Abstract: Methods, non-transitory computer-readable media, and devices are disclosed that identify and validate a signature for predicting relative benefit of two therapies for frontline and therapy sequencing for patients. Described and illustrated by way of the example herein are machine learning approaches for gaining data-driven insights from the mutational landscape in metastatic pancreatic adenocarcinoma (mPDAC) and validating the signature in predicting relative benefit from FOLFIRINOX (FFX) and Gemcitabine/nab-Paclitaxel (GA) therapies. This technology inputs a patient's genomics findings and clinical data and generates predictions of relative effectiveness for the two distinct FFA and GA chemotherapy options. The predictions for an individual patient provide personalized guidance on treatment sequencing to improve patient health outcomes.

Abstract: A method and system are disclosed for performing supervised outcome-driven persona-typing, including receiving a first data set having patient specific data, treatment data, and outcome data for each of a plurality of first patients. Once received, the plurality of first patients are grouped based on the first data set, and a persona for each of patient groups is generated. A second data set may then be received for a second patient including second patient specific data. A comparison is then carried out on the second data set to identify an existing persona, and then associate the second patient with the identified persona. Based on each of the above steps, a patient care plan is then created.

Abstract: A method and system for determining a recommendation for drug treatment are described herein. For example, the method includes determining drug scores based upon network-based distances for one or more target drug nodes, modeling one or more outputs based upon input data, wherein the input data comprises at least a portion of the drug scores, selecting an algorithmic output from the one or more modeling outputs based upon at least one performance criteria, determining if the selected algorithmic output of the modeling satisfies a threshold, and if the selected algorithmic output satisfies the threshold, generating the recommendation for drug treatment. The system includes a processing device and computer readable medium including programming instructions that, when executed, cause the processing device to perform the method as described herein.

Abstract: A method and system for determining a recommendation for drug treatment are described herein. For example, the method includes determining drug scores based upon network-based distances for one or more target drug nodes, modeling one or more outputs based upon input data, wherein the input data comprises at least a portion of the drug scores, selecting an algorithmic output from the one or more modeling outputs based upon at least one performance criteria, determining if the selected algorithmic output of the modeling satisfies a threshold, and if the selected algorithmic output satisfies the threshold, generating the recommendation for drug treatment. The system includes a processing device and computer readable medium including programming instructions that, when executed, cause the processing device to perform the method as described herein.

Abstract: A method and system are disclosed for performing supervised outcome-driven persona-typing, including receiving a first data set having patient specific data, treatment data, and outcome data for each of a plurality of first patients. Once received, the plurality of first patients are grouped based on the first data set, and a persona for each of patient groups is generated. A second data set may then be received for a second patient including second patient specific data. A comparison is then carried out on the second data set to identify an existing persona, and then associate the second patient with the identified persona. Based on each of the above steps, a patient care plan is then created.

Abstract: A method and system for generating a therapy plan for a patient is described. The method includes providing a knowledgebase of annotated disease treatment information, a patient history database including disease types for a plurality of patients and a molecular database including molecular data associated with patients, selecting at least one of the plurality of patients for analysis, identifying a list of one or more candidate therapy plans from the knowledgebase based on at least one of the disease type and the molecular data for the at least one patient, and scoring each of the one or more candidate therapy plans by combining a molecular vector, a disease-specific vector and a patient history vector. The therapy plan with the highest score can then be selected for implementation. The system includes various hardware components for implementing the various functions as defined by the method for generating the treatment plan.