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公开(公告)号：US20220253729A1
公开(公告)日：2022-08-11
申请号：US17590143
申请日：2022-02-01
Inventor： Akshay Vashist , Chumki Basu , Todd Huster , Pingji Lin , Dennis Mok , John R. Wullert, II
IPC: G06N5/04 , G06N5/02 , G06F40/40 , G06F40/284
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. This workflow also aligned structured elements with data from structured data sources and augmented the base structured information with additional searchable trial features or characteristics and sentiment or polarity scores derived from the unstructured data.
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公开(公告)号：US20210342523A1
公开(公告)日：2021-11-04
申请号：US17374802
申请日：2021-07-13
Inventor： Michael Bellero , William Hannon , Boris Reznichenko , Karen Rutkowski , Brian Geldziler
IPC: G06F40/166 , G16H15/00 , G06F40/14
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.
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Patent Agency Ranking
公开(公告)号：US20240303226A1
公开(公告)日：2024-09-12
申请号：US18601828
申请日：2024-03-11
Inventor： Osman Serdar TURKOGLU , Arun JAIN
CPC classification number: G06F16/211 , G06F16/287 , G16H20/10
Abstract: The disclosure relates to systems and methods of translating event data to generate a scalable data structure to identify or predict an event of interest such as a clinical diagnosis. The scalable data structure is expandable to accommodate various types of event data each having different types of timing indications on a timeline. The system may translate the event data in a way that event data can inherit event data values from other event data in a single time series of events. The scalable data structure may be used to generate unified visualizations of all translated events as well as for forecasting and predicting events of interest. The scalable data structure may be implemented in various contexts such as for clinical diagnostics in which clinical trial data or medical health data from various sources are translated to generate the scalable data structure.
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公开(公告)号：US20230394235A1
公开(公告)日：2023-12-07
申请号：US18329990
申请日：2023-06-06
Inventor： Mohammed Arifur RAHMAN , Nityanand CHOUBEY , Imtiaz MOHIUDDIN , Paresh PATEL , Babu Sudhagar RAJ , Mehul SHAH
IPC: G06F40/226 , G06F40/242 , G06F40/295 , G06F40/284 , G06F16/35
CPC classification number: G06F40/226 , G06F40/242 , G06F16/35 , G06F40/284 , G06F40/295
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.
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5.发明授权公开(公告)号：US11475212B2
公开(公告)日：2022-10-18
申请号：US17374802
申请日：2021-07-13
Inventor： Michael Bellero , William Hannon , Boris Reznichenko , Karen Rutkowski , Brian Geldziler
IPC: G06F40/166 , G16H15/00 , G06F40/14 , G06F40/295
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.
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6.发明公开公开(公告)号：US20240212154A1
公开(公告)日：2024-06-27
申请号：US18390022
申请日：2023-12-20
Inventor： Akshay VASHIST , Hossain SABOONCHI , Jong-Hoon AHN
CPC classification number: G06T7/0016 , G06T7/11 , G06T7/62 , G06T2207/20081 , G06T2207/20084 , G06T2207/30084
Abstract: A system may access a source image of the subject. A system may execute an image segmentation model that uses the source image to distinguish the target object from among other objects in the source image. A system may generate a binary image of the target object based on execution of the image segmentation model. A system may generate, based on the binary image, a size estimate of the target object. A system may execute a machine learning-based model that uses the size estimate and one or more covariates to predict a future size of the target object. A system may determine a risk classification for the subject based on the predicted growth rate, the risk classification being based on a probability that the target object of the subject will result in a disease state, the risk classification to be used to determine a treatment regimen to treat or prevent the disease state.
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公开(公告)号：US20240050028A1
公开(公告)日：2024-02-15
申请号：US18446973
申请日：2023-08-09
Inventor： Jeffrey Martin COCHRAN, JR.
IPC: A61B5/00 , A61B5/349 , A61B5/0245 , A61B5/11
CPC classification number: A61B5/4812 , A61B5/743 , A61B5/7267 , A61B5/349 , A61B5/0245 , A61B5/11
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).
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