Abstract: A model-assisted system for processing data to extract a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of dates represented in at least one document included in the medical record; identify a plurality of snippets of information included in the at least one document, each snippet of the plurality of snippets being associated with a date of the plurality of dates; inputting the plurality of snippets into a machine learning model, the machine learning model having been trained to determine associations between dates and patient events based on a training set of snippet data; and determine whether each date of the plurality of dates is associated with a patient event based on an output of the machine learning model.

Abstract: A model-assisted system for identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Abstract: A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets; identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

Abstract: A method by one or more computing devices implementing a data insights sharing service to allow a first user of the data insights sharing service to share data insights with other users of the data insights sharing service. The method includes storing metadata describing one or more data insights, where the one or more data insights were generated based on analyzing a dataset of the first user, responsive to receiving a request from a second user to access the one or more data insights, generating the one or more data insights based on the metadata describing the one or more data insights without accessing the dataset, and providing the one or more data insights to the second user via a graphical user interface (GUI) of the data insights sharing service.

Abstract: A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets, identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

Abstract: A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

Abstract: A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

Abstract: 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.

Abstract: 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.

Abstract: A method by one or more computing devices implementing a data insights sharing service to allow a first user of the data insights sharing service to share data insights with other users of the data insights sharing service. The method includes storing metadata describing one or more data insights, where the one or more data insights were generated based on analyzing a dataset of the first user, responsive to receiving a request from a second user to access the one or more data insights, generating the one or more data insights based on the metadata describing the one or more data insights without accessing the dataset, and providing the one or more data insights to the second user via a graphical user interface (GUI) of the data insights sharing service.

Abstract: A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets, identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

Abstract: A model-assisted system for identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Abstract: 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.

Abstract: System and methods are described to parse input source code and generate a tree representing the input source code, optimize the tree by determining reusable sub-trees of the tree and replacing the reusable sub-trees with variables, and transpile the optimized tree to generate output source code.

Abstract: Systems and methods for executing a formula written using a formula language may include receiving a formula written using a formula language, the formula associated with a record of a database; transpiling the formula in the formula language to a program code in a programming language; and executing the formula based on the program code.

Abstract: Systems and methods for generating and testing formulas written using a formula language may include receiving, by a server computing system, in a formula input area a formula written using a formula language, the formula associated with a record in a database; displaying, by the server computing system, one or more fields in a field area based on the one or more fields being included in the formula, the one or more fields associated with the record; enabling, by the server computing system, a value associated with the one or more fields to be modified based on the one or more fields being included in the formula; and displaying, by the server computing system, in a result area a result generated based on execution of the formula, wherein the result area is to be visible concurrently with the formula input area and the field area.

Abstract: System and methods are described to parse input source code and generate a tree representing the input source code, optimize the tree by determining reusable sub-trees of the tree and replacing the reusable sub-trees with variables, and transpile the optimized tree to generate output source code.

Abstract: A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

Abstract: Systems and methods for executing a formula written using a formula language may include receiving a formula written using a formula language, the formula associated with a record of a database; transpiling the formula in the formula language to a program code in a programming language; and executing the formula based on the program code.

Abstract: Systems and methods for generating and testing formulas written using a formula language may include receiving, by a server computing system, in a formula input area a formula written using a formula language, the formula associated with a record in a database; displaying, by the server computing system, one or more fields in a field area based on the one or more fields being included in the formula, the one or more fields associated with the record; enabling, by the server computing system, a value associated with the one or more fields to be modified based on the one or more fields being included in the formula; and displaying, by the server computing system, in a result area a result generated based on execution of the formula, wherein the result area is to be visible concurrently with the formula input area and the field area.