441results about "Electronic clinical trials" patented technology

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

This application describes, amongst other things, an example method for responding to user queries. The method includes receiving, via a user interface, a user query from a user and categorizing the user query using a first task-specific orchestration. The method also includes providing information about the user query to a destination task-specific orchestration, including, in accordance with categorizing the user query in a first category, assigning a second task-specific orchestration as the destination task-specific orchestration; and, in accordance with categorizing the user query in a second category, assigning a third task-specific orchestration as the destination task-specific orchestration. The method further includes, in response to providing the information about the user query, receiving an output from the destination task-specific orchestration; and providing a response to the user query via the user interface based on the output from the destination task-specific orchestration.

Provided is a method including obtaining a prompt, determining a prompt embedding vector representing the prompt in an embedding space, modifying the prompt embedding vector using a trained model configured to adjust prompt embedding vectors to decrease proximity to vectors of blocks in a data set from which data is retrieved to augment generation by the generative AI model, determining that the modified prompt embedding vector is within a threshold distance to vectors in the embedding space corresponding to one or more blocks in the data set, selecting the one or more blocks in the data set, generating a response using the generative AI model based on the selected one or more blocks in the data set, quantifying an amount of influence of the respective block on corresponding text in the generated response, and providing the response and a representation of the quantified amount of influence as an output.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing and adapting monitoring programs. In some implementations, a system communicates with a set of remote devices involved in a monitoring program that involves collection of data from the remote devices over a communication network. The system determines a set of attributes associated with an outcome or condition that has occurred for multiple of the remote devices. The system generates parameters to adapt the monitoring program, and the system selects a second group of remote devices to involve in the adapted monitoring program based on profiles or sets of attributes of users associated with the remote devices. The system configures the remote devices in the selected second group to perform monitoring for the adapted monitoring program, including acquiring data for the adapted monitoring program and providing the acquired data to a server over the communication network.

A computer-implemented method of predicting, simulating, or forecasting values of one or more specified subject-related attributes during a clinical trial comprises: receiving input data comprising: a medical history of a subject, the medical history comprising values of a plurality of subject-related attributes of a subject; and data specifying a requested output, the data comprising: the one or more specified subject-related attributes of the subject and a time frame; and applying a trained generative machine-learning model to the received input data, the trained generative machine-learning model configured to generate output data based on the input data, the output data comprising: respective values of the one or more specified subject-related attributes of the subject in the specified time frame.

The application provides an intelligent follow-up management system and method for clinical trials based on artificial intelligence and big data, and relates to the technical field of data analysis and processing. The system is composed of three parts: causal federal knowledge graph, risk perception follow-up scheduling and privacy compliance audit. Each center generates a causal summary without sharing original data and obtains a global graph through secure aggregation. A patient-level causal simulator is issued to generate individualized follow-up actions and causal evidence chains in combination with a constrained conservative strategy learning. Key events in the whole process are written into a permission chain in the form of hash and are governed by a smart contract for informed consent and withdrawal. This scheme improves the follow-up completion rate and early detection ability of adverse events and strengthens compliance auditability.

This invention discloses an AR-assisted puncture navigation spatial registration method and system based on environmental reference features, relating to the field of medical image navigation. The system includes an augmented reality navigation device, a permanent ceiling marker, and a temporary marker board. The augmented reality navigation device includes a binocular augmented reality camera group, a global tracking camera, a global spatial registration module, and a display module. The binocular augmented reality camera group constructs a binocular overlapping field of view, acquiring the pose information of the binocular augmented reality camera group relative to the temporary marker board during the calibration phase, and acquiring near-field images of the surgical area in real time during the surgical phase and displaying them on the display module. The global tracking camera acquires the pose information of the global tracking camera relative to the global coordinate system of the ceiling. The global spatial registration module solves the extrinsic parameter matrix between cameras and the environmental relationship matrix based on the pose information. During the surgical phase, the path planned by the imaging equipment is converted into a real-time navigation path and superimposed on the display module. This solves the complex calibration problem under non-overlapping fields of view.

Optimized bio-synchronous drug delivery begins with establishing a bio-synchronous treatment protocol that incorporates individual temporal and innate biological characteristics into a pharmacological treatment plan. The bio-synchronous treatment protocol is thereafter initiated using bioactive agent delivery device. Bio-synchronous drug delivery includes continual collection of patient data such as physical, psychological, temporal and environmental characteristics. This data is analyzed so to not only determine an initial treatment protocol but to also determining whether modification to the ongoing bio-synchronous treatment protocol is required. And, responsive to determining a modification is required the system modifies the bio-synchronous treatment protocol and use of delivery device. These modifications and treatment protocols can include reactive and proactive psychological support supplied to the patient in a variety of formats.

Containers for facilitating adherence to dietary plans are provided, along with related systems and methods. A locking mechanism secures an access panel moveable between an opened position and a closed position in the closed position. A controller receives user input regarding desired caloric intake amount for a time period and regarding a type or types of foodstuff stored at the container, and weight readings from at least one weight sensor. The controller determines a caloric value of removed foodstuffs from the containers from the weight readings and the type or types of foodstuff indicated in the user input, and operates the locking mechanism to secure the access panel in the closed position where the caloric value of the removed foodstuffs for the time period exceeds the desired caloric intake amount for the time period.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is carried out diabetic eye disease diagnosis reasoning, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

A user healthcare method and system according to the present invention may comprise the steps of: receiving, from a user terminal logged in with a user account, a selection of at least one musculoskeletal part, for which exercise is to be performed by the user, from among a plurality of musculoskeletal parts; in response to the selection of the musculoskeletal part, providing, to the user terminal, a plurality of pieces of survey data for diagnosing the health condition of the user; receiving, through the user terminal, response data for the plurality of pieces of survey data; generating an exercise program for the selected musculoskeletal healthcare on the basis of the response data; and allocating the exercise program to the user account.

The present application belongs to the technical field of clinical big data processing, and particularly relates to a double J tube film bacteria infection risk prediction method and system based on microbiological examination. Through collecting patient basic data, clinical parameters and physiological indexes, the inhibitory efficiency of the drug on the film bacteria is evaluated through fosfomycin trometamol intervention experiment; microbiological examination and film bacteria quantitative analysis are performed on urine and tube samples; independent risk factors of infection are identified by applying logistic regression analysis; finally, a weighted risk prediction model is constructed, the probability score is calculated, the risk level is divided, and a preventive intervention suggestion report is generated. The present application realizes early identification and quantitative evaluation of infection risk by deeply mining the correlation between microbiological examination data and clinical multi-dimensional indexes, provides evidence-based medical support for preventing urinary tract infection, significantly improves the risk warning accuracy and optimizes the perioperative infection prevention and control system, and has important significance for improving the prognosis quality of urology.

We provide the system. [Solution] Information processing means that collects preference information and health information and generates personalized food menus based on this information, Information processing means that collects information on preferences and health, and generates an individualized meal plan based on this information, An interactive interface means that engages with the user in a conversational format and supports their choices using natural language, Route optimization methods to optimize paths and reduce travel time by means of transportation, A means of linking with smart devices to understand the user's exercise and nutritional intake and manage health information, A means of simplifying operations that allows food selection to be repeated with a single instruction, A system that includes this.

Disclosed are a system for providing psychological counseling and a method therefor. According to the present invention, when a preset extended reality (XR) room-creation menu is selected from a website screen, an expert of an expert terminal performs a psychological counseling function in a metaverse chat room with a specific user under management, and a user of a terminal and the expert of the expert terminal may participate in another metaverse chat room together while performing a counseling function to perform a chat function, so that the expert may perform the psychological counseling function with the user in real-time and accordingly identify the user's psychological state and provide appropriate coping measures, and may accurately identify the user's state as the user performs the chat function with another user in a metaverse chat room.

The present invention relates to a system and a method for monitoring mental health and generating personalized reports. According to one embodiment of the present invention, the method for monitoring mental health and generating personalized reports comprises: receiving a user's response data to a self-report questionnaire, wherein the self-report questionnaire comprises questionnaires corresponding to the respective mental health scales; and generating a report on the user's mental state based on the response data to the self-report questionnaire. The mental state report includes a personalized mental health graph that visualizes daily scores for each mental health scale in the form of a bar chart.The bar charts included in the personalized mental health graphic can be displayed in colors that are predetermined according to criteria for the respective mental health scale. According to the present invention, it is possible to capture psychological, biological, and social digital phenotypes of groups with non-suicidal self-harm via the self-report questionnaire and thereby investigate predictive factors related to these phenotypes.

This invention discloses an early screening system and method for hyperuricemia based on fingertip blood uric acid detection. The system includes a fingertip blood collection module, a detection module, a data processing module, a storage module, an analysis module, an early warning module, a display module, a communication module, a calibration module, and a power supply module. It accurately collects fingertip blood samples from subjects, obtains blood uric acid concentration data using specific detection equipment, constructs a multi-dimensional analysis model based on clinical characteristic parameters, and determines the optimal diagnostic cutoff value based on ROC curves, achieving rapid and accurate screening for hyperuricemia. The method includes steps such as sample collection, detection, data processing, model analysis, result determination, and early warning. It is simple to operate, fast, and highly accurate, solving the problems of poor compliance and difficulty in grassroots implementation associated with traditional venous blood testing. It is suitable for large-scale population screening and early diagnosis of hyperuricemia in areas with scarce medical resources, and has significant clinical application value and social significance.

The application relates to the technical field of medical time series data disease typing, and discloses a diabetes typing and diagnosing system. The system comprises the following steps: generating patient time series data through data preprocessing, and cutting through a multi-scale sliding window, synchronously calculating statistical features and trend slopes of physiological indexes in each window, and constructing a mode trajectory of individual health evolution. The trajectory is mapped to a high-dimensional space to divide diabetes subtypes. For a new data point, multi-dimensional distances and migration probabilities to each prototype are calculated to generate a preliminary subtype membership vector. The vector is fed back to the trajectory construction process to dynamically adjust window parameters and calculation weights in an iterative manner, and after optimization, a stable personal health trajectory and an updated membership are output. Finally, a structured subtype diagnosis with a confidence level is generated. The system realizes dynamic, accurate and individualized typing of diabetes subtypes.

The present application relates to a method and system for evaluating drug effectiveness in the presence of a partially cured population, the method comprising the steps of: obtaining observational study sample data, determining proxy variables and covariates, and dividing the sample into a treatment group and a control group according to a treatment protocol; performing parameter estimation on the treatment group and the control group using a mixed cure model respectively to obtain a latent incurable rate and a survival function of non-cured persons for the treatment group and the control group; calculating a drug cure rate causal effect parameter based on the latent incurable rate of the treatment group and the control group; calculating a survival function of an incurable main layer based on the latent incurable rate of the treatment group and the control group and the survival function of non-cured persons; and evaluating the effectiveness of the drug based on the drug cure rate causal effect parameter and the survival function of the incurable main layer.

A system for predicting clinical trial participation may comprise a participation probability (PP) server connected to a machine learning (ML) module. The PP server may receive clinical trial (CT) parameters from a user device. The PP server may query a patient database using the CT parameters to identify potential participants. The PP server may collect social media data for the potential CT participants. The PP server may generate feature vectors based on the social media data. The PP server may provide the feature vectors to the ML module and receive participation probability index (PPI) scores for the potential CT participants from the ML module. The PP server may generate a ranked list of likely CT participants based on the PPI scores. The PP server may provide targeted content to potential participants with low PPI scores. The PP server may update the PPI scores based on engagement with the targeted content.

Disclosed are a clinical research data analysis method capable of real-time analysis and a device therefor. The clinical research data analysis method according to an embodiment of the present invention may comprise: a data collection step for collecting clinical research data input to an electronic data collection system; a data analysis processing step for generating a data analysis result by performing real-time analysis or statistical analysis by using the collected clinical research data as an input; and a service processing step for converting the data analysis result into a preset report format and providing same to at least one clinical research-related institution.

The following relates generally to improved clinical symptom analysis. In some embodiments, one or more processors: (1) receive patient input data; (2) obtain patient survey data representing the patient's condition; (3) determine a trend in the patient survey data using a determination algorithm; (4) apply the generative AI algorithm to the patient input data, the patient survey data, and the trend in the patient survey data to produce a report of the trend of the patient's condition; and / or (5) display the report on a display device.

We provide the system. [Solution] A means of receiving and storing individual medical history information, A means for generating medical proposals using generative artificial intelligence based on the received information, A means for providing additional information regarding the generated medical proposal and for generating a response to an inquiry, A means of managing citizens' health information and providing optimal treatment plans through integration with mobile devices, A means of providing the latest medical and facility information in smart cities, A system that includes this.

Systems and methods for machine learning of medical records are provided. The system can execute multiple machine learning models on the medical records in parallel using multi-threaded approach wherein each machine learning model executes using its own, dedicated computational thread in order to significantly speed up the time with which relevant information can be identified from documents by the system. The multi-threaded machine learning models can include, but are not limited to, sentence classification models, comorbidity models, ICD models, body parts models, prescription models, and provider name models. The system can also utilize combined convolutional neural networks and long short-term models (CNN+LSTMs) as well as ensemble machine learning models to categorize sentences in medical records. The system can also extract service provider, medical specializations, and dates of service information from medical records.

This invention relates to the field of rehabilitation follow-up management technology, and discloses a method and system for dynamically adjusting follow-up plans based on multidimensional data. The method includes acquiring historical rehabilitation indicator data and historical follow-up frequency records of subjects and performing time-series dynamic analysis to obtain indicator fluctuation patterns and frequency cumulative effect values; performing causal inference processing based on these to obtain causal association paths and weights; extracting real-time rehabilitation indicator status based on the paths and performing risk prediction to obtain assessment results when fluctuations exceed limits; combining the above characteristics to perform frequency arrangement sequence causal simulation to determine target follow-up sequences with target deviations less than preset thresholds; matching historical trajectories and performing node analysis and timing miss risk assessment to obtain immediate adjustment instructions; finally, retrieving resource records and performing continuity verification to output personalized follow-up plans. This method can achieve precise allocation of follow-up resources and closed-loop adaptive optimization of individual rehabilitation trajectories.

The application relates to the field of biological medicine, and discloses a cardiovascular disease risk cycle evaluation method for a patient, which comprises the following steps: acquiring a multi-source longitudinal health data sequence of the patient; performing standardization and missing value processing to generate a structured feature matrix with unified time granularity; inputting the structured feature matrix into a pre-trained time-aware deep state space model to output a state trajectory vector sequence representing the dynamic characteristics of the cardiovascular system; calculating a risk evolution curvature index, combining with a turning point threshold to identify a risk acceleration period, a platform period or a reversal period; and generating a risk grade label, an intervention suggestion and a visual atlas according to the above. The system comprises six units of data acquisition, preprocessing, modeling, curvature calculation, cycle identification and evaluation output. The application realizes high-sensitivity identification of a key turning point by continuously tracking the risk dynamics of an individual, and improves the accuracy and timeliness of cardiovascular disease prevention.

The present application relates to the technical field of respirator control, in particular to invasive-noninvasive ventilation switching system and method based on modified GCS score. The method uses expiratory decay characteristics to construct passive reference flow rate conditions, decouples the real myogenic flow rate component from the mixed waveform; by comparing the characteristics of high and low load two groups of breathing cycles, the specific performance decay characteristics and response delay characteristics are extracted, and the mechanical saturation state when the ventilation benefit decreases while increasing the inspiratory effort is captured; based on the mechanical load saturation index and the modified GCS score, the interlocking decision mechanism is realized. The present application extracts the real breathing characteristics of the patient, quantifies the nonlinear saturation degree of the respiratory system under high load, increases the safety gate of the respiratory mechanics dimension on the basis of the consciousness evaluation, forms the double-dimensional safety interlocking decision, improves the high-risk switching operation of the central wake but mechanical decompensation, and improves the safety of ventilation switching.