Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

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 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 for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based. on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

Abstract: Substantially encapsulated micronutrient granules for fortification of an edible salt composition are disclosed. Said encapsulated micronutrient granules comprise granules comprising 0.1 to 20% of at least one micronutrient and 1 to 99% of at least one binding agent selected from a group consisting of a fatty acid, cellulose derivative and sugar, encapsulated by an outer coating comprising a fatty acid and cellulose derivative.

Abstract: A method for providing an ophthalmic lens to a wearer including: providing the wearer's astigmatism features measured at a first proximity; providing the wearer's astigmatism features measured at a second proximity, wherein the second proximity is greater than the first proximity; determining customized wearer's astigmatism features based on the combination resulting of linear combinations of the wearer's astigmatism features measured at the first proximity and of the wearer's astigmatism features measured at the second proximity; and applying the customized astigmatism features to the ophthalmic lens.

Abstract: A method for monitoring a visual capacity modification parameter of a user of at least one display device, the visual capacity modification parameter being indicative of the modification of a parameter linked to the visual capacity of the user related to the use of the at least one display device, the method includes a display use data determining step during which display use data indicative of a use of the at least one display device by the user are determined, a visual capacity modification parameter determining step during which a modification parameter of the visual capacity of the user is determined based on said display use data, and an information generating step during which an information based on said modification parameter of the visual capacity of the user is generated.

Abstract: A method for providing an ophthalmic lens to a wearer including: providing the wearer's astigmatism features measured at a first proximity; providing the wearer's astigmatism features measured at a second proximity, wherein the second proximity is greater than the first proximity; determining customized wearer's astigmatism features based on the combination resulting of linear combinations of the wearer's astigmatism features measured at the first proximity and of the wearer's astigmatism features measured at the second proximity; and applying the customized astigmatism features to the ophthalmic lens.

Abstract: A method for monitoring a visual capacity modification parameter of a user of at least one display device, the visual capacity modification parameter being indicative of the modification of a parameter linked to the visual capacity of the user related to the use of the at least one display device, the method includes a display use data determining step during which display use data indicative of a use of the at least one display device by the user are determined, a visual capacity modification parameter determining step during which a modification parameter of the visual capacity of the user is determined based on said display use data, and an information generating step during which an information based on said modification parameter of the visual capacity of the user is generated.

Abstract: The invention relates to sulphur- and/or selenium-containing compounds that can inhibit the enzyme inositol mono-phosphatase (IMPase), and which can be used in treating conditions that are treatable through inhibition of this enzyme, such as bi-polar disorder, the compounds having a structure of Formula (I), or is a pharmaceutically acceptable salt thereof; Formula (I); in which: E is S or Se and each of phenyl rings A and B is optionally substituted with one or more substituents, in which each substituent is selected independently from: (1) a halogen, which is preferably selected from F, Cl and Br; (2) C1-C4 alkyl, such as C1-C2 alkyl or C1 alkyl, optionally substituted with one or more halogen atoms, each of which is preferably selected from F, Cl and Br; and (3) C1-C4 alkoxy, such as C1-C2 alkoxy or C1 alkoxy; optionally substituted with one or more halogen atoms, each of which is preferably selected from F, Cl and Br.

Abstract: The invention relates to sulphur- and/or selenium-containing compounds that can inhibit the enzyme inositol mono-phosphatase (IMPase), and which can be used in treating conditions that are treatable through inhibition of this enzyme, such as bi-polar disorder, the compounds having a structure of Formula (I), or is a pharmaceutically acceptable salt thereof; Formula (I); in which: E is S or Se and each of phenyl rings A and B is optionally substituted with one or more substituents, in which each substituent is selected independently from: (1) a halogen, which is preferably selected from F, Cl and Br; (2) C1-C4 alkyl, such as C1-C2 alkyl or C1 alkyl, optionally substituted with one or more halogen atoms, each of which is preferably selected from F, Cl and Br; and (3) C1-C4 alkoxy, such as C1-C2 alkoxy or C1 alkoxy; optionally substituted with one or more halogen atoms, each of which is preferably selected from F, Cl and Br.