Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Techniques are disclosed herein for generating predictions of gut microbiome diversity for a user. In some examples, a nutritional service may utilize data associated with a gut transit time and user data to generate a prediction of gut microbiome diversity for a user. For example, the nutritional service may perform an analysis of the data associated with gut transit time and the answers to questions to generate the prediction. In some examples, the nutritional service identifies a uniqueness of the microbiome, identify interesting species, and the like. The information determined and/or otherwise generated by the nutritional service may be presented to the user on a display.

To provide patient status determination device, which receives continuous biological information from a state detection device that continuously detects biological information of a patient and measured biological information from a measuring device that measures biological information of the patient, appropriately determines the state of the patient, based on the received continuous biological information and the measured biological information, whereby it is possible to determine the state of the patient properly, based on the plurality of biological information of the patient.

To provide patient status determination device, which receives continuous biological information from a state detection device that continuously detects biological information of a patient and measured biological information from a measuring device that measures biological information of the patient, appropriately determines the state of the patient, based on the received continuous biological information and the measured biological information, whereby it is possible to determine the state of the patient properly, based on the plurality of biological information of the patient.

Methods and systems for estimating causal effects from knowledge graphs are provided. The method includes obtaining intervention application data and subject history data for a candidate group of subjects, and dividing, based on the intervention application data, the candidate group into a reception subgroup that received an intervention and a rejected subgroup that did not receive the intervention. The method includes for each subject within the candidate group, mapping, based on the subject history data, a covariate value set onto a knowledge graph with an embedding neural network; and for each subject in the reception subgroup or the rejection subgroup, translating the covariate value sets for the subjects within the reception subgroup or the rejection subgroup into a reception matrix or a rejection matrix with a feature neural network. The method includes comparing the reception subgroup to the rejection subgroup to determine a differential intervention effect.

The embodiments of the present application relate to providing a distributed network-based system that allows users to search for potential healthcare providers that satisfy certain criteria and to dynamically identify healthcare providers that best meet a user's particular needs as defined by each specific search. In embodiments, the system creates experience score for each particular provider who matches the search criteria. The experience score is dynamically determined based on the type of search that is performed (e.g., search by specialty or search by condition or procedure), the query terms used in the search, and other factors including for example, the medical specialty/specialties the provider practices relative to the search performed, evidence the provider treats a condition and/or performs a procedure that matches the consumer's search, patient volume for the searched condition or procedure, total volume of patients, board certification(s) relevant to searched performed, disciplinary action information, malpractice claims history, and degree level attained by the healthcare provider. In other embodiments, the system dynamically ranks healthcare providers within a search results list from best choice to worst choice based on several factors including, for example, the type of search, the query terms used in the search, the quantity of providers who match the query, the locations of the providers who match the query, and the quality and other characteristics of providers who match the search query.

The embodiments of the present application relate to providing a distributed network-based system that allows users to search for potential healthcare providers that satisfy certain criteria and to dynamically identify healthcare providers that best meet a user's particular needs as defined by each specific search. In embodiments, the system creates experience score for each particular provider who matches the search criteria. The experience score is dynamically determined based on the type of search that is performed (e.g., search by specialty or search by condition or procedure), the query terms used in the search, and other factors including for example, the medical specialty/specialties the provider practices relative to the search performed, evidence the provider treats a condition and/or performs a procedure that matches the consumer's search, patient volume for the searched condition or procedure, total volume of patients, board certification(s) relevant to searched performed, disciplinary action information, malpractice claims history, and degree level attained by the healthcare provider. In other embodiments, the system dynamically ranks healthcare providers within a search results list from best choice to worst choice based on several factors including, for example, the type of search, the query terms used in the search, the quantity of providers who match the query, the locations of the providers who match the query, and the quality and other characteristics of providers who match the search query.

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.