Abstract: Methods for assisting medical personnel in performing a diagnosis, diagnostic aids and computer readable media. The method initialisation step comprises: receiving a plurality of input medical texts tagged with potential medical codes; curating and validating the plurality of input medical texts to output a subset of medical texts that are validated and tagged with medical codes; and using the subset of tagged and validated medical texts to model the associations between the medical texts and the medical codes, and generating a medical text learning model based on the associations. The method diagnostic step comprises: inputting a specimen text relating to a patient into the medical text learning model; processing the specimen text using the medical text learning model; identifying suggested medical codes based on the specimen text; and outputting diagnoses, symptoms and treatments linked to the suggested medical codes for assisting medical personnel in providing a diagnosis for the patient.

Abstract: A computer apparatus to assist diagnosis of a patient including: a memory storing instructions for execution and an output for results of a processor that provides a patient builder and a vertex filter. Where the builder inputs patient data including historical clinical; and open data, and to create a patient clinical object, PCO, graph; and a full patient graph PCOs for each patient. The filter includes: a context builder to build a domain based on a specification and open data; a context-based vertex filter ranking vertices in the full patient graph based on domain affiliation, retaining any vertices with high domain affiliation; and computing a similarity between the patient PCO and other PCOs in the full patient graph using the retained vertices; and a patient ranker ranks the PCOs according to t similarity where the output lists patients similar to the patient to suggest patient diagnoses.

Abstract: A system and method to input patient data including previous diagnosis, drugs, symptoms and treatment, open data and expert knowledge, and to use these inputs to create a patient clinical object (PCO), biomedical knowledge and rule based knowledge graphs, and to enrich the PCO using the biomedical knowledge graph. A meta diagnosis predictor is to use the PCO and the biomedical knowledge graph and/or the rule based knowledge graph in plural predictors of a diagnosis-based predictor to provide a set of diagnoses based on previous diagnoses, a drug-based predictor to provide a set of diagnoses based on drugs taken by the patient, a symptom-based predictor to provide a set of diagnoses based on symptoms of the patient and a treatment-based predictor to provide a set of diagnoses based on the treatments. Any of the sets of diagnoses may be combined to give a predicted primary diagnosis for the patient.

Abstract: A computer apparatus to identify healthcare resources used by a patient given a potential diagnosis, the apparatus comprising: a memory storing instructions for execution by a processor, the processor configured by the instructions to implement an impact estimator to: receive a patient clinical object, PCO, which represents the patient in the form of a graph, and includes previous healthcare resource utilization, HCRU, information of the patient as a patient HCRU subgraph; receive an HCRU knowledge graph, HCRU KG, associating healthcare resources with medical services; receive potential diagnosis information from a clinician; identify and extract potential healthcare resources and services in the HCRU KG associated with the potential diagnosis to form an HCRU subgraph associated with the potential diagnosis; and to perform graph analysis on the HCRU subgraph associated with the potential diagnosis and the patient HCRU subgraph to obtain an updated patient HCRU subgraph taking the potential diagnosis into acco

Abstract: A computer apparatus to identify healthcare resources used by a medical institution, comprising: a memory storing instructions for execution by a processor, the processor configured by the instructions to provide: a knowledge graph builder; and a knowledge graph customizer; wherein: the knowledge graph builder is arranged to input open data and clinician information, to generate a set of medical services terms and a set of medical resources terms from the open data and clinician information, and to associate the medical resources with the medical services to build a knowledge graph; and the knowledge graph customizer is arranged to match the knowledge graph with records of the medical institution to provide a customized subgraph of the knowledge graph which is specific to the medical institution.

Abstract: 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.

Abstract: An array of registered entity property-value pairs representing a registered entity are stored in a data storage system. Each of the pairs includes a property label representing a property and a value range of the property. For each identifier property-value, an identifier property label, uniquely identifying the registered entity, and an identifier value representing a value of the identifier property are stored. A dataset is acquired with dataset property-value pairs for each of a first set of acquired dataset entities, where each pair includes a property label representing a property and a value representing a value range of the property. An identifier property-value pair matching an acquired dataset property-value pair for the acquired dataset entity is identified and then the acquired dataset property-value pairs are consolidated into the array of registered entity property-value pairs.

Abstract: 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.

Abstract: A microservice-based data processing apparatus, including: a type register, storing a list of types, a type being a semantic concept expression; and microservices each comprising an annotation of an input type and output types from the list; processing logic transforming input data expressed by the input type into output data expressed by the output types; and a messaging mechanism for inputting data, via a message, to a microservice, the mechanism defining a message format for structuring the messages. The format includes a first field specifying the data being input; and a second field specifying a type, from the list of types, semantically expressing the concept instantiated by the data. Each microservice includes a controller to receive a message from the mechanism having the format, and to respond by executing the logic when the type specified by the second field matches the input type of the microservice.

Abstract: Methods for assisting medical personnel in performing a diagnosis, diagnostic aids and computer readable media comprising code which, when executed by a computer, cause the computer to execute a method for assisting medical personnel in performing a diagnosis, the method comprising an initialisation step and a diagnostic step, wherein the initialisation step comprises: receiving a plurality of input medical texts tagged with potential medical codes; curating and validating the plurality of input medical texts to output a subset of medical texts that are validated and tagged with medical codes; and using the subset of tagged and validated medical texts to model the associations between the medical texts and the medical codes, and generating a medical text learning model based on the associations, and wherein the diagnostic step comprises: inputting a specimen text relating to a patient into the medical text learning model; processing the specimen text using the medical text learning model; identifying suggeste

Abstract: Diagnostic aids, methods for assisting medical personnel in performing a diagnosis, and computer readable media comprising code which, when executed by a computer, cause the computer to execute a method for assisting medical personnel in performing a diagnosis, wherein the diagnostic aids comprise: a receiver to receive an unstructured input; an analyser and parser to split the unstructured input into a plurality of logical components, and to detect medical terms in the plurality of logical components; a mapping engine to receive a medical classification hierarchy of medical standard codes in the form of a knowledge graph, and semantically annotate the knowledge graph with synonyms of medical terms used in the medical standard codes; an automatic coding solver to analyse the medical terms detected in the plurality of logical components by the analyser and parser, to generate a list of potential matching medical standard codes for each of the medical terms, to compare the lists of potential matching medical st

Abstract: A data processing apparatus including a library, storing software services, each executing a data processing function; a user interface receiving commands, expressed in a language and defining a data processing target and a data processing request; and a parser. The parser extracting from each command: the request from the language; and the target. A knowledge base maintaining a record of the request and the target; a scheduler obtaining the request from the parser, and compiling a schedule to fulfill the request; a controller controlling execution of the schedule, the target being the input data to the execution, and to output a result; and a result processor obtaining the result, and, based on records of requests and targets, to identify a request candidate, and to output to the user as a selectable command in the language, via the user interface, the candidate with the result defined as a data processing target.

Abstract: A data storage system a process including: storing an array of registered entity property-value pairs representing the registered entity where each of the pairs includes a property label representing a property and a value range of the property; storing an entry of one identifier property-value pair where each pair includes an identifier property label uniquely identifying the registered entity, and an identifier value representing a value of the identifier property; acquiring a dataset having acquired dataset property-value pairs for each of a first set of acquired dataset entities, where each pair includes a property label representing a property and a value representing a value range of the property; identifying an identifier property-value pair matching an acquired dataset property-value pair for the acquired dataset entity; and consolidating the acquired dataset property-value pairs into the array of registered entity property-value pairs.

Abstract: A microservice-based data processing apparatus, including: a type register, storing a list of types, a type being a semantic concept expression; and microservices each comprising an annotation of an input type and output types from the list; processing logic transforming input data expressed by the input type into output data expressed by the output types; and a messaging mechanism for inputting data, via a message, to a microservice, the mechanism defining a message format for structuring the messages. The format includes a first field specifying the data being input; and a second field specifying a type, from the list of types, semantically expressing the concept instantiated by the data. Each microservice includes: a controller to receive a message from the mechanism having the format, and to respond by executing the logic when the type specified by the second field matches the input type of the microservice.

Abstract: A data processing apparatus including a library, storing software services, each executing a data processing function; a user interface receiving commands, expressed in a language and defining a data processing target and a data processing request; and a parser. The parser extracting from each command: the request from the language; and the target. A knowledge base maintaining a record of the request and the target; a scheduler obtaining the request from the parser, and compiling a schedule to fulfill the request; a controller controlling execution of the schedule, the target being the input data to the execution, and to output a result; and a result processor obtaining the result, and, based on records of requests and targets, to identify a request candidate, and to output to the user as a selectable command in the language, via the user interface, the candidate with the result defined as a data processing target.

Abstract: A computer apparatus to identify healthcare resources used by a medical institution, comprising: a memory storing instructions for execution by a processor, the processor configured by the instructions to provide: a knowledge graph builder; and a knowledge graph customizer; wherein: the knowledge graph builder is arranged to input open data and clinician information, to generate a set of medical services terms and a set of medical resources terms from the open data and clinician information, and to associate the medical resources with the medical services to build a knowledge graph; and the knowledge graph customizer is arranged to match the knowledge graph with records of the medical institution to provide a customized subgraph of the knowledge graph which is specific to the medical institution.

Abstract: A computer apparatus to identify healthcare resources used by a patient given a potential diagnosis, the apparatus comprising: a memory storing instructions for execution by a processor, the processor configured by the instructions to implement an impact estimator to: receive a patient clinical object, PCO, which represents the patient in the form of a graph, and includes previous healthcare resource utilization, HCRU, information of the patient as a patient HCRU subgraph; receive an HCRU knowledge graph, HCRU KG, associating healthcare resources with medical services; receive potential diagnosis information from a clinician; identify and extract potential healthcare resources and services in the HCRU KG associated with the potential diagnosis to form an HCRU subgraph associated with the potential diagnosis; and to perform graph analysis on the HCRU subgraph associated with the potential diagnosis and the patient HCRU subgraph to obtain an updated patient HCRU subgraph taking the potential diagnosis into acco

Abstract: A computer apparatus to assist diagnosis of a patient including: a memory storing instructions for execution and an output for results of a processor that provides a patient builder and a vertex filter. Where the builder inputs patient data including historical clinical; and open data, and to create a patient clinical object, PCO, graph; and a full patient graph PCOs for each patient. The filter includes: a context builder to build a domain based on a specification and open data; a context-based vertex filter ranking vertices in the full patient graph based on domain affiliation, retaining any vertices with high domain affiliation; and computing a similarity between the patient PCO and other PCOs in the full patient graph using the retained vertices; and a patient ranker ranks the PCOs according to t similarity Where the output lists patients similar to the patient to suggest patient diagnoses.

Abstract: 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.

Abstract: 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.