Systems and methods for decryption of payloads are disclosed herein. In various embodiments, systems and methods herein are configured for decrypting thousands of transactions per second. Further, in particular embodiments, the systems and methods herein are scalable, such that many thousands of transactions can be processed per second upon replicating particular architectural components.

With the pharmaceutical injection system in an embodiment, pharmaceutical injection amount setting conditions for setting a pharmaceutical injection amount are inputted to a portable terminal (300), and the pharmaceutical injection amount setting conditions are transmitted to a health care worker-use information terminal (500). The portable terminal (300) receives the pharmaceutical injection amount set on the basis of the pharmaceutical injection amount setting conditions transmitted from the health care worker-use information terminal (500), and transmits the pharmaceutical injection amount to a pharmaceutical injection device (100). The health care worker-use information terminal (500) receives the pharmaceutical injection amount setting conditions transmitted from the portable terminal (300), and the pharmaceutical injection amount set on the basis of the pharmaceutical injection amount setting conditions is inputted. The health care worker-use information terminal (500) transmits the inputted pharmaceutical injection amount to the portable terminal (300). The pharmaceutical injection device (100) receives the pharmaceutical injection amount transmitted from the portable terminal (300), and a piston drive mechanism (101) is driven on the basis of the pharmaceutical injection amount.

A user-configurable radiological data transformation, routing and archiving engine includes a plurality of sub-engines representing algorithms programmed to be processed by a processor, the sub-engines including a user-configurable transformation sub-engine, a user-configurable routing sub-engine, a user-configurable archiving sub-engine and a user-configurable priors puller sub-engine.

System and method for flexible video construction, particularly of a personalized video clip which provides instructions to a viewer with regard to health and wellness. An ordered list of video input files is chained together, to create a single output video file using a chosen container. Timestamp values are tracked, to ensure synchronization of multiple joined clips, optionally using adjustments of the audio channel or the video channel. A video construction server utilizes information from multiple sources, to construct the video clip.

A system including a healthcare risk engine to provide a healthcare risk knowledge graph from open data and knowledge data related to risk by using risk-related terms to retrieve documents from open data and by extracting the healthcare risk knowledge graph as entities from the documents and links between the entities. A patient risk graph predictor predicts risks for a patient by combining information in a Patient Clinical Object (PCO) with entities in the healthcare risk knowledge graph to produce a patient risk graph. An impact estimator estimates an impact of a treatment by taking the treatment and adjacent nodes from the healthcare risk knowledge graph to form a healthcare treatment subgraph, finding an entity and adjacent nodes in the patient risk graph to form a patient treatment subgraph, providing an impact graph by combining the patient treatment subgraph and the healthcare treatment subgraph, and retaining resultant linked nodes.

A system for assessing patient risk using open data and input of knowledge data, the system including a healthcare knowledge data input to receive open data and a knowledge input to accept input of knowledge data relating to risk; a healthcare risk engine to provide a healthcare risk knowledge graph from the open data and knowledge data by using input of risk-related terms to retrieve documents from the open data and by extracting the healthcare risk knowledge graph as entities from the documents corresponding to risk-related terms, as well as links between the entities. A patient risk graph prediction module predicts risks for a patient by combining information in a Patient Clinical Object (PCO) with entities in the healthcare risk knowledge graph to produce a patient risk graph.

The claimed invention relates to mobile computing monitoring of health statistics using chemically synthesized conjugate markers providing highly specific details of personal health states. Using portable computing and communications devices, commonly known as ‘smartphones’, personal health and wellness information is gathered from chemical markers and reported to the user. Chemical markers include chemicals which undergo a measurable color change when combined with body fluids such as saliva. Health information derived from chemical markers may be optically collected, locally reported and widely broadcast over a ‘cloud based’ internet data distribution system.

Real-time and individualized disease monitoring is central to rapidly evolving medical sciences and technologies, but for the vast majority of patients, disease progression and treatment are monitored only in an irregular and discontinuous fashion. Consequently, disease progression and relapse are often allowed to proceed too far before they are detected, compromising the possibility of any effective treatment. For one patient, this can mean becoming refractory to the few early drug treatments that are available; for another, missing early detection may be deadly. This invention provides a method for the detection of early signals of disease and recovery thereof comprising a universal yet personalized health-monitoring solution using cell phones or other wearable smart device data that generate extensive real-time data. The invention further provides a system and method to provide answers to a variety of questions related to the patient health status and health trajectory. Its flexibility and generality is designed for a preferred application to rare disorders and rare questions for which other analytical system are lacking.

An ontology matching apparatus for large-scale biomedical ontologies according to the present invention is provided, and the ontology matching apparatus includes a preprocessing unit configured to classify received candidate ontologies into one or more ontology subsets to generate ontology subsets, a distribution processing unit configured to divide the generated ontology subsets by virtue of a distribution algorithm, apply a matching algorithm to the divided ontology subsets to generate matching threads, and deliver the generated matching threads to individual cores of participating nodes, and an aggregating unit configured to collect and sum matching results generated by the individual cores performing matching operations based on the matching threads to generate an ontology mapping.

A method includes: receiving, at a clinical intelligent agent, patient specific data comprising information regarding the condition of the patient in a room; determining an event requiring action has occurred with respect to the patient, wherein the determining comprises comparing, using a monitor of the clinical intelligent agent, the patient specific data with historical reference data; displaying one or more alerts on a patient screen; scoring, using the clinical intelligent agent, the one or more alerts, wherein the scoring comprises escalating the one or more alerts based upon a response to the one or more alerts; and prioritizing, using the clinical intelligent agent, care provider tasks displayed on the patient screen based on the score of the one or more alerts. Other aspects are described and claimed.