146 results about "Treatment decision making" patented technology

The invention discloses an intelligent emergency pre-examination triage system based on big data, and relates to the technical field of data analysis, the emergency pre-examination triage system comprises a data acquisition module, an evaluation index extraction module, an intelligent analysis module, a triage evaluation module, a triage decision module, a feedback interaction module and a patient information management module, all the modules are connected through electric signals. According to the intelligent emergency treatment pre-examination triage system based on the big data, the comprehensive evaluation model and the triage evaluation coefficient are integrated, and the symptoms of the patient are evaluated quickly and accurately, so that the triage level and the recommended treatment department are quickly determined, the emergency treatment efficiency is remarkably improved, the waiting time of the patient is shortened, and the patient experience is improved. According to the method, medical resources are configured more reasonably, meanwhile, more comprehensive patient portraits can be provided for medical staff according to multi-dimensional information such as medical history and examination results of patients, and making of more accurate diagnosis and treatment decisions is facilitated.

The invention discloses a failure restoration retreatment decision-making method based on dynamic occlusion analysis, and the method comprises the steps: constructing a four-dimensional virtual biomechanical model which comprises a patient anatomical structure and a dynamic function through multi-modal data fusion, and enabling the model to be embedded into a real three-dimensional mandibular movement track of a patient to simulate functional movement, thereby breaking through a static analysis blind area, and achieving the real three-dimensional mandibular movement of the patient. The missed diagnosis rate of dynamic interference is reduced; based on quantitative indexes and thresholds such as occlusal contact displacement, occlusal contact distribution symmetry and adjustment quantity of the maximum tooth tip staggering position and the median relation position, experience judgment of doctors is replaced, objectivity, repeatability and accuracy of failure repair and retreatment decision are improved, and excessive or insufficient adjustment is avoided; the treatment effect rehearsal is realized through virtual presetting, the irreversible operation risk is avoided, and the number of times of re-visit and the trial and error cost of a patient are reduced; and meanwhile, the four-dimensional virtual model can be connected with transition and design and manufacturing of a final restoration, so that the accuracy, safety and efficiency of retreatment of a failed restoration are improved.

The invention discloses a depression personalized treatment decision-making system based on a knowledge graph, and belongs to the technical field of medical aid decision-making. A collection module is designed to collect patient related data, and then a construction module is used to construct the knowledge graph; the knowledge graph is optimized through the optimization module, a personalized auxiliary treatment scheme is output through the personalized output module, auxiliary decision making is provided for treatment of a patient, and the method has the advantages that by integrating multi-source data and constructing the high-quality knowledge graph, a new thought and method are provided for diagnosis and treatment in the medical field. The application also provides a depression personalized treatment decision-making system based on a knowledge graph by combining an artificial intelligence technology, then realizes optimization of the knowledge graph through operations such as entity extraction, relation extraction, attribute extraction, entity alignment, entity linking, relation deduction and the like, and finally realizes personalized treatment decision-making of depression through multi-modal data analysis and knowledge graph reasoning. And intelligent support is provided for depression diagnosis and treatment.

The invention belongs to the technical field of medicine, and discloses a method and system for predicting anastomotic fistula occurring after colorectal cancer operation, and the method comprises the steps: obtaining clinical pathology data and laboratory analysis data of a patient; filling missing data in the clinical pathology data and the laboratory analysis data; screening key indexes by using a single-factor regression analysis technology; performing multi-factor regression analysis on the key indexes, and screening to obtain independent prediction factors for predicting whether the colorectal cancer patient has the anastomotic fistula or not after operation; based on the independent prediction factors obtained through screening, an anastomotic fistula prediction model is constructed and trained, and the trained anastomotic fistula prediction model is used for predicting the postoperative anastomotic fistula occurrence probability of the colorectal cancer patient. The method can more accurately predict the probability of occurrence of anastomotic fistula of a colorectal cancer patient after an operation, has better accuracy and reliability, can facilitate preoperative prediction of the probability of occurrence of anastomotic fistula for clinical doctors, and assists in guiding treatment decision.

The invention provides a blood transfusion treatment scheme recommendation system and method based on historical information, and relates to the field of medical information.The blood transfusion treatment scheme recommendation method comprises the steps that firstly, basic information and diagnosis information of a patient, real-time blood test information and historical blood transfusion medical record information are extracted from HIS, LIS, EMR and TMIS systems; performing joint semantic analysis on the basic information, the diagnosis information and the real-time blood test information, and converting the historical blood transfusion medical record information into a dynamic vector sequence; then, the current blood index and the historical blood transfusion time sequence are dynamically matched in a frequency domain space through nonlinear spectrum analysis, and a personalized recommendation scheme is generated in combination with the implicit reasoning ability of a pre-trained medical large model, so that the limitation of a traditional system in the aspects of static parameter setting, individual adaptability and semantic mining is broken through; and the accuracy and timeliness of blood transfusion treatment decision making are remarkably improved.

The invention discloses a breast disease corpus construction method based on a BioClinicalBERT model, through a breast MRI corpus construction method, in combination with man-machine collaborative labeling and an advanced self-supervised learning technology, discriminative features are extracted from a breast MRI image, and through calculation of multistage similarity, the accuracy and efficiency of breast MRI diagnosis are effectively improved. Powerful support is provided for early diagnosis and treatment of breast diseases, and meanwhile the wide application potential in other medical image analysis and diagnosis fields is shown due to the universality. And when the method is applied to the case text standardization method, the case texts not only contain accurate structured tags and medical image information, but also have high readability and understandability. A doctor can quickly browse the medical record text and accurately obtain key information, so that diagnosis and treatment decisions can be more efficiently made.

The invention discloses an intensive care treatment scheme intelligent decision-making system based on data driving, and relates to the technical field of medical information, and the system comprises a data collection module which is used for obtaining multi-source clinical data of an intensive care unit in real time; and the decision generation module is connected with the data acquisition module and is used for generating a treatment scheme according to the clinical data. According to the intensive care treatment scheme intelligent decision-making system based on data driving, through four-dimensional snapshot construction of a dynamic decision-making chain and medical interpretable evolution relation labeling, continuous logic of an intensive care decision-making is guaranteed, data abnormal driving change and medical logic jump risks are effectively identified, medical errors caused by decision-making faults are reduced, and the decision-making efficiency is improved. The abstract decision-making process is converted into a concrete evidence chain capable of penetrating auditing, and the clinical responsibility traceability and the doctor-patient credibility are improved.

The invention relates to the technical field of medical data processing, and discloses a construction method and application of a systemic lupus erythematosus activity evaluation model. Expression level data of CD177 on the surface of neutrophil, anti-dsDNA antibody titer data, anti-dsDNA antibody concentration data, complement C3 concentration data and complement C4 concentration data are combined, a machine learning algorithm is utilized, and the systemic lupus erythematosus activity evaluation model is constructed. A high-precision systemic lupus erythematosus activity evaluation model is obtained through training, systemic lupus erythematosus activity can be evaluated efficiently, comprehensively and accurately, and an SLE patient is effectively assisted in seeing a doctor in time and making a correct treatment decision.

The invention discloses a detection kit and a detection method for B-type botulinum neurotoxin. According to the method, qualitative detection of various suspected botulinum neurotoxin poisoning cases can be completed in a short time, evidence support is provided for assisting doctors in diagnosis and timely performing treatment decision making, and the method has important significance in the aspect of social health safety. Meanwhile, the detection method disclosed by the invention can be used for remarkably improving the specificity of toxin protein, avoiding the problem of long time and the like caused by animal experiments such as serotype identification and the like, and can be applied to research, development and production processes of botulinum neurotoxin products. In addition, quality control can be carried out on the stability, the shelf life and the like of the botulinum neurotoxin standard substance.

The invention discloses a layering and preference learning-based personalized dynamic treatment strategy generation system for Parkinson's disease, which comprises a dynamic state and preference representation module, a preference-based reward function learning module, a layering treatment strategy generation module and a treatment path simulation and interactive output module, the system learns preferences from doctor-patient interaction and generates layered, dynamic and explainable personalized long-term treatment strategies, so that making of personalized long-term treatment decisions is achieved, and the problems that in an existing Parkinson's disease reinforcement learning scheme, an award function is fixed, and long-term planning ability is lacked are solved.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.

The invention provides a dental implant data monitoring analysis method based on deep learning, and the method comprises the steps: collecting the multi-modal structural data of a patient, such as preoperative images, clinical examination and surgical parameters, carrying out the standardized preprocessing, and inputting the data into a deep neural network model with a double-branch structure, thereby achieving the extraction of the implant failure risk score and local causal association strength; on the basis of model output, a dynamic individualized causal map is constructed, key clinical intervention variables serve as regulation and control nodes, anti-fact disturbance simulation is carried out, variable intervention sensitivity is quantified, and a key causal path is analyzed; according to the method, a priority intervention suggestion list and an auxiliary decision report are generated, the causal atlas can be adaptively optimized according to actual follow-up feedback, long-term closed-loop evolution is realized, the accuracy of implant failure risk prediction and the pertinence and operability of an intervention scheme are improved, and clinical individualized treatment decision support is enhanced.

The invention discloses a death rate prediction method and device based on structured medical data and a large language model, a storage medium and electronic equipment, and belongs to the technical field of natural language processing and the technical field of artificial intelligence. The technical problem to be solved by the invention is how to automatically extract important features related to death rate from electronic health record data of a patient and construct a multi-source information fusion prediction model to assist doctors in early intervention and accurate treatment decision. According to the technical scheme, (1) the death rate prediction method based on the structured medical data and the large language model comprises the following steps: S1, constructing a patient electronic health record data set; s2, constructing a death rate prediction framework; and S3, training the death rate prediction model. And (2) a death rate prediction device based on the structured medical data and the large language model, wherein the death rate prediction device comprises a patient electronic health record data set construction unit, a death rate prediction framework construction unit and a death rate prediction model training unit.

The invention relates to the technical field of biomedicine and artificial intelligence, in particular to a biological material implantation effect prediction system based on deep learning. According to the method, the deep learning model is combined with the convolutional neural network and the long-short-term memory network, multiple data types are effectively fused, the accuracy and robustness of implantation effect prediction are improved, potential operation risks and complications can be identified, and a scientific treatment decision basis is provided for doctors. And the signal matching and feedback module compares the prediction result with historical case data to generate feedback signals including high matching, a re-evaluation scheme and a risk warning signal. The system dynamically adjusts a treatment scheme according to feedback to ensure efficient intervention. Through intelligent prediction, subjective judgment is reduced, the utilization efficiency of medical resources is improved, invalid treatment and excessive treatment are avoided, and meanwhile, a treatment scheme is optimized by referring to historical cases.

The present disclosure relates to a system for treatment decision support of a patient for a plurality of treatment options for treatment of type 2 diabetes mellitus, wherein the system comprises: a processing unit; a memory; an interface; an input device; wherein, the memory comprises instructions that, when executed by the processing unit, carries out a method for treatment decision support of a patient for a plurality of treatment options for treatment of type 2 diabetes mellitus.

The invention belongs to the technical field of medical information, and particularly relates to a newly diagnosed HIV infected person antiviral treatment intelligent decision support system which comprises a multi-source data integration module, a medicine knowledge module, an individualized decision support module, an interactive output module, a knowledge optimization module and a safety and privacy protection module. The system replaces doctors to query and integrate patient data across multiple systems, clinical time is saved, key information omission is avoided, comprehensive data support is provided for treatment decisions, experience decisions are converted into systematic analysis and graded risk prompts are generated by means of an individualized decision support module, scheme deviation is effectively reduced, the medication risk is reduced, and the treatment efficiency is improved. The method assists the basic level in improving the treatment normalization, allows doctors to input new experience and new achievements summarized in practice into the system after double-blind auditing, and provides continuously iterative knowledge support for the scientificity of the antiviral treatment decision of the newly diagnosed HIV infected person.

The invention relates to an intelligent auxiliary system for differentiated thyroid cancer postoperative evaluation and iodine-131 treatment decision, and belongs to the field of medical technology and artificial intelligence, and the intelligent auxiliary system comprises a medical record data collection module which is used for receiving an auxiliary evaluation instruction and obtaining medical record data; the pathological feature extraction module is used for extracting pathological features from the medical record data through a preset analysis model; the analysis result output module is used for analyzing the pathological features through the analysis model and a pre-constructed thyroid cancer postoperative diagnosis and treatment decision tree and outputting reference suggestions so that doctors can know the reference suggestions; wherein the reference suggestions at least comprise reference suggestions for iodine-131 treatment. The application has the effects of improving the thyroid cancer postoperative diagnosis and treatment quality of doctors and improving the diagnosis and treatment standardability and efficiency.

This invention relates to the field of medical imaging systems, specifically disclosing a multimodal developmentally adapted intelligent diagnostic and differentiated treatment decision-making system for pediatric diseases. The system includes a multimodal data acquisition module for children, a developmental deviation quantification module, a physiological adaptive image standardization module, a multimodal physiological feature encoding module, a developmental constraint prediction model construction module, and a longitudinal incremental iteration module. Based on the DDI index and developmental grading, this invention generates spatial transformation and grayscale normalization parameters adapted to the child's developmental state, eliminating anatomical scale differences, refining the registration of key anatomical structures, dynamically adjusting deformation constraints, preserving core developmental and pathological features, avoiding registration artifacts, and establishing a solid foundation for high-precision data.

Provided herein are methods for predicting chemotherapy benefit. The invention predicts chemotherapy benefit based on the expression analysis of biomarkers, e.g., RNA biomarker transcription analysis, taken from a tumor sample. The biomarker expression data can be combined with clinical variables, e.g., tumor size and nodal status, to generate a profile that predicts the benefit of including chemotherapy as a treatment decision.

Embodiments pertain to a method of assessing the cardiometabolic health of a subject by (1) receiving a plurality of parameters of the subject, where the plurality of parameters includes a T2 relaxation time constant of a subject's blood (T2 value) and one or more additional parameters; and (2) feeding the plurality of parameters into an algorithm, where the algorithm correlates the plurality of parameters to the subject's cardiometabolic health. The methods may also include a step of communicating cardiometabolic health risk. The methods may also include a step of implementing a treatment decision. Further embodiments pertain to system for assessing the cardiometabolic health of a subject in accordance with such methods.

The present disclosure encompasses methods for molecularly subtyping formalin-fixed paraffin-embedded tumor samples. The disclosure works particularly well for old and degraded (archival) samples for which standard methods are unfeasible. Further, the methods disclosed allow for the correlation of patient outcome data with the molecular subtype of the tumor and provides a wealth of information which will guide treatment decisions and / or selection of therapeutic agents.

The present invention discloses a detection kit and a detection method for type B botulinum neurotoxin. The present invention can complete qualitative detection of various suspected cases of botulinum neurotoxin poisoning in a short time, provide evidence support for assisting doctors in diagnosis and timely treatment decisions, and is of great significance in social health and safety. At the same time, the detection method of the present invention can significantly improve the specificity of toxin proteins, avoid the time problems caused by animal experiments such as serotype identification, and can be applied to the research and development and production process of botulinum neurotoxin products. In addition, the present invention can perform quality control on the stability and shelf life of botulinum neurotoxin standards.

The application provides a cross-institutional referral intelligent routing method and system based on a medical intelligent agent middleware, relates to the technical field of medical informatization, and comprises the following steps: establishing a sign data monitoring window, mapping sign index changes and medication record time sequences, constructing a drug reaction sensitivity curve to calculate a treatment effective coefficient; adopting a dynamic time warping algorithm to match a drug-sign correlation graph, and generating a treatment decision tree; and predicting disease development, identifying the best referral opportunity and target institution based on the treatment effective coefficient and specialty characteristics. The application improves cross-institutional referral efficiency, optimizes medical resource allocation, and enhances patient treatment effect.

The present application relates to a myocardial damage assessment method, an apparatus, a sample analysis system and the use thereof. The method, the apparatus and the sample analysis system can quickly and accurately perform assessment with respect to the cause and risk of myocardial damage of individuals, thus providing guidance for diagnosis decision making and treatment decision making.

The present invention discloses a COPD big data management platform and a construction method thereof, and belongs to the field of chronic disease health management. The present invention constructs a Chinese and Western medicine standard medical health database and a Chinese and Western medicine characteristic prevention and treatment technology database by effectively integrating multi-source data, realizes data standardization, eliminates data redundancy, and improves data quality. The Chinese and Western medicine standard medical health database, the Chinese and Western medicine characteristic prevention and treatment technology database, and a dedicated medical system interface are integrated to establish a COPD big data center, laying a foundation for subsequent data analysis, mining, and application, making the health and medical data of COPD patients more reliable and easy to analyze, thereby providing more valuable support for Chinese and Western medicine clinical decision-making and health management, and medical workers can also obtain the required information faster and make more accurate diagnosis and treatment decisions. In addition, the present invention also establishes an auxiliary engine that can help medical staff and patients better obtain information and feedback.

The RGMA gene polymorphism biomarker, the kit and the application can be effectively used for disease course monitoring, prognosis evaluation and precise treatment decision of neuromyelitis optica pedigree diseases, and are rapid, convenient and accurate and reliable in result. Once a patient suffering from the neuromyelitis optica pedigree disease is diagnosed, accurate prediction can be provided for future outcome of the disease only through one-time detection, a basis is provided for clinical treatment decision, meanwhile, a certain basis is provided for follow-up fundamental research and clinical research, and the method has high application and research value. The invention relates to an RGMA (arginine-glycine-aspartic acid) gene polymorphism biomarker, which comprises rs725458-CC and rs4778099-AA (arginine-glycine-aspartic acid).

Improved digital interfaces and graphical user interfaces for analyte monitoring systems are provided. For example, disclosed herein are various embodiments of methods, systems, and interfaces for a self-check questionnaire. In addition, various embodiments of interfaces for a treatment decision guide and related knowledge check are described.

The disclosure relates to targeted Chromatin Accessibility and Mutation Sequencing (tCAM-seq) for parallel analysis of genetic and epigenetic analysis of patients with pancreatic adenocarcinoma (PDAC) to 1) determine responsiveness to standard of care chemotherapy, and 2) identify actionable allelic variant mutations that can be used to guide treatment decisions. Prognostic methods and treatment strategies are also provided.

The invention provides a knee osteoarthritis treatment decision-making tree system and device, and relates to the technical field of knee osteoarthritis treatment decision-making trees, and the system comprises a data receiving module, a data processing module and a result generation module. The data receiving module is used for receiving natural language text data and iconography image data of the patient and respectively preprocessing the natural language text data and the iconography image data; the data processing module comprises a text feature unit, an image feature unit and a decision unit; the decision-making unit is used for fusing the text features and the image quantization features to obtain comprehensive features; based on the comprehensive features, through a decision tree model, a treatment scheme for knee osteoarthritis is output; and the result generation module is used for sending out the treatment scheme. According to the scheme, information such as medical records of patients can be automatically analyzed, personalized treatment schemes are given, interference of human factors is reduced, real-time and objective decision support is provided for doctors, and therefore the diagnosis and treatment efficiency and effect are improved.

The invention provides a cross-institution referral intelligent routing method and system based on medical agent middleware, and relates to the technical field of medical informationization, and the method comprises the steps: building a physical sign data monitoring window, carrying out the time sequence mapping of physical sign index changes and medication records, constructing a drug response sensitivity curve, and calculating a treatment effective coefficient; adopting a dynamic time warping algorithm to match the medicine-physical sign association map, and generating a treatment decision tree; disease progression is predicted based on the effective treatment coefficients and specialized specialities, and the optimal referral opportunity and target mechanism are identified. According to the invention, the cross-mechanism referral efficiency is improved, the medical resource configuration is optimized, and the patient treatment effect is enhanced.