1766results about "Medical practises/guidelines" patented technology

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

The current disclosure provides methods for an automated clinical recommendation system that generates clinical recommendations for patients of a health care system based on natural language queries submitted by care providers of the health care system. The natural language queries may be typed into a user interface (UI) of the clinical recommendation system, or submitted by voice via a microphone. The clinical recommendation system provides a clinically explainable disease state of a patient based on patient data included in a query, and recommends a next course of action (e.g., a treatment) based on clinical guidelines and population statistics, in a manner that reduces a current burden of clinicians in consulting digital clinical manuals via a series of time-consuming and cumbersome interactions with a graphical user interface (GUI) of the digital clinical manuals.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

The invention relates to the technical field of vascular surgery, in particular to a vascular surgery interventional intracavity treatment postoperative complication prediction and management system which integrates data acquisition, processing, model training, prediction analysis, comprehensive decision and early warning management and is specially designed for vascular surgery interventional intracavity treatment. The system monitors the postoperative physiological indexes of a patient in real time through multiple sensors, combines clinical data and disease history, carries out physiological sequence prediction and image analysis by utilizing RNN and CNN models, and accurately evaluates the risk of complications. The comprehensive decision-making module generates a personalized management scheme, and the early warning management module monitors in real time and pushes early warning and suggestions in time when abnormity occurs. The system not only improves the prediction accuracy and the management effect, but also optimizes the medical resource configuration, enhances the patient experience, contributes to the development of precise medical treatment and intelligent management, and effectively solves the key problem of postoperative complication prediction and management.

Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention provides a medical AI question and answer method based on retrieval enhancement, which comprises the following steps: acquiring a medical professional knowledge base, and constructing an entity recognition model and a medical knowledge graph; the method comprises the following steps: splitting a medical professional knowledge base, and creating a vector index and a full-text retrieval reverse index; intention classification is carried out, and initial processing is completed; performing multi-path mixed retrieval on the medical professional knowledge base to obtain a user question related text block; a filtering rule is preset, and the user question related text blocks are filtered; answering the user question according to a preset second prompt word, and answering the user question according to a preset third prompt word when the user question exceeds the range of the medical professional knowledge base or the multi-path mixed retrieval has no result. According to the method, related context information can be accurately searched, reliable reference information is provided for a generative large model to answer professional questions, the interactive experience of a medical question answering system is improved, and the accuracy and specialty of answer generation of the system are remarkably improved.

The invention discloses an ear-nose-throat clinical nursing decision-making system based on vital signs of a patient, and relates to the technical field of ear-nose-throat clinical nursing. The nursing decision-making system comprises a nursing database construction module, a data acquisition module, a vital sign analysis module, an illness state early warning module, a nursing decision-making support module, a personalized nursing scheme generation module and a nursing scheme execution tracking module, all the modules are connected through electric signals, and the nursing database construction module is connected with the data acquisition module. And acquiring nursing data and nursing rule specifications. According to the ear-nose-throat clinical nursing decision-making system based on the vital signs of the patient, by comprehensively analyzing the vital sign data, the medical history information, the allergy history and the like of the patient, a nursing plan meeting the actual needs of the patient is generated, so that the pertinence and effectiveness of nursing can be improved, the individual needs of different patients can be met, and the patient experience is improved. Therefore, symptoms of patients are relieved, and life quality is improved.

An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Techniques relate to generating and / or verifying content based on protocols and / or user input. The techniques can receive protocol data from an entity, including a procedure, guideline, policy, or standard. User input requesting content generation related to specific parameters can also be received. Based on the protocol data, a data request can be generated and sent to a content generation component. Machine-generated content can be received from the content generation component in response. The machine-generated content can be analyzed based on the protocol data. Output content can be generated based on the machine-generated content and provided to a computing device. In examples, this ensures that generated content aligns with entity-specific protocols and / or user-specified parameters, enabling tailored and / or compliant content delivery for various contexts.

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

Various systems and methods are provided for generating and editing of medical reports and records using artificial intelligence (AI) assistance. An intelligent medical reporting tool may edit medical materials such as electronic medical records, medical reports, and treatment plans. The intelligent medical reporting tool can provide edit recommendations and suggested additions to medical materials to a user of the tool. Edit recommendations and suggested additions may be related to contradictions, incompleteness, clarity, and clinical guidelines.

The invention discloses a clinical aid decision-making system based on big data, and relates to the technical field of intelligent medical treatment, and the system extracts structured and unstructured data from electronic medical records and medical images, employs a bidirectional LSTM deep learning framework based on a self-attention mechanism, carries out the alignment of the cross-modal features of medical record texts and image data, and carries out the recognition of the cross-modal features of the medical record texts and the image data. The method comprises the following steps: establishing a medical knowledge graph based on an RDF triple, modeling a high-order interaction relationship through a cross-modal interaction attention mechanism CMA, enabling disease expression to be more accurate and interpretable, constructing the medical knowledge graph based on the RDF triple, dynamically expanding knowledge in combination with a graph neural network GNN, and matching the disease expression of a patient through a semantic similarity calculation model; a multi-layer similarity calculation framework is adopted to perform similar case screening, coarse screening is performed through surface feature matching, deep semantic matching is performed in combination with GNN optimized disease semantic vectors, cases with similar disease progress paths are inferred and matched through a knowledge graph, and comprehensive and multi-level similar case screening is realized.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A surgical planning and assessment system is disclosed. The system may include a computing system having a processor, a data store, and a patient specific planning and analysis module. The system may be configured to store a patient specific target plan including a digital representation of a spinal rod having a target geometry and transform the digital representation into a curved line. The system may generate a spline data set comprising a plurality of spline segments that are sequentially linked to one another. The system may adjust, iteratively, each spline segment until the plurality of spline segments approximate the curved line. The system may generate a digital file comprising drawings for manufacturing a spinal rod based on the plurality of spline segments. In various embodiments, each spline segment may be defined by a corresponding arc or a corresponding line extending between a corresponding starting point and a corresponding ending point.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention relates to a battle wound field real-time hemostasis method based on a biological information sensing technology, and the technical scheme comprises the steps: introducing a time sequence biological signal for modeling, and carrying out the dynamic prediction of a battle wound state; on the basis of a biosensor data flow, a multi-dimensional time sequence state space is constructed, and the multi-dimensional time sequence state space comprises a nonlinear change trend of a bleeding rate, an evolution mode of tissue physiological parameters and a dynamic evolution process of a blood coagulation process; external intervention requirements are minimized while a hemostasis strategy is dynamically adjusted through active learning; defining multi-objective optimization modeling of key objectives of hemostasis efficiency, tissue damage minimization and pressure adaptability; estimating distribution prediction of success probabilities of different hemostasis strategies; the intelligent response mechanism is composed of a nanoscale blood coagulation factor carrier and a phase change regulation and control network. An environment triggering response model is constructed, and the blood coagulation factor carrier selectively releases blood coagulation factors according to biological signals including the pH value, the temperature and the bleeding rate.

Introduced here are approaches for capturing and recording medical event details during cardiopulmonary resuscitation (CPR) events and other emergency medical events. A recording begins when an operator inputs a request to initiate recording. The operator may use a recording system with a combination of input devices to record medical actions. The recording system acknowledges the operator's actions by various means which may include icons displayed for the operator. The operator's interactions with the icons indicate corresponding actions observed by the operator, which are recorded as a log of the actions in temporal order that serves as a non-transient record of the medical event. An analysis of the recorded details of the medical event, by comparing the actions to prescribed actions in one or more professional standards, can provide an assessment of the actions taken.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A machine learning-based clinical workflow system processes patient encounter data to generate structured data records and predicted diagnosis classification codes. The system may obtain an input record, generate vector embeddings, and identify reference records using vector similarity operations. A machine learning (ML) component may generate a structured data record based on the input and reference records. The system may also generate an ML instruction, identify reference codes, and produce a predicted code. The predicted code may include a predicted diagnosis classification code, a predicted procedural code, or a billing code. Vector databases storing embeddings of medical codes and records may facilitate efficient retrieval of relevant information. Some implementations may include prediction of billing codes to address revenue capture. Some implementations may utilize specialty-specific processes and data to enhance accuracy for particular medical fields. The system may incorporate clinician feedback to continuously improve performance and adapt to evolving healthcare practices.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention relates to the technical field related to artificial intelligence, in particular to an intelligent medical diagnosis engine based on an AI large model, which comprises an auxiliary diagnosis module for collecting data such as an electronic medical record and a medical image of a patient and predicting an auxiliary result by using an integrated learning method; the drug recommendation module is used for analyzing drug recommendation data by using an AI large model and predicting potential curative effects and side effects of new drugs; and the operation simulation module is used for constructing an operation environment and a human body model in combination with a virtual reality technology. A large amount of medical data can be quickly processed and analyzed through auxiliary diagnosis, the diagnosis time is shortened, gene, physiological and pathological multi-omics data of a patient can be integrated by reducing unnecessary invasive examination and misdiagnosis and drug recommendation, a personalized drug treatment scheme is provided for the patient, and the patient experience is improved. Comprehensive and accurate diagnosis basis and treatment suggestions are provided for doctors; by accurately simulating a real operation scene including mechanical and optical characteristics, a more real training experience is provided for doctors and medical students.

Systems and methods for automatically populating a post-operative report of a surgical procedure are disclosed. A system may include at least one processor configured to implement a method including receiving an identifier of a patient, an identifier of a healthcare provider, and surgical footage of a surgical procedure performed on the patient. The method may include analyzing frames of the surgical footage to identify phases of the surgical procedure based on interactions between medical instruments and biological structures and, based on the interactions, associate a name with each phase. The method may include determining a beginning of each phase and associating a time marker with the beginning of each phase. The method may include populating a post-operative report with the patient identifier, the names of the phases, and time markers associated with the phases in a manner that enables the health care provider to alter the post-operative report.

The invention relates to a personalized medical decision-making method based on artificial intelligence, and relates to the field of medical health and artificial intelligence, and the personalized medical decision-making method comprises the following steps: S1, inputting medical data of a patient in a patient information input module, including historical cases, examination reports, genomic sequences and real-time health monitoring data; and S2, based on the patient information, the patient health comprehensive evaluation module evaluates the health state of the patient, and the medical decision module outputs a preliminary treatment scheme according to the evaluation result. By collecting comprehensive medical data of a patient and comprehensively evaluating the health state of the patient based on the data, the illness state of the patient is more accurately grasped, a more accurate treatment scheme is made, individual differences, including age, physiological indexes, psychological conditions and disease duration, of the patient are fully considered, and by calculating the comprehensive evaluation score of the patient, the patient health state is comprehensively evaluated. The timeliness and effectiveness of a treatment scheme are ensured, and the rehabilitation speed of a patient is increased.