Abstract: The disclosure relates to systems and methods of generating physiological state classifications such as sleep classifications. Physiological state classification may refer to a machine-learning model's prediction of a subject's physiological state based on sensor data. In particular, the machine-learning model may generate a sleep classification that represents a prediction of a subject's sleep stage. A sleep stage may refer to whether the subject is awake or asleep (for a binary classification). In some examples, the sleep stage may refer to whether the subject is awake, N1, N2, N3, and Rapid Eye Movement (REM) (for a multi-class classification).

Abstract: Systems and methods are described for automatically inspecting and validating unstructured documents having natural language text, such as journal article describing clinical research. For example, a journal's prose may be parsed to identify domain-specific entities and values. Domain-specific rules may be evaluated against generated structured data storing the entities and their corresponding values. rule may relate to a domain-specific requirement for the document. Findings may be generated for each evaluated rule, indicating whether the document meets a corresponding requirement. Feedback indicating whether a given finding is incorrect or is to be updated, which may indicate that the corresponding rule should be updated or removed, may be obtained. Based on the feedback, the set of domain-specific rules may be updated to obtain an updated set of rules including an update to or deletion of the rule. Some embodiments include automatically validating documents using voice enabled features.

Abstract: Systems and methods are disclosed for data driven document creation and modification. The systems and methods include obtaining a first dataset having data records associated with entities, obtaining a list of entities associated with a first subset of data records in the first dataset, and obtaining configuration information, wherein the configuration information includes rules for identifying logical relationships in the data records and wherein the configuration information is specified using a vector-oriented language. The systems and methods further include extracting, for each entity in the list of entities, based on the rules, data records from the first subset of data records associated with the entity and generating a document for each entity in the list of entities using the extracted data records and the configuration information.

Abstract: Systems and methods are described for a scalable approach to build a knowledge database of clinical trial data by extracting, aligning, and synthesizing information from a variety of sources including clinical trial registries, abstracts of papers, and full-text medical journal articles, as well as external gazetteers, dictionaries, and lexicons. For examples, a system may implement a flexible and repeatable workflow that extracts both structured and semi-structured elements from unstructured data such as journal articles using a ‘back off strategy’ in which specialized rules are used to extract structured, clinical trial design parameters as well as information retrieval techniques that exploit regularities in language used in the medical literature to discover semi-structured trial outcomes.