 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;
process, using a machine learning model, the selection of answers, and the unprocessed MRI data to output a symptom severity indicator for a mental health category 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 and a severity of symptoms corresponding to the one or more mental health disorders, the labeled training data comprising:
MRI data recorded for each of the plurality of individuals;
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.