121 results about "Medical terminology" patented technology

A comprehensive database system employs common medical terms, vocabularies and identifiers in identifying organizational characteristics as well as location availability and suitability for delivering services by a particular physician to a patient with particular medical conditions via a user friendly display interface. A system for providing healthcare related information comprises a database incorporating information identifying an encompassing organization and multiple constituent organizations of the encompassing organization including a constituent organization type identifier. The database information also incorporates location information identifying multiple locations in a constituent organization available to accommodate a patient for different purposes and includes a location type identifier. An interface processor employs the database in supporting business applications of an organization and in providing a user with service and location information related to a particular constituent organization in response to user command.

A computer implemented system and method allow users to create and edit electronic health records with the use of touch screen monitors, such that complex forms are managed by a user-friendly graphic user interface. The graphic user interface includes collapsible menus that enable the management of the complex hierarchical structure of the forms. The graphic user interface also includes a system that allows for three different values, corresponding to a positive value, a negative value and the deactivation of the data field, to be entered in the same location within the electronic form. The data elements of each data field are coded with recourse to standard medical terminologies enabling dynamic interaction and intelligent prompting.

Clinical trials are defined, managed and evaluated according to an overall end-to-end system. The central authority creates protocol meta-models and makes them available to clinical trial protocol designers. Each meta-model includes a short list of preliminary patient eligibility attributes which are appropriate for a particular disease category. The protocol designer chooses the appropriate meta-model, and encodes the clinical trial protocol, including eligibility and patient workflow, within the selected meta-model. The resulting protocol database is stored together with databases of other protocols in a library of protocol databases. Sponsors and individual clinical sites have controlled access to the protocols. Study sites make reference to the pertinent protocol databases to which they have access in the protocol database library in order to perform patient eligibility screening. Once a patient is enrolled into a study, the protocol database indicates to the clinician what tasks are to be performed at each patient visit. These tasks can include both patient management tasks and data management tasks. The workflow graph advantageously also instructs the proper time for the clinician to obtain a patient's informed consent. The system reports patient progress to study sponsors, who can then monitor the progress of the trial, and to a central authority which can then generate performance metrics. Advantageously, a common controlled medical terminology database is used by all components of the system.

A system and method for browsing a pharmacovigilance database with a graphical representation that shows relationships between medical terms may include providing access to a plurality of medical terminologies and mapping medical terms of the plurality of terminologies to a searchable database by using a semantic network to relate the medical terms of the different terminologies. The system and method may further include providing a graphical user interface that enables graphical navigation of the plurality of terminologies, enables display of a mapping between a first medical term from a first medical terminology to a second medical term from a second medical terminology, and enables coding of pharmacovigilance reports using medical terms of the second terminology based on a description provided using medical terms of the first terminology.

The invention discloses a method and a system for automatically establishing a medical term mapping relationship based on word segmentation and coding. The method comprises establishing the mapping relationship between terms of a dictionary A and terms of a dictionary B, and specifically comprising the steps of 1, dividing each sample term into a main field and an attribute field; 2, establishing a main field table by the main fields and codes obtained in the step 1, and establishing a word segmentation dictionary by common Chinese characters and the attribute fields; 3, performing Chinese word segmentation on words in the main field table; 4, performing Chinese word segmentation on the terms of the dictionary A; 5, computing correlation degree between the main field of each sample term and the main field of the corresponding term of the dictionary A; 6, matching and coding the main fields of the sample terms and the main fields of the terms of the dictionary A according to the correlation degrees; and 7, coding the main fields of the terms of the dictionary B, and establishing the mapping relationships aiming at the terms, with the same main field codes, of the dictionary A and the dictionary B. According to the method, the problem that the manual term mapping efficiency is low thus medical information is difficult to be shared is solved.

A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

The invention relates to a text data processing method and device, an electronic device, and a computer readable storage medium, and relates to the field of computer information processing. The methodcomprises the steps: performing the segmentation of the words in a medical text, and generating word vectors; marking the lexical category of the words in the medical text through a lexical categoryvector; inputting the word vectors and the lexical category vector into a bidirectional deep learning model so as to obtain a first result; inputting the first result and a rule template into the deeplearning model, so as to extract the notional words in the medical text. The invention also relates to a medical text data processing method and device, an electronic device and a computer readable storage medium, and the method can quickly and accurately recognize the medical terms form a long text of clinical cases.

A system and method for rapid entry of past medical diagnoses allows for development of specific, clinically relevant diagnoses from a simple input medication through use of a developed database that maps an input medication to a specific medical diagnosis through an intermediate indications data set. Input medications are normalized to their generic or chemical name and the normalized medication data is associated to an intermediate diagnosis through a medications indications listing contained in a database. Each medication develops a short list of possible specific and/or macro-diagnoses (indications) consistent with the medications and a selected indication is concept-matched to a specific medical diagnosis in order to develop a proper diagnosis in clinically meaningful terms, from a concept-mapped portion of the database. The concept-mapped portion is developed by grouping medical terms into clinically meaningful groups and developing pointers within those groups between identified diseases and a developed indications taxonomy, itself developed by forming a composite of FDA-approved and non-FDA approved indications with respect to particular normalized medications.

A method of developing sematic search engine is used for searching adverse event report from literature articles, references or abstracts. The algorithm of the method comprises web-based semantic search system; medical terminology dictionaries including MedDRA, ICD-10 dictionaries, and others represented as reported medical event; product including drug, medical device, herbal medicine, and/or nutritional food dictionaries such as WHO Drug Dictionary; identifiable patient or animal; identifiable reporter (author); and a vast amount of medical/scientific literature databases. The search engine is widely used to monitor literature adverse event case report, safety related animal and human study report from abstracts and/or articles for product safety signal detection and regulatory reporting purposes worldwide.

Training a machine to provide specialized health care apparatus may include receiving text describing a user's health condition via a user interface. Text may be converted into corresponding medical terms. A database may be searched for a list of health care providers treating health conditions associated with the medical terms. A machine learning model may be built that may include user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition. The machine learning model may predict one or more of the health care providers that provide treatment for the user's health condition that matches the user's preference. The machine learning model may be retrained based on one or more of feedback from the user, the health care providers, and updated traits of the users and the health care providers.

A system for matching patients to available clinical trials. The process includes a computer with a CPU having a processor, memory and an electronic database. The electronic database has a language equivalency database with a thesaurus of medical terminology and a regular language translator. Also, the regular language translator is operable to translate medical terms in the thesaurus of medical terminology into regular language terms such that a patient survey using regular language is generated and responded to by a patient. Also, the system identifies available clinical trials for the patient based on the responses by the patient.

Certain embodiments of the present invention provide a system and method for structuring dynamic data from a plurality of images. Certain embodiments include accessing an image data set including data representing a plurality of images, determining a dynamic or functional attribute in the image data set, associating the dynamic or functional attribute with a lexical attribute from a lexicon, and storing the lexical attribute and an associated code. The lexical attribute and associated code may be stored in a database and/or a structured report, for example. The lexicon may be modified to accommodate attributes and medical terminology. Attributes relate to a feature and/or a region of interest in the image data set.

The invention discloses a method and system for generating an electronic case history with informing and signing functions. The method comprises the following steps of: collecting a voice file and a medical image file during diagnosis of a doctor on a patient; transmitting the voice file and the medical image file to a server; resolving the received voice file by the server to obtain a voice resolving result, and resolving the received medical image file by the server to obtain a medical image resolving result; converting the voice resolving result and the medical image resolving result into text files; comparing the converted text files with pre-stored medical terms; performing medical term error correction to form an electronic case history file; storing the electronic case history file; configuring the corresponding relationship between the formed electronic case history and a client of the patient or a family member of the patient; marking the time of an event in the electronic case history to form a timestamp; sending an informing and signing notice to the client of the patient or the family member of the patient corresponding to the electronic case history file; receiving the informing and signing notice by the client of the patient or the family member of the patient; and storing notice receiving feedback information into the electronic case history file.

The invention discloses a medical text named entity recognition method and device. The method comprises the following steps of: respectively inputting a medical text into a forward long and short-termmemory network and a backward long and short-term memory network so as to obtain a first output result and a second output result; respectively mapping the first output result and the second output result by utilizing a first activation function, and combining the mapped results to obtain a third output result; calculating the third output result by utilizing a second activation function so as toobtain an n*r-dimensional matrix P; and substituting the matrix P into a conditional random field transfer matrix, and calculating and obtaining a global optimum label sequence corresponding to a named entity. According to the method, the medical term recognition correctness and recall rate are high, the calculation speed is high, and medical term recognition can be rapidly carried out, so that model calculation and prediction can be carried out.

The invention relates to the technical field of Chinese electronic medical record processing, and discloses a semi-supervised learning method for constructing a medical knowledge graph from Chinese electronic medical records. The method is an end-to-end method, and comprises the following steps: firstly, obtaining an original corpus of a Chinese electronic medical record, performing operations such as data preprocessing, knowledge description system design, manual entity relationship marking and the like, and sorting related medical terms to construct a Chinese medical word segmentation dictionary; completing the knowledge extraction work in combination with a machine learning algorithm and a deep neural network; expanding a Chinese electronic medical record mark data set step by step in combination with a semi-supervised learning method, storing all extracted knowledge triples in a Neo4j database, and constructing a medical knowledge graph. According to the method provided by the invention, the Chinese electronic medical record annotation corpus is provided, and the medical knowledge in the medical record can be accurately identified.

The invention discloses an accurate and quick electronic medical record type-in system which consists of three parts, namely an intelligent medical input module, a medical term error correction module and a term acquisition module. According to the system for typing electronic medical records, traditional Chinese medicinal clinical terms from a real world can be standardly applied to traditional Chinese medicinal clinical scientific research work in the real world, so that information can be accurately and efficiently acquired in the clinical work, and clinical electronic medical records meeting a quality control requirement can be generated; furthermore, errors can be timely corrected, and new generated traditional Chinese medicinal clinical terms can be acquired; the new terms are examined and approved to ensure that traditional Chinese medicinal clinical electronic medical records can be written quickly and accurately.

The invention discloses a method and a system for automatic recognition of medical text terms based on long-term and short-term memory network, which are designed for automatically extracting medicalterm entities from medical text. The method comprises the following steps: each character in a medical text sentence is represented by a pre-trained character vector to obtain training data; the training data is inputted into the two-way long-short memory network to obtain the label category with the highest probability of each character in the medical text sentence; the output is input into a conditional random field and the Viterbi algorithm is used to calculate the label sequence with the highest probability of association. The method integrates the respective advantages of the two-way long-short-time memory network and the conditional random field, and can effectively improve the accuracy rate of word labeling.