Systems and methods for treatment selection
There is disclosed a method and a system for predicting the efficacy of one or more treatments. A completed questionnaire may be received from a patient requiring treatment. The responses to the questionnaire may be input to a machine learning algorithm (MLA). The MLA may have been trained using labelled patient data. A predicted efficacy of one or more treatments and a prototype corresponding to the patient may be received from the MLA. An interface may be output indicating the predicted efficacy of the one or more treatments and the prototype.
1. A method for predicting a treatment efficacy for a patient implemented with at least one processor equipped with at least one memory device having stored thereon code instructions which, when executed by the at least one processor, cause the at least one processor to perform the method comprising:
training a machine learning algorithm (MLA) by:
receiving datasets from one or more sources corresponding to treatments for mental illness, wherein each data point in the datasets comprises questionnaire data corresponding to a patient and an indication of treatment efficacy corresponding to the respective patient;
transforming results of the datasets, thereby generating normalized results;
generating, based on the normalized results, a training dataset;
selecting one or more features in the training dataset; and
training, using the selected one or more features, the MLA to predict, for input patient data, an efficacy of each of a plurality of treatments, and
predicting the treatment efficacy for the patient by:
receiving questionnaire responses from the patient;
inputting the questionnaire responses into the MLA;
outputting, by the MLA, a predicted efficacy of each of the plurality of treatments for the patient;
generating, based on the predicted efficacy of each of the plurality of treatments, an interface; and
outputting for display the interface.
2. The method of claim 1 , wherein the interface comprises, for each of the plurality of treatments a predicted likelihood of remission.
3. The method of claim 1 , further comprising receiving, via the interface, user input indicating a treatment plan, wherein the treatment plan comprises at least one treatment of the plurality of treatments.
4. The method of claim 1 , wherein the interface comprises an indication of a current treatment of the patient.
5. The method of claim 4 , wherein the interface comprises a predicted likelihood of remission of the current treatment.
6. The method of claim 1 , wherein the interface comprises the plurality of treatments and one or more side effects associated with each treatment of the plurality of treatments.
7. The method of claim 1 , further comprising:
receiving, via the interface, a selection of a treatment of the plurality of treatments;
receiving, via the interface, a selection of a dosage of the treatment; and
outputting for display a second interface comprising the selected treatment and the selected dosage.
8. The method of claim 1 , wherein transforming the results of the datasets comprises grouping questions in different datasets relating to a same feature.
9. The method of claim 1 , wherein transforming the results of the datasets comprises converting categorical responses in the datasets to binary responses.
10. The method of claim 1 , wherein the training is performed using a loss function, wherein the loss function determines a difference between a predicted likelihood of remission and a labeled occurrence of remission.
11. A method implemented with at least one processor equipped with at least one memory device having stored thereon code instructions which, when executed by the at least one processor, cause the at least one processor to perform the method comprising:
receiving questionnaire responses from a patient requiring treatment;
inputting the questionnaire responses into a machine learning algorithm (MLA), wherein the MLA was trained based on labelled patient data, wherein each data point in the labelled patient data comprises questionnaire data corresponding to a respective patient and a label indicating an efficacy of a treatment for the respective patient;
receiving, from the MLA, a predicted efficacy of one or more treatments for the patient;
generating, based on the predicted efficacy of the one or more treatments, an interface; and
outputting for display the interface.
12. The method of claim 11 , wherein the interface comprises, for each of the one or more treatments, a predicted likelihood of remission.
13. The method of claim 11 , further comprising receiving, via the interface, user input indicating a treatment plan, wherein the treatment plan comprises at least one of the one or more treatments.
14. The method of claim 11 , wherein the interface comprises an indication of a current treatment of the patient and a predicted likelihood of remission of the current treatment.
15. The method of claim 11 , wherein the interface comprises an indication of features that were influential in determining the predicted efficacy of the one or more treatments.
16. The method of claim 11 , wherein the interface comprises the one or more treatments and one or more side effects associated with each of the one or more treatments.
17. The method of claim 11 , further comprising:
receiving, via the interface, a selection of a treatment of the one or more treatments;
receiving, via the interface, a selection of a dosage of the treatment; and
outputting for display a second interface comprising the selected treatment and the selected dosage.
18. The method of claim 17 , wherein the second interface comprises a probability of remission corresponding to the selected treatment.
19. A method for training a machine learning algorithm (MLA), the method implemented with at least one processor equipped with at least one memory device having stored thereon code instructions which, when executed by the at least one processor, cause the at least one processor to perform the method comprising:
receiving datasets from one or more sources corresponding to treatments for mental illness, wherein each data point in the datasets comprises questionnaire data corresponding to a patient and an indication of treatment efficacy corresponding to the respective patient;
transforming results of the datasets, thereby generating normalized results;
generating, based on the normalized results, a training dataset;
selecting one or more features in the training dataset; and
training, using the selected one or more features, the MLA to predict, for input patient data, an efficacy of each of the treatments.
20. The method of claim 19 , wherein transforming the results of the datasets comprises grouping questions in different datasets relating to a same feature and converting categorical responses in the datasets to binary responses.