Merative US L.P.

Methods and systems of estimating b-values. One system including an electronic processor configured to receive a set of medical images associated with a patient, where the set of medical images are diffusion-weighted images. The electronic processor is also configured to extract a set of patches from each medical image included in the set of medical images. The electronic processor is also configured to determine, via an estimation model trained using machine learning, a set of estimated b-values, where each estimated b-value is associated with a patch included in the set of patches. The electronic processor is also configured to determine a b-value for each of the medical images included in the set of medical images, where the b-value is based on the set of estimated b-values.

Systems and methods for managing at least one medical image that includes image data and metadata. One method includes generating, at a processor, a number of copies of the at least one medical image, wherein the number of copies is equal to a number of independent reviewers associated with the at least one medical image. Each of the copies includes the image data and the metadata. The method also includes modifying the metadata of each of the copies to include a unique identifier, and storing the copies to at least one image storage device.

Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters. The patient summary generation engine generates a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.

Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.

Methods and systems for rending medical images within a web browser application. The web browser application retrieves a worklist and automatically determines an image study from the worklist to be cached. The web browser application retrieves at least one medical image included in the image study. The web browser application creates a web database for storing the at least one medical image within the browser application. When a user selects a medical image for display within the web browser, the web browser application determines whether the medical image is stored in the web database. When the medical image is stored in the web database, the web browser application retrieves the medical image from the web database. When the medical image is not stored in the web database, the web browser application retrieves the medical image from a remote image repository.

An approach for providing a cap with an embedded high-resolution lens and a sampling insert that is used during an authentication of a composition of a liquid in a container sealed by the cap. The cap has a top portion of a cap with an opening, a sampling insert inside the opening in the top portion of the cap, and a high-resolution lens inside an opening in the sampling insert.

Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.

Systems and computer-implemented methods of performing image registration. One method includes receiving a first image and a second image acquired from a patient at different times and, in each of the first image and the second image, detecting an upper boundary of an imaged object in an image coordinate system and detecting a lower boundary of the imaged object in the image coordinate system. The method further includes, based on the upper boundary and the lower boundary of each of the first image and the second image, cropping and padding at least one of the first image and the second image to create an aligned first image and an aligned second image and executing a registration model on the aligned first image and the aligned second image to compute a deformation field between the aligned first image and the aligned second image.

A medical episode analysis engine is provided. The engine generates a first matrix data structure having an entry for each concept pairing and storing a value representing relatedness weighted according to a temporal weighting function. The engine generates a second matrix data structure by calculating, for each entry in the first matrix, a relatedness measure of the concepts in the concept pairing based on a frequency of occurrence together. The engine generates, for each first concept, a concept embedding, based on the second matrix, that specifies, for each other second concept, a temporally weighted relatedness measure. The engine generates, for each anchor concepts, a corresponding episode definition comprising a plurality of related concepts corresponding to a same episode, based on the concept embedding. The engine processes new input data based on the episode definition data structures to identify instances of corresponding episodes in the new input data.

A mechanism is provided in a data processing system to implement an annotator for annotating content using context-based surface forms. The mechanism receives a dictionary data structure of surface forms comprising a plurality of regular expressions and input content. The mechanism compares a given span of text in the input content to each regular expression in the dictionary data structure. Responsive to the given span of text matching a given regular expression, an annotator annotates the span of text with a content indicator corresponding to a content category associated with the dictionary data structure. The mechanism performs a natural language processing operation on the input content based on results of the annotation.