Abstract: A system, method, and computer-readable medium are disclosed for digital therapeutics directed to patient care specific to a disease. Digital therapeutics (DTx) knowledge models are created and their corpus is validated. Condition and symptom models are created for processing the digital therapeutics (DTx) knowledge models. Clinical and condition models are converted to reconfigurable runtime implementations. Architecture and implementation subsets of knowledge models, language models, condition and symptom models, technology and tools are created. Digital therapeutics (DTx) natural language models are created. Multiple program content versions of natural language content, clinical physician, and condition models are created.

Abstract: A system, method, and computer-readable medium are disclosed for digital therapeutics directed to patient care specific to a disease for digital therapeutics that implement digital deep layer patient profile. Patient related information is presented by receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data. The data is mapped as to intensities, multiple dimensions and time. The mapping is converted to create an unstructured binary data with binary correlations as a digital deep layer patient profile. The digital deep layer patient profile can be processed with machine learning and image processing algorithms.

Abstract: A system, method, and computer-readable medium are disclosed for digital therapeutics directed to patient care specific to a disease for digital therapeutics that implement digital deep layer patient profile. Patient related information is presented by receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data. The data is mapped as to intensities, multiple dimensions and time. The mapping is converted to create an unstructured binary data with binary correlations as a digital deep layer patient profile. The digital deep layer patient profile can be processed with machine learning and image processing algorithms.

Abstract: A system, method, and computer-readable medium to deliver patient reported outcomes and therapeutic recommendations to patients with immunotherapy. Management of adverse events in the treatment of patients implementing a digital therapeutic(s) that enforces a digital care pathway that receives patient data of answers to machine generated questions and patient symptoms, where the patient data is consumed by digital care pathway. The patient data is process with digital therapeutics (DTx) knowledge models that influence the digital care pathway. Recommendations are provided based on the processed data.

Abstract: A system, method, and computer-readable medium are disclosed for digital therapeutics directed to patient care specific to a disease. Digital therapeutics (DTx) knowledge models are created and their corpus is validated. Condition and symptom models are created for processing the digital therapeutics (DTx) knowledge models. Clinical and condition models are converted to reconfigurable runtime implementations. Architecture and implementation subsets of knowledge models, language models, condition and symptom models, technology and tools are created. Digital therapeutics (DTx) natural language models are created. Multiple program content versions of natural language content, clinical physician, and condition models are created.

Abstract: A system, method, and computer-readable medium are disclosed for processing patient related information in a health artificial intelligence system. patient answers are received to health symptom dialog questions and transformed into longitudinal data. The longitudinal data is stored as deep layer patient profile. Primary patient symptoms that are associated with detect knowledge models are processed as they relate to longitudinal data and deep layer patient profile. Laboratory results associated with diagnostic knowledge models are processed as related to the longitudinal data and deep layer patient profile. Physician results associated with diagnostic knowledge models are processed as related to the longitudinal data and deep layer patient profile. Recommendations are provided from the primary symptoms and laboratory results for addressing adverse events and related to the longitudinal data, deep layer patient profile, and knowledge models.