250 results about "Treatment options" patented technology

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

Patients often leave medical appointments with misunderstandings about their diagnoses and treatment plans due to the specialized language used by healthcare professionals. This miscommunication can lead to serious health complications, longer wait times for care, and increased medical costs. This disclosure provides an affordable, scalable solution to this problem through a system that uses natural language processing and large language models to translate medical jargon into plain language that patients can easily understand. The system also prompts patients to ask informed questions and connects them with others who share similar health experiences. Additionally, it simplifies insurance documents, helping patients better understand their coverage and treatment options. By addressing these communication barriers, the system aims to improve health outcomes, enhance treatment adherence, and reduce overall healthcare costs for patients and health systems.

The invention provides an intense pulsed light skin beauty local area identification method and system, and relates to the technical field of phototherapy, and the method comprises the steps: collecting an original face image of a patient, obtaining a historical skin treatment segmentation data set, carrying out the preprocessing of the original face image, and obtaining a standard face image; based on image feature information in the skin treatment segmentation data set, constructing a skin region division model, inputting the facial standard image into the skin region division model for target recognition, and obtaining a plurality of to-be-treated region images in the facial standard image; performing independent component analysis on the to-be-treated area image to obtain a melanin channel and a hemoglobin channel in the to-be-treated area image; constructing a multi-modal attention network model, inputting the melanin channel and the hemoglobin channel into the multi-modal attention network model for feature extraction and segmentation, and respectively obtaining melanin segmentation information and hemoglobin segmentation information; and fusing the melanin segmentation information and the hemoglobin segmentation information to obtain a skin treatment segmentation map, collecting treatment features in the skin treatment segmentation map, classifying the treatment features according to a random forest classifier, obtaining a skin treatment type corresponding to each to-be-treated area image, and obtaining the skin treatment type corresponding to each to-be-treated area image. And a treatment scheme corresponding to the skin treatment type is given. According to the method, the local detail sensitivity and the global information processing efficiency of the skin region division model can be improved.

The invention discloses a positioning puncture surgery robot system with a reconfigurable working space, and the system comprises a multi-mode image collection module which is used for obtaining a high-resolution image of a target region of a patient; the three-dimensional anatomical model reconstruction module is used for generating a three-dimensional anatomical model containing a skeleton structure and a soft tissue structure; the target point selection and puncture path generation module is used for automatically recommending puncture target points according to operation types and clinical requirements and automatically generating a plurality of preliminary puncture paths; the multi-plane segmentation and path injection module is used for segmenting the three-dimensional anatomical model from a coronal plane, a sagittal plane and an axial plane and generating slices, and injecting puncture paths into the corresponding slices; by integrating multi-modal image acquisition, three-dimensional anatomical model reconstruction and automatic path planning and optimization setting, the operation efficiency and accuracy are remarkably improved, the operation risk is reduced through real-time data analysis, and a more personalized treatment scheme is provided for a patient.

The invention discloses an anesthesia assessment big data intelligent supervision system, which comprises a user interface module and a supervision system module, and is characterized in that the supervision system module comprises a multi-source data acquisition and integration module, a real-time data analysis and assessment module, an intelligent early warning and decision support module and a visual report and long-term tracking module; by integrating advanced information technology and medical knowledge, intelligentization and precision of anesthesia management are realized, all modules of the system cooperate with one another, data acquisition, processing, analysis and application are completed together, comprehensive, accurate and personalized anesthesia risk assessment and scheme suggestions are provided for users, and the user experience is improved. According to the method, doctors can be helped to master the condition of a patient more accurately, a more reasonable treatment scheme is formulated, risks and complications in the anesthesia process are reduced, the treatment effect and satisfaction degree of the patient are improved, configuration of medical resources can be optimized, the efficiency and quality of medical services are improved, and powerful support is provided for sustainable development of medical institutions.

The invention discloses a disease prognosis evaluation and dynamic prediction method based on time sequence analysis, which comprises the following steps: acquiring disease prognosis medical data of a patient to construct a medical data time sequence set, extracting medical data time sequence characteristics and constructing a prognosis evaluation model, updating the prognosis evaluation model, and performing dynamic prediction. Inputting the to-be-evaluated patient disease prognosis medical data into the updated prognosis evaluation model to obtain a disease prognosis evaluation result, constructing a prognosis prediction model, inputting the to-be-evaluated patient disease prognosis medical data into the prognosis prediction model to obtain a disease prognosis prediction result, and updating the disease prognosis prediction result. And generating evaluation interpretation, prediction interpretation, a disease prognosis evaluation chart and a disease prediction trend chart. According to the method, the efficiency and accuracy of disease prognosis evaluation and prediction can be improved, timely and effective decision support is provided for clinic, the requirements of personalized medical treatment are met, and more accurate treatment schemes and rehabilitation guidance are provided for patients.

The invention discloses a doctor service method based on a medical large model and a related device. The method comprises the following steps: acquiring service request data of a target doctor in a target service scene; wherein the target service scene is any one of the following medical service scenes: diagnosis decision suggestion, treatment scheme recommendation, medical record generation, medical record quality control and medical record information management; and analyzing the service request data based on the large medical model to obtain a medical service suggestion output by the large medical model to the target doctor in the target service scene. In this way, medical service suggestions can be automatically provided for doctors.

The invention discloses a semantic enhancement auxiliary inquiry method and system based on a medical knowledge graph. According to the method, natural language processing, knowledge graph construction and reasoning and other means are fused, and by means of hierarchical entanglement state tracking, dynamic prompt template design, undirected isomeric graph construction and other methods, patient information is accurately obtained through multiple rounds of dialogues, the semantic understanding and reasoning ability is enhanced, an optimization scheme is provided for intelligent inquiry, and the intelligent inquiry efficiency is improved. And disease classification and treatment schemes can be determined in an assisted manner.

A wearable electronic medical device that stores initial baseline biometric data in memory and determines patient-specific thresholds for monitored biometric parameters. Biometric sensors detect changes in these parameters in response to therapeutic treatment or disease progression, and exceeded thresholds activate actions such as patient notifications, changes in treatment, and transmission of data. Trusted receivers receive suggested diagnoses and treatment options determined by an AI-powered web crawler. The treatment includes electrocuetical and pharmaceutical treatment, and the physiological change includes an indication of a change in the cardiovascular condition. The device determines patient-specific thresholds by applying statistical weighting to monitored biometric parameters and determining an acceptable data set.

The invention discloses a liver cancer image lightweight segmentation method based on UNet, and relates to the technical field of liver cancer segmentation of medical images. According to the method, a lightweight dilated convolution attention module combining local separable dilated convolution and a context anchor attention module is provided, and the feature expression ability is enhanced while the calculated amount is reduced; a self-adaptive wavelet pooling down-sampling module combining wavelet transform and a self-adaptive attention mechanism can perform further feature concentration and extraction on the feature map; a lightweight feature up-sampling and enhancement fusion module composed of a local separable cavity convolution module and a multi-frequency channel attention module can effectively improve the cross-scale feature fusion capability of the model, and maintain a low parameter amount and a low calculation amount. According to the method, while the parameter quantity and the calculation cost are remarkably reduced, the anatomical features of the liver can still be accurately captured, a finer segmentation result is provided, doctors are assisted in optimizing treatment schemes such as tumor excision and living body transplantation, and the clinical decision-making efficiency is improved.

The technology generally relates to systems and methods for predicting the impact of a potential treatment option for a blood vessel based on characteristics of the blood vessel. The characteristic may include, for example, virtual flow reserve (VFR). Different regions of interest may be selected and the value of the characteristic may be predicted based on a potential treatment option being implemented in the selected region. The technology further relates to system and methods for automatically reviewing a classification of a segment of a vessel as stenotic. An artificial intelligence (Al) model may be implemented to generate a lumen mask of the blood vessel within a region of interest. Based on a measure of similarity between the lumen contour determined by a lumen algorithm, different than the Al model, and the lumen mask, an indication that the classification of the segment as stenotic is a false positive may be provided.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

Provided herein are methods and immune biomarkers that identify progression and treatment options for multiple sclerosis (MS). Also provided are materials and methods for the prognosis, staging, and monitoring of MS in a sample and include methods of determining a subject as being at risk of developing MS. The methods include detecting at least one biomarker, such as CXCL13, CXCL10, CD27, NEFL, CCL4, and / or CCL3 in cerebrospinal fluid (CSF) in the subject; and administering a pharmaceutically effective amount of a treatment (e.g., tolebrutinib and potentially one or more additional therapies, such as an anti-CD20 therapy) to the subject.

The invention belongs to the technical field of biological medicine, and particularly relates to morinda officinalis polysaccharide-synanthrin composite gel as well as a preparation method and application thereof. The radix morindae officinalis polysaccharide-synanthrin composite gel provided by the invention is prepared from radix morindae officinalis polysaccharide, synanthrin and water. The radix morindae officinalis polysaccharide-synanthrin composite gel has good biocompatibility and multifunctionality, can intervene in the intestinal microenvironment and renal fibrosis at the same time, and fills the blank of existing treatment methods. The polypeptide has potential curative effects in the aspects of intestinal barrier repair, inflammation regulation and control, microorganism regulation and renal fibrosis inhibition, and a new treatment choice is provided for clinic. The raw materials of the composite gel are based on natural polysaccharides, have good safety and degradability, and meet the high requirements of the medical field on the safety of biological materials. Compared with the existing single-component hydrogel, the composite design of the hydrogel can realize a multi-target effect, the treatment effect is remarkably improved, and the hydrogel has a relatively great market competitive advantage.

A system for managing treatment of a patient is provided with a computer in data communication with a database storing a plurality of prompts, medical content, and patient medical history. A user device in data communication with the computer presents a first prompt to the patient and receives the patient reaction to the first prompt. Software executing on the computer parses the patient reaction into discrete and non-discrete portions, with a generative artificial intelligence program parsing the non-discrete portions to extract a key data set. A determinative artificial intelligence program then correlates the key data set and discrete response portions with the content in the database to create a determinative data set. A generative artificial intelligence program receives the determinative data set and non-discrete portions of the reaction and generates a second prompt to be displayed to the patient to refine their treatment.

The present disclosure relates to methods and systems for providing treatment response predictions based on full slide images. Embodiments disclosed herein relate to a computer-implemented method and corresponding system for providing treatment response prediction (TRP) for a patient suffering from a cancerous disease. Methods and systems are based on obtaining (S10) a full slide image (WSI) of a patient showing a tissue sample associated with a cancerous disease; providing (S20) a prediction function (PF) configured to derive a treatment response prediction (TRP) for one or more treatment options from the full slide image (WSI); and applying (S30) the prediction function (PF) to the full slide image (WSI) in order to provide a treatment response prediction (TRP).

The invention discloses a compound as well as a preparation method and application thereof, and belongs to the technical field of biological medicines. The invention provides a compound which has a structure as shown in a formula I or a formula II, has a remarkable inhibition effect on NSD3 protein expression, and has remarkable killing and growth inhibition effects on lung squamous carcinoma cells. The compound provided by the invention provides a new scheme for treating lung squamous cell carcinoma, and has a good clinical application prospect. # imgabs0 # imgabs1 #

Systems and methods are provided for monitoring health. An exemplary method includes: collecting a first data regarding a patient during an in-office visit; providing a remote monitoring service for remotely monitoring the patient's health; remotely collecting, using the remote monitoring service, a second data of the patient; providing a probabilistic network for assigning metric-based information to the plurality of data using a plurality of conditional probabilities; processing, using the probabilistic network, the first data and the second data using the probabilistic network; generating, using the processed plurality of data, one or more machine learning models for producing a knowledge base trained to recognize pattern types in the data; generating, using the knowledge base, one or more artificial intelligent features for recommending treatment options based on the data regarding the patient; and providing, using the one or more artificial intelligent features, one or more treatment recommendations for improving the patient's health.

The invention provides application of ACT001 in preparation of a medicine for treating spinal cord injury, and relates to the technical field of biomedicine. The parthenolide structure-improved compound ACT001 disclosed by the invention, as a novel neuroprotective drug, has a remarkable effect, and can be used for practically relieving inflammatory response after spinal cord injury, inhibiting formation of glial scars after injury and protecting neurons so as to promote functional recovery. The medicine can improve the lower limb function recovery of the SCI patient, improve the life quality of the patient, and bring a brand new treatment choice for acute and chronic spinal cord injury patients.

The invention relates to the technical field of medical data, and discloses a postoperative rehabilitation progress intelligent evaluation method and system.According to the postoperative rehabilitation progress intelligent evaluation method and system, medical record data and postoperative rehabilitation data of all patients are obtained through a multi-dimensional management module and classified to form a patient data set and a progress data set; the intelligent evaluation module analyzes the operation difficulty of each patient in the treatment process and the postoperative rehabilitation state of the patient according to the patient data set and the progress data set, generates a corresponding difficulty coefficient and a corresponding reaction coefficient, quantifies the operation risk and the physiological response of the patient to the operation treatment scheme, pays attention to the individual difference, and improves the treatment efficiency. The intelligent evaluation module analyzes the change trend of postoperative rehabilitation data of each patient and generates a corresponding trend data set, the dynamic analysis precision is high, the health condition, the postoperative rehabilitation state and the postoperative rehabilitation progress of the patient are evaluated, corresponding rehabilitation suggestions are output, progress feedback is visualized, and the personalized rehabilitation management treatment effect is good.

The invention relates to the technical field of medical information, and discloses a CRRT treatment intelligent safety early warning system based on multi-modal fusion, and the system comprises a data collection and fusion module which is used for collecting multi-modal data from a plurality of heterogeneous data sources in real time, carrying out the standardization and fusion processing of the multi-modal data, and generating fusion data with a unified time sequence; the knowledge model library is used for storing medical knowledge, clinical guidelines and historical data related to CRRT treatment and trained early warning and decision models; according to the system, through multi-modal data fusion and machine learning, the crossing from passive alarm to active early warning is realized, and the safety of a patient is remarkably improved. The intelligent decision support can output an individualized treatment scheme, promote precise medical treatment and optimize resource utilization, the system integration and grading alarm mechanism effectively reduces the cognitive load of medical care, and the self-learning ability of the system ensures that an early warning model can be continuously evolved and promotes the continuous improvement of clinical treatment quality.

The invention provides a method, a device, a system and a program product for predicting an oral cavity state, and the method comprises the steps: obtaining a first three-dimensional scanning image of the same patient in the oral cavity at different times, the first three-dimensional scanning image being used for reflecting the oral cavity state of the patient; extracting one or more characteristic parameters for representing the oral cavity state from the plurality of first three-dimensional scanning images; according to the one or more characteristic parameters of the oral cavity state, evolution information of the oral cavity state is predicted through a prediction model, and the evolution information is used for evaluating the oral cavity health condition of the patient. The evolution information of the oral cavity state can be accurately predicted, and a more reliable basis is provided for diagnosis. The patient can intuitively know the evolution information of the disease, so that the understanding and cooperation of the patient on the treatment can be enhanced. For patients with special conditions, by predicting the evolution information of the oral cavity state, doctors can more accurately judge the treatment opportunity and formulate a more reasonable treatment scheme, and the treatment effect is improved.

The invention relates to the technical field of medical data processing, and particularly discloses a urinary calculus examination image recognition and data visualization analysis and early warning reminding system, which comprises an examination data analysis module, an early warning module, a data processing module and an early warning module, wherein the examination data analysis module is used for extracting calculus incidence related sequence data from user urinary calculus examination image and blood and urine report synchronous sequence data; position distribution, morphological characteristics and biochemical indexes are covered. The data visualization module is used for converting the data into a stone volume dynamic change simulation diagram, a biochemical index abnormal value association map and a stone position dynamic mapping diagram, so that a user can conveniently inquire. And the dynamic threshold reasoning module is used for carrying out space-time alignment and cross-local dimension anomaly fusion reasoning on the visualization result to obtain a user personalized multi-dimensional grading early warning threshold and a clinical decision simulation path. The early warning and report integration module determines a current early warning level according to the latest index and an early warning threshold value, and generates a comprehensive diagnosis and treatment report in combination with visual curve data and a decision path; patients and doctors can conveniently know illness states and formulate treatment schedules.

The invention discloses an intelligent medical service system based on large language model oneself. The system comprises a user information acquisition module; an intelligent recommendation engine; a data analysis and evaluation module; an intelligent algorithm and a big data analysis technology are applied, matched medical resources, treatment schemes or doctors are intelligently recommended according to information provided by a user, through an optimization and feedback mechanism, in the fields of common disease diagnosis and chronic disease management, the diagnosis accuracy can reach 85% or above, and the diagnosis accuracy can reach 85% or above. Personalized health management plans and medical consultation suggestions are customized according to personal information of users, recommendation results are updated and optimized in real time through machine learning and data mining, individuation and precision of services are ensured, a system technical architecture supports self-learning and self-adaption, and by accumulating user feedback and historical data, the health management plan and the medical consultation suggestions are made to be more intelligent. A medical recommendation engine and user experience are continuously optimized, the intelligent level of the platform is gradually improved along with the increase of the number of users, and more accurate and efficient medical recommendation is realized.

Methods and systems for determining an expected disease treatment outcome upon treating a subject, methods and devices for selecting a treatment option for the subject, and methods of treating a subject for a disease, are described herein. The method can include receiving a plurality of subject characteristics for the subject; accessing a tree-based model corresponding to a treatment option for the disease, wherein the tree-based model is generated based on a plurality of prior patient characteristics and an associated treatment outcome for the corresponding treatment option; and determining from the plurality of subject characteristics and the tree-based model, an expected treatment outcome for the subject if the subject were treated with the corresponding treatment option.

The invention discloses an acupuncture data monitoring system based on a brain-computer interface, which belongs to the field of acupuncture data monitoring and comprises a data acquisition module, an acupuncture difference analysis module, a brain wave judgment module and an acupuncture scheme dynamic adjustment and feedback module. The data acquisition module is used for acquiring WorldClim temperature data, Ketz climate classification data, brain wave basic value data of people of different regions and ages, real-time electroencephalogram signals of patients and acupuncture and moxibustion parameters; according to the acupuncture data monitoring system based on the brain-computer interface, acupuncture sensitivity degrees, brain wave threshold values and acupuncture suggestions of patients under different resident climates can be rapidly obtained, accurate matching of personalized treatment schemes is achieved, pertinence and effectiveness of acupuncture treatment are improved, meanwhile, doctors are assisted in dynamically adjusting strategies according to physiological responses of the patients, and the accuracy of acupuncture treatment is improved. And the treatment process is simplified.

The invention relates to the field of medical care informatics, in particular to a doctor-patient collaborative management system and method based on a perioperative period. Comprising the steps of collecting and preprocessing all necessary information of a patient, and performing structured processing on preprocessed medical data to obtain structured medical data; based on the login request of the doctor and the patient, verifying the access authority of the doctor and the patient to obtain a verified login request; based on the verified login request, calling structured medical data; performing real-time analysis and predictive analysis based on the structured medical data to obtain a risk assessment report; based on the risk assessment report, different treatment schemes are assessed and sorted, and doctors are helped to formulate the treatment schemes; and generating a comprehensive report in combination with the treatment scheme, the operation result and the patient satisfaction survey result. The technical problems that when doctor-patient collaborative management in the perioperative period is achieved, medical data cannot be accurately processed, patient health risks cannot be accurately predicted, and management individuation is insufficient are solved.

The invention belongs to the technical field of medicinal chemistry, and relates to 3-amino-4-(2, 4, 5-trifluorophenyl) bornyl butyrate as well as a preparation method and application thereof. The 3-amino-4-(2, 4, 5-trifluorophenyl) bornyl butyrate has the following structure: the 3-amino-4-(2, 4, 5-trifluorophenyl) bornyl butyrate provided by the invention has an excellent hypoglycemic effect, has a low toxic effect on AC16 cells, and provides a treatment choice with better efficiency and higher safety for diabetics.

An object of the system according to the embodiment is to allow a user to efficiently collect information on medical facilities and treatment options and make an optimal selection.SOLUTION: A system includes a reception part, an analysis part, a provision part, and a reservation part. The reception unit receives information on medical facilities and treatment options from a user. The analysis unit analyzes the information received by the reception unit and generates a list of related medical facilities and treatment options. The providing unit provides the user with the list generated by the analyzing unit. The reservation unit performs reservation and inquiry based on the list provided by the providing unit.SELECTED DRAWING: Figure 1