789 results about "In patient" patented technology

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

The present disclosure provides methods for the acute treatment of migraine with or without aura, comprising the administration of ubrogepant. In particular, the present disclosure provides methods for the acute treatment of migraine in patients having hepatic impairment; in patients with renal impairment; and in patients concurrently taking CYP3A4 modulators or BCRP and / or P-gp only inhibitors.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The present disclosure provides methods for the acute treatment of migraine with or without aura, comprising the administration of ubrogepant. In particular, the present disclosure provides methods for the acute treatment of migraine in patients having hepatic impairment; in patients with renal impairment; and in patients concurrently taking CYP3A4 modulators or BCRP and / or P-gp only inhibitors.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses an esophageal cancer prognosis risk analysis method and system based on machine learning, and a medium, and relates to the technical field of artificial intelligence technology and bioinformatics, and the method comprises the steps: 1, collecting multi-modal data of a patient, and carrying out the standardization processing, so as to construct a stable feature set; 2, constructing a plurality of machine learning models, realizing high and low risk group prediction of each machine learning model based on stable feature set modeling, and determining the machine learning model and an optimal feature set according to the high and low risk group prediction; and step 3, calculating SHAP interaction values among the features in the optimal feature set, drawing an interaction value curve according to the SHAP interaction values to obtain TopA interaction feature pairs, taking the interaction feature pairs as newly constructed features to be included in the original feature set in the step 1, and repeating the feature screening process in the step 1 and the step 2 to obtain an optimal machine learning model. Predicting high and low risk groups of patients; according to the prognosis risk analysis method, the detection specificity of high and low risk groups of local advanced esophageal cancer patients is greatly improved.

Photoacoustic medical-device navigation systems and methods provide alternatives to those incorporating fluoroscopy for medical-device navigation in patient bodies. A medical-device navigation system can include an optical-fiber stylet, ultrasound transducers, and a console. The stylet can transmit light to an instant location of a distal tip of an elongate medical device in a patient's body and, thereby, irradiate endogenous chromophores to generate ultrasound-frequency photoacoustic pressure waves therefrom. The ultrasound transducers can detect ultrasound signals corresponding to the photoacoustic pressure waves. The console can instantiate medical-device navigation processes for navigating the elongate medical device via the stylet as the elongate medical device is advanced to the target location in the patient's body. The medical-device navigation processes can include acquiring ultrasound-signal data from the ultrasound transducers, reconstructing images from the ultrasound-signal data, and displaying reconstructed images on a display for navigating the elongate medical device to the target location in the patient's body.

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

This disclosure is directed to systems and techniques for detecting change in patient health based upon peak and non-peak patient activity data. In some examples, the peak and non-peak patient activity data correspond to one or more peak (time) periods and the one or more non-peak periods, respectively, where at least one peak period and at least one non-peak period corresponds a highest activity level and a lowest activity level, respectively, for a single day. If a change in patient health is detected, the techniques described herein may direct a medical device to generate for display output indicating the detection of the change in patient health.

The invention relates to a gouty arthritis patient pain management method and system based on an MDT management mode, and the method comprises the steps: automatically collecting the multivariate clinical data of a patient through integrating an existing information system of a hospital, carrying out the cleaning, standardization and structural processing through a special data preprocessing module, and carrying out the cleaning, standardization and structural processing. A machine learning prediction model trained by historical data is utilized to automatically generate a personalized MDT nursing plan, tasks are intelligently distributed to corresponding team members by means of a workflow engine, the execution condition is continuously monitored in the intervention process, and the nursing scheme is dynamically adjusted based on clinical data and prediction results which are periodically updated. And a complete'monitoring-prediction-intervention-reevaluation 'closed-loop management process is formed. The problems that a traditional MDT mode depends on manpower, efficiency is low, and decision-making lacks data support are effectively solved, and the accuracy, the collaboration and the continuity of pain management and wound surface nursing of the gouty calculus combined infection patient are improved.

The invention of the gradient porous structured sacral prosthesis and the manufacturing process aims to create an alternative replacement for bone loss in patients who require bone replacement. The gradient porous structured sacral prosthesis consists of an artificial bone fragment, a metal rod, and a set of screws for fixing the fragments. The manufacturing process consists of seven steps: Identifying the position and boundaries of the bone, First-step topology optimization, Second-step topology optimization, Defining the structural pattern and unit cell size, Designing the artificial bone implant, Mechanical performance testing of the implant, and Fabrication of the sacral implant. The devices and processes according to the present invention are clearly sequenced and characterized. Modification or change in the manufacturing sequence or use may reduce the capabilities of the present invention or make it unusable for the purposes of the present invention.

The invention belongs to the technical field of radiooncology, artificial intelligence and medical image fusion, and particularly relates to a model construction method and system for predicting the radiation pneumonitis risk of a patient receiving immune combined radiotherapy. According to the method, for a non-small cell lung cancer patient receiving immune checkpoint inhibitor (ICIs) combined radiotherapy, clinical information and radiotherapy dose parameters of the patient are collected, a positioning CT image and dynamic immune factor data are simulated, multi-source features including radiomics features, an immune change curve and dose volume indexes are extracted, and a multi-modal fusion prediction model is constructed. The system integrates automatic data input, feature extraction, model training and risk output modules, can output individual radiation pneumonitis risk scores, provides risk grading and dose intervention suggestions, and has high prediction accuracy and clinical interpretability. The method can be widely applied to individualized risk management and precise treatment of non-small cell lung cancer immune combined radiotherapy patients.

The invention discloses an intelligent sorting engine system for multiple examination tasks in a hospital treatment scene, which comprises a data acquisition module, an intelligent analysis module, a sorting module, a task pushing module, a feedback module, an emergency adjustment module and a module connected with a hospital reservation system, and efficient and reasonable checking task sorting and management are realized. Through a multi-source data fusion technology, data of each information system in a hospital can be integrated, and part of examination result data of other medical institutions can be obtained through a data interface with external medical equipment. Therefore, a doctor is ensured to have comprehensive and accurate patient information during diagnosis, repeated examination is avoided, and the utilization efficiency of medical resources is improved. A dynamic planning algorithm is adopted to quickly adjust the sorting of the examination tasks according to the real-time changing doctor seeing condition and resource condition, and a personalized examination task execution sequence is generated by considering factors such as the psychological state and the comfort level of a patient.

Described herein are antibodies, particularly monoclonal antibodies, that specifically bind to human HER2 and are useful for the treatment of cancer in patients.

The invention relates to the technical field of chronic obstructive pulmonary disease rehabilitation medical equipment and services, discloses a chronic obstructive pulmonary disease multi-mode respiratory training system based on behavioral design science, and aims to solve the technical problems that an existing respiratory training product is poor in patient compliance, poor in user experience and lack of personalized behavior guidance. The system comprises a hardware equipment cluster and a software service platform, a multi-dimensional intervention architecture is constructed based on an FBM behavior model, and closed-loop services of respiratory training, drug management, health monitoring and rehabilitation support are realized through an motivation promotion module, a capability optimization module, a multi-sensory prompt module and a data interaction module. The system effectively stimulates the training enthusiasm of a patient, reduces the operation threshold, improves the training and medication compliance, improves the respiratory function and life quality of the patient, reduces the consumption of medical resources, and has remarkable clinical application value and social and economic benefits through deep fusion of behavioral design and a multi-modal rehabilitation technology.

Treatment of Cancer Methods for treatment of cancer, in particular soft tissue sarcomas (STS), are described, as well as compounds, compositions, and combined preparations for use in such methods. An effective amount of: a LAG-3 protein, or a derivative thereof that is able to bind to MHC class II molecules; a programmed cell death protein-1 (PD-1) pathway inhibitor; and radiotherapy, is administered to a subject. Optionally the subject has undergone, is undergoing, or is to undergo, surgery for treatment of the cancer. A clinical trial study is described in which a triple combination of LAG-3 derivative eftilagimod alpha, a PD-1 pathway inhibitor, and radiotherapy was administered. The combination was surprisingly effective in the treatment of STS in patients who have completed 10 weeks of treatment, followed by surgery 2-3 weeks later. A substantial number of near complete pathological responses were observed, which are rarely seen in STS patients with standard therapeutic approaches including radiotherapy.

An implantable medical device, illustratively an elastic bar, for the gradual correction of pectus excavatum in patients.

The application relates to the field of restraint devices, and discloses a self-adaptive intelligent restraint device, which comprises a restraint bed, the top of the restraint bed is sequentially provided with an ankle fixing ring, a thigh restraint ring, a waist and abdomen restraint ring, a wrist restraint ring and a shoulder restraint belt from left to right, the top of each of the four restraint rings and the restraint belt is provided with a fastening restraint belt with different sizes, the outer side of the restraint bed is provided with a detection mechanism, one end of the fastening restraint belt is provided with a quick connection mechanism, and the other end of the fastening restraint belt is provided with an adjusting mechanism. The patient pressure is accurately calculated through a strain gauge, the fastening of the restraint belt is automatically adjusted by a motor after comparison with a preset value, the safety, comfort and real-time performance of the restraint are ensured, in addition, the device is also integrated with a quick connection mechanism convenient for medical staff to operate and a function of sending an alarm to a nurses' station through Bluetooth when the patient is agitated.

A quality assurance method for a radiotherapy device is disclosed. The method comprises disposing a phantom on a patient couch of the radiotherapy device, moving the patient couch to simulate motion of a subject, determining a measured value associated with the phantom, and comparing the measured value to an expected value. A radiotherapy device is also disclosed, the radiotherapy device comprising a radiation source configured to apply a radiation beam, a patient couch, a phantom disposable on the patient couch, and a controller communicatively coupled to the patient couch. A computer-readable medium storing instructions is also disclosed. A phantom for a radiotherapy device is also disclosed.

The invention relates to the technical field of brain function network analysis, in particular to a post-stroke aphasia patient fMRI focal recognition method based on deep learning. The method comprises the following steps of: acquiring a region consistency value for geometric structures of matched local regions of two adjacent fMRI images in an fMRI image sequence, similarity of gray distribution and an image matching degree, and dividing a same time window by using the region consistency value; and obtaining a functional connection coefficient according to the difference of the region consistent values of the fMRI images in two adjacent time windows and the difference of the geometric structure features and the gray level distribution features of the local regions, determining the convolution kernel size of deep learning of the fMRI images in the time windows by using the functional connection coefficient, and performing focal recognition on the fMRI images in the time windows. According to the method, the function connection coefficient presenting the cooperation degree of the brain region activity modes in the adjacent time windows is analyzed, the convolution kernel size of the time windows is determined in a self-adaptive mode, and the recognition effect on the local focus of the patient is improved.

The invention discloses a multi-modal drug recommendation method and system based on large language model driving, and relates to the technical field of medical informatics and artificial intelligence. The invention aims to solve the limitation of an existing drug recommendation model in the aspects of patient characterization construction, drug multi-modal modeling and large language model application. Comprising the following steps: constructing patient characterization, and aligning a patient state and a potential medication space in a characterization learning stage through a cooperative prompt project driven by a large language model; multi-modal drug characterization is constructed, and clinical logic and chemical characteristics of drugs are deeply depicted through semantic expert and molecular structure expert dual-channel design; a'static anchoring dynamic 'time sequence reasoning mechanism is provided, context modulation is performed on a dynamic evolutionary clinical event sequence by utilizing a static treatment baseline formed by global medication history, and finally, an accurate and safe medication combination is generated through a label sensing prediction module. According to the method, the accuracy, safety and clinical logic self-consistency of drug recommendation are remarkably improved.

The invention relates to the field of metabolite detection, in particular to an endogenous metabolite detection device and method for metabolic disorder patients. The method comprises the following steps: segmenting a Raman spectrum curve of an endogenous metabolite of a patient to obtain a plurality of fluctuation segments and a reference baseline value of each fluctuation segment, screening out a normal fluctuation segment, and combining the reference baseline value of the normal fluctuation segment to obtain a normal baseline value of the Raman spectrum curve; adjusting each fluctuation section of the Raman spectrum curve according to the difference between the reference baseline value and the normal baseline value of each fluctuation section to obtain an adjusted spectrum curve, denoising the adjusted spectrum curve to obtain a final spectrum curve, and detecting the endogenous metabolites of the patient. According to the invention, the baseline drift phenomenon of the Raman spectrum curve of the endogenous metabolite of the patient can be eliminated, and the Raman spectrum data quality and the detection accuracy of the endogenous metabolite are improved.

The invention provides a tracheal catheter fixing and patient falling prevention linkage nursing device. The device comprises a tracheal catheter fixing module, a falling prevention detection module, an alarm intervention module and an interaction management module. The tracheal catheter fixing module is used for ensuring that the tracheal catheter does not slip or shift in the body position change or nursing process of a patient; the anti-falling detection module is used for monitoring the body position change of a patient on the nursing bed in real time; the alarm intervention module is used for giving an alarm to medical staff when potential dangers are monitored; the interaction management module is used for optimizing a risk reminding strategy in combination with medical care feedback of the device in the use process; by means of the linkage mechanism of catheter fixing and body position monitoring, accurate recognition and intelligent early warning of tracheal catheter slipping and the risk that a patient falls off a bed are achieved, and the safety and the active intervention capacity of clinical nursing are remarkably improved.

Existing pulse oximeters have impaired accuracy in measuring blood oxygen levels (SpO2) in patients of color, where the presence of melanin in the skin tends to interfere with measurements of pulse oximetry, resulting in an overestimation of SpO2. Thus, the pulse oximeter often reports a higher oxygen saturation for patients with darker skin tones, often with increasing bias as the actual saturation (SaO2) decreases. This bias is further reinforced by calibration based on individuals with light skin pigmentation, which may lead to inequitable healthcare for patients of color. Our proposed solution is to modify current pulse oximetry calculations—which utilize the relative tissue absorbance of red and infrared light to estimate SpO2—to account for the concentration of melanin by additionally measuring the skin absorbance of UV˜A light. This derived concentration of melanin can then be used to modify the pulse oximetry algorithm output, thus estimating SpO2 more accurately for patients of color.

A system and method for generation and use of radiation outcome prediction (response & side effects) score for patients undergoing radiotherapy for various medical conditions wherein the score is a personalized score, provided by analyzing multiple parameters including the tumor specific, patient specific, gene specific and treatment planning specific parameter(s), during and post therapy.

This invention discloses a method for locating epileptogenic zones based on high-frequency oscillations and connectivity, comprising the following steps: 1. Acquiring SEEG data and selecting representative channels; 2. Filtering to acquire 54-200Hz signals and selecting baseline and target data; 3. Acquiring normalized high-frequency energy; 4. Calculating time and energy coefficients to obtain a high-frequency epileptogenicity index; 5. Filtering to acquire 12-45Hz signals; 6. Calculating nonlinear regression analysis; 7. Calculating total intensity; 8. Defining and calculating the connectivity high-frequency epileptogenicity index and evaluating performance. This invention can accurately locate epileptogenic zones in patients with different seizure patterns, and has potential application value in epilepsy treatment.

The auxiliary support comprises a bottom plate, one end in the bottom plate is rotationally connected with a fixed hinge shaft, the two sides of the fixed hinge shaft are hinged to one ends of movable connecting rods respectively, and the other ends of the movable connecting rods are rotationally connected with the two ends of a movable hinge shaft respectively. The middle of the supporting shaft is fixedly connected with a connecting base, one end of the connecting base is fixedly connected with one end of a threaded rod, and the other end of the threaded rod is connected with one end of an inner threaded cylinder in a sleeved mode through a threaded structure. The other end of the internal thread cylinder is rotationally connected with a clamping block, the clamping block is clamped and matched with a clamping groove, and the clamping groove is concavely formed in the middle of the top surface of the bottom plate; sliding openings are formed in the two sides of the bottom plate respectively, adjusting bases are slidably arranged in the sliding openings, one ends of the adjusting bases are hinged to one ends of supporting connecting rods, and the other ends of the supporting connecting rods are hinged to one sides of movable connecting rods respectively.

A deep learning method, system, device, and medium for drug recommendation are disclosed. The method includes: establishing drug representations; establishing patient representations; and generating drug prediction results. These three steps first utilize an RNN to establish patient representations, then integrate patient diagnostic information, treatment information, and historical medication information, employing dual attention to assign weights at different element levels in patient history and single visits, and finally using a neural network for drug recommendation. Furthermore, patient similarity can be incorporated for drug recommendation, effectively improving the accuracy and safety of drug recommendations. The system, device, and medium, through the storage and utilization of relevant functional modules, realize drug recommendation using deep learning methods. As a medical auxiliary tool, it can significantly improve the discrimination quality and work efficiency of pathologists.

The present disclosure relates to methods of treating PTEN-deficient metastatic hormone- sensitive prostate cancer (mHSPC) in patients in need thereof, the methods comprising administering to the patients a combination comprising capivasertib or a pharmaceutically acceptable salt thereof; and abiraterone or an ester prodrug thereof, or a pharmaceutically acceptable salt thereof.