Abstract: A sensor-based leading indicator management ecosystem is described. Sensor data associated with the individual, possibly within a Personal Area Network (PAN) and related to the healthcare of the individual, is compiled and converted to a one or more sets of leading indicators with respect to one or more possible future healthcare actions. Leading indicators may then be compiled into one or more condition state vectors that represent encoded inputs into one or more trained action prediction agents. The trained action prediction agents then generate, possibly in real-time or based on time-series data, predicted actions that may be required at a predicted point-of-care or a moment-of-care. Further, action prediction agents may be context or domain specific.

Abstract: Examples are disclosed for a navigation system that facilitates navigation through hierarchically-related views within a user interface using multiple independent entry points. In one example, a system is configured to generate a user interface that includes a hierarchy of views based on navigation data and display a plurality of navigation components in the user interface. A selection of a navigation entry is received that corresponds to a desired view from one of the navigation components. A request is sent to a remote navigation service for data associated with the navigation entry of the desired view. The navigation components are updated based on updated navigation data received from the remote navigation service. An updated user interface is displayed that includes a dynamically generated view for the navigation entry based on the updated structure of the hierarchy of views in the plurality of navigation components.

Abstract: A packet processing system having each of a plurality of hierarchical clients and a packet memory arbiter serially communicatively coupled together via a plurality of primary interfaces thereby forming a unidirectional client chain. This chain is then able to be utilized by all of the hierarchical clients to write the packet data to or read the packet data from the packet memory.

Abstract: A system and method for integrating machine learning algorithms to enrich documents in a content management system. The content management system includes a content services engine, a transformation engine to execute one or more transformations on documents stored in the content management system, and a machine learning services to apply a machine learning algorithm to the documents based on the one or more transformations.

Abstract: A packet processing system having each of a plurality of hierarchical clients and a packet memory arbiter serially communicatively coupled together via a plurality of primary interfaces thereby forming a unidirectional client chain. This chain is then able to be utilized by all of the hierarchical clients to write the packet data to or read the packet data from the packet memory.

Abstract: A scalable cache coherency protocol for system including a plurality of coherent agents coupled to one or more memory controllers is described. The memory controller may implement a precise directory for cache blocks from the memory to which the memory controller is coupled. Multiple requests to a cache block may be outstanding, and snoops and completions for requests may include an expected cache state at the receiving agent, as indicated by a directory in the memory controller when the request was processed, to allow the receiving agent to detect race conditions. In an embodiment, the cache states may include a primary shared and a secondary shared state. The primary shared state may apply to a coherent agent that bears responsibility for transmitting a copy of the cache block to a requesting agent. In an embodiment, at least two types of snoops may be supported: snoop forward and snoop back.

Abstract: A system for facilitating interoperability among health care modules includes an interface configured to receive a first electronic record from a first health care module. The first electronic record has a first data structure. The system also includes a processor communicatively coupled to the interface. The processor is configured to analyze the first electronic record having the first data structure, and, based on the analysis, extract a portion of data from the first electronic record. The processor further creates a second electronic record using the portion of data from the first electronic record, where the second electronic record has a second data structure. The second data structure is configured to be compatible with a second health care module. The interface is further configured to transmit the second electronic record for display to the second health care module.

Abstract: A system for facilitating interoperability among health care modules includes an interface configured to receive a first electronic record from a first health care module. The first electronic record has a first data structure. The system also includes a processor communicatively coupled to the interface. The processor is configured to analyze the first electronic record having the first data structure, and, based on the analysis, extract a portion of data from the first electronic record. The processor further creates a second electronic record using the portion of data from the first electronic record, where the second electronic record has a second data structure. The second data structure is configured to be compatible with a second health care module. The interface is further configured to transmit the second electronic record for display to the second health care module.

Abstract: Embodiments are provided for protecting boot block space in a memory device. Such a memory device may include a memory array having a protected portion and a serial interface controller. The memory device may have a register that enables or disables access to the portion when data indicating whether to enable or disable access to the portion is written into the register via a serial data in (SI) input.

Abstract: A storage system with privacy-centric multi-partitions and method for use therewith are provided. In one embodiment, a storage system comprises a memory configured to be partitioned into a plurality of partitions, wherein each partition is associated with its own boot block, and wherein each boot block is configured to boot any of the plurality of partitions. The storage system also comprises a controller configured to communicate with the memory and to: in response to a failure to boot one of the plurality of partitions with that partition's boot block, use a boot block of another one of the plurality of partitions to boot the one of the plurality of partitions; and restrict access to each of the plurality of partitions only to authenticated entities. Other embodiments are provided.

Abstract: A method of an electronic apparatus for providing information related to a fulfillment center includes confirming data request information for requesting data related to the fulfillment center, acquiring first data corresponding to the data request information from at least one other apparatus connected to the electronic apparatus, and generating second data by processing the first data based on template information corresponding to the data request information.

Abstract: An apparatus for updating a user profile based on a user file is included. Apparatus may include a processor and a memory communicatively connected to processor. Memory may contain instructions configuring processor to identify a plurality of profile sections in a user profile comprising a profile content; receive a user file; parse document content in user file by identifying a plurality of document sections in user file, categorizing a first document section of plurality of document sections of user file, identifying at least a document subsection of first document section, and categorizing document content in a first document subsection of at least a document subsection; and insert document content in at least one of the plurality of profile sections based on categorization of first document section and categorization of first document subsection.

Abstract: A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.

Abstract: A method for tracking an activity of a user. The method includes receiving, by a tracking element, compressed user activity information representative of activities performed by the user during a visit to a web page; receiving, by the tracking element, web page content information representative of web page content displayed to the user during the visit; decompressing the compressed user activity information; and generating user visit information by the tracking element.

Abstract: We disclose computational models that alleviate the effects of human ascertainment biases in curated pathogenic non-coding variant databases by generating pathogenicity scores for variants occurring in the promoter regions (referred to herein as promoter single nucleotide variants (pSNVs)). We train deep learning networks (referred to herein as pathogenicity classifiers) using a semi-supervised approach to discriminate between a set of labeled benign variants and an unlabeled set of variants that were matched to remove biases.

Abstract: A computer system includes memory storing a database including a label text table for storing multiple label texts each associated with a national drug code (NDC), a fields table, and a field options table. A processor is configured to execute instructions including receiving drug input from a user indicative of a specified NDC, querying the database to obtain a label text associated with the specified NDC, at least one field associated with the obtained label text, and multiple field options associated with the obtained field, building a data structure, converting the data structure to a user interface for displaying the obtained label text, the obtained field, and the multiple obtained field options, receiving a user selection of one of the multiple obtained field options, and printing the label text including the updated field to a label and/or saving the label text including the updated field to a record database.

Abstract: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

Abstract: Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that correlate with response of cancer patients to platinum-based chemotherapy. Described herein are data structures, data processing, and machine learning models to predict a probability of benefit of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising platinum therapy.

Abstract: Method of generating frustration score starts with processor receiving data associated with interactions by users with website including web pages displayed during sessions. Processor generates page view scores based on page view experience indicators and zone experience indicators associated with the web pages, generates session scores based on the page view scores and session experience indicators, and generates web page scores based on page view scores. Processor identifies the webpage associated with highest level of frustration based on page view and web page scores and identifies the session that is associated with highest level of frustration based on the session scores. Processor causes a user interface to be displayed on a display device that includes a visualization of interactions by a user with the website associated with the session with the highest level of frustration and an identification of the web page associated with the highest level of frustration.

Abstract: A patient-trial matching system (100) includes a structuralizer (102) configured to convert input non-structured patient health data and input non-structured clinical trial eligibility criteria into structured patient health data and structured clinical trial eligibility criteria by organizing a content of the non-structured data as known data elements. The patient-trial matching system further includes a semantic matcher (122) configured to match the structured patient health data and the structured clinical trial eligibility criteria based on user input matching criteria and outputs matched results. The patient-trial matching system further includes a ranking engine (126) configured to rank the matched results using ranking criteria (128), which include ranking patients matched to a clinical trial of interest in response to matching to find a group of trial patients and ranking clinical trials matched to a particular patient in response to matching to find a clinical trial.