A tracking system includes a processor, a communications network, a blockchain, and a cultivator device. Growth data on each of the growing states of a cultivated cannabis batch is generated and stored on the blockchain. An intermediary device generates batch segment data having growth data of the cultivated cannabis batch and post-cultivation processing data for that batch segment. Unique identifiers for an end-user product include a link to stored consumer data on the blockchain that includes a) attestation of the authenticity of the end user product, and b) batch segment data for that batch segment to which the end user product belongs. A consumer device scans an end-user unique identifier and retrieves the consumer data from the blockchain and transmits an efficacy value regarding efficacy to the consumer of the end user product.

A tracking system includes a processor, a communications network, a blockchain, and a cultivator device. Growth data on each of the growing states of a cultivated cannabis batch is generated and stored on the blockchain. An intermediary device generates batch segment data having growth data of the cultivated cannabis batch and post-cultivation processing data for that batch segment. Unique identifiers for an end-user product include a link to stored consumer data on the blockchain that includes a) attestation of the authenticity of the end user product, and b) batch segment data for that batch segment to which the end user product belongs. A consumer device scans an end-user unique identifier and retrieves the consumer data from the blockchain and transmits an efficacy value regarding efficacy to the consumer of the end user product.

A system and method of diagnosing tissue integrity related to a joint of a patient may include imaging a first bone of the joint of the patient, determining a bone density profile of the first bone based on results of the imaging step, comparing the bone density profile of the first bone to at least one reference bone density profile of a reference first bone, and predicting an integrity of a tissue with respect to the first bone based on the comparison. The first bone may be a tibia and the bone density profile of the tibia may include a bone density profile of a sulcus of a medial tibial condyle of the tibia. The tissue may be an anterior cruciate ligament (“ACL”) and the predicting step may include predicting the integrity of both an anteromedial bundle and a posterolateral bundle of the ACL.

A system and method of diagnosing tissue integrity related to a joint of a patient may include imaging a first bone of the joint of the patient, determining a bone density profile of the first bone based on results of the imaging step, comparing the bone density profile of the first bone to at least one reference bone density profile of a reference first bone, and predicting an integrity of a tissue with respect to the first bone based on the comparison. The first bone may be a tibia and the bone density profile of the tibia may include a bone density profile of a sulcus of a medial tibial condyle of the tibia. The tissue may be an anterior cruciate ligament (“ACL”) and the predicting step may include predicting the integrity of both an anteromedial bundle and a posterolateral bundle of the ACL.

Methods and systems for guiding a patient for a medical examination using a medical apparatus. For example, a computer-implemented method for guiding a patient for a medical examination using a medical apparatus includes: receiving an examination protocol for the medical apparatus; determining a reference position based at least in part on the examination protocol; acquiring a patient position; determining a deviation metric based at least in part on comparing the patient position and the reference position; determining whether the deviation metric is greater than a pre-determined deviation threshold; and if the deviation metric is greater than a pre-determined deviation threshold: generating a positioning guidance based at least in part on the determined deviation metric, the positioning guidance including guidance for positioning the patient relative to the medical apparatus.

Methods and systems for guiding a patient for a medical examination using a medical apparatus. For example, a computer-implemented method for guiding a patient for a medical examination using a medical apparatus includes: receiving an examination protocol for the medical apparatus; determining a reference position based at least in part on the examination protocol; acquiring a patient position; determining a deviation metric based at least in part on comparing the patient position and the reference position; determining whether the deviation metric is greater than a pre-determined deviation threshold; and if the deviation metric is greater than a pre-determined deviation threshold: generating a positioning guidance based at least in part on the determined deviation metric, the positioning guidance including guidance for positioning the patient relative to the medical apparatus.

Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

A method and apparatus can include a system controller and a system processor. The system controller can retrieve a health science related dataset from at least one database, the retrieved dataset including information associated with at least one of a patient medical information, healthcare provider clinical information, health related publications and treatment information, and pharmaceutical information, and transmit to a user equipment a recommendation of at least one of health diagnosis and treatment for a patient. The system processor can utilize multidimensional nonlinear manifold clustering on at least one element from the retrieved dataset, assign an entity formulated from the at least one element of the retrieved dataset into a decision hyper-volume based on the multidimensional nonlinear manifold clustering, and determine the recommendation of at least one of health diagnosis and treatment for the patient based on the assignment of the entity into the decision hyper-volume.

A method and apparatus can include a system controller and a system processor. The system controller can retrieve a health science related dataset from at least one database, the retrieved dataset including information associated with at least one of a patient medical information, healthcare provider clinical information, health related publications and treatment information, and pharmaceutical information, and transmit to a user equipment a recommendation of at least one of health diagnosis and treatment for a patient. The system processor can utilize multidimensional nonlinear manifold clustering on at least one element from the retrieved dataset, assign an entity formulated from the at least one element of the retrieved dataset into a decision hyper-volume based on the multidimensional nonlinear manifold clustering, and determine the recommendation of at least one of health diagnosis and treatment for the patient based on the assignment of the entity into the decision hyper-volume.

A method for answering questions posed by a user, the method comprising: receiving from a medical conversational user interface a user-generated natural language medical information query at an artificial intelligence-based medical conversation cognitive agent; extracting a medical question; compiling a medical conversation language sample; extracting internal medical concepts and medical data from the sample; inferring a therapeutic intent of the user; generating a therapeutic paradigm logical framework, wherein logical framework comprises medical logical progression paths from the medical question to respective therapeutic answers, each of the logical progression paths includes medical logical linkages from the medical question to a therapeutic path-specific answer, and the medical logical linkages include the internal medical concepts and external therapeutic paradigm concepts derived from a store of medical subject matter ontology data; selecting a likely medical information path based upon the therapeutic intent of the user; and answering the medical question.