Patent ID: 11857322
Assignee: NEUMORA THERAPEUTICS, INC.
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 0:
1. A system for evaluating a patient for mental health issues, 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 plurality of input features;
process the received plurality of input features using a Bayesian Decision List to assign a probability of a potential indication of mental health of the patient;
based at least in part on the assigned probability of the potential indication, output an indication of mental health of the patient irrespective of a mental health condition, the indication of mental health identifying a drug to which the patient would likely be a higher responder; and
in response to the outputted indication of mental health of the patient, recommend an effective amount of the identified drug to be administered to the patient,
wherein the Bayesian Decision List was generated by:
receiving labeled training data comprising data for a plurality of individuals, the labeled training data including category labels indicative of (i) whether each of the plurality of individuals has one or more mental health disorders and (ii) at least one drug to which each of the plurality of individuals would likely be a higher responder, the labeled training data comprising a plurality of attributes;
based at least in part on the received labeled training data, generating a plurality of rules predicting a category label associated with a set of attributes of the plurality of attributes;
calculating a confidence score for each of the generated plurality of rules, the confidence score being representative of a capacity to predict the category label;
eliminating one or more rules of the generated plurality of rules based at in part on a threshold capacity to predict the category label; and
generating the Bayesian Decision List designed to predict the category label using the rules that are not eliminated from the plurality of rules.