73 results about "Patient population" patented technology

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of the free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of the free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

The invention discloses a method and a system for evaluating movement of two lower limbs of a stroke patient, and relates to the technical field of movement function evaluation. The invention discloses a stroke patient double-lower-limb movement evaluation system which comprises a task guide module, a movement acquisition module, a movement scoring module, an interference evaluation module, a trend analysis module, a combination analysis module and a result output module. According to the method, the scoring model based on crowd category feature matching is constructed, exercise ability scoring is performed by adopting a machine learning algorithm or a fixed weighted combination, personalized evaluation is realized according to the group features of different patients, and the generalization ability and prediction accuracy of the scoring model are improved; group modeling is carried out by introducing features such as patient age, gender and motion level, and a scoring strategy is automatically switched according to the number of samples, so that the scoring mechanism also has reliability and clinical consistency in a family scene with limited data volume.

Disclosed is method and system for identifying treatment subpopulations within a patient population of a clinical study, by applying a causal ensemble model configured to output an ensemble Conditional Average Treatment Effect (eCATE).

The invention belongs to the technical field of biological medicine, and particularly relates to application of actinomycin D in preparation of a ctDNA detection sensitizer. The invention provides a ctDNA release method based on actinomycin D as an enhancer, which can significantly improve the release level of ctDNA in early small-volume tumors and tiny residual lesions, thereby improving the sensitivity and positive detection rate of liquid biopsy in MRD detection. Under an extremely low dosage, the ActD can generally promote a plurality of human tumor cells to release ctDNA under the condition of not causing obvious cell death, and has good biological safety and adaptability. The method is especially suitable for tiny, biological inert or anatomical position hidden focuses which are difficult to identify by iconography, namely blind area patient groups in disease monitoring, and has a wide clinical application prospect.

The invention relates to the technical field of cardiovascular disease health risk assessment and management, and discloses a cardiovascular patient group health assessment system based on diet and individual characteristics, which comprises a data acquisition module, an analysis and prediction module and a health assessment module. According to the cardiovascular patient group health assessment system based on diet and individual characteristics, a data acquisition module acquires a user data set, a data preprocessing unit identifies the user data set, a risk proportion unit calculates a disease risk ratio Hbfx, and a variable prediction unit calculates a disease prediction data set Fbyc; the health assessment module calculates a comprehensive influence data set Zhyx and comprehensively assesses the influence of significant variables of a user on CVD risks, analysis and assessment are more comprehensive and highly targeted, the health assessment module compares a reference data set according to the comprehensive influence data set Zhyx and judges and assesses corresponding scores, and the effect of early intervention and treatment of cardiovascular diseases is better.

Systems and methods for ophthalmic disease detection and risk assessment that combines digitally-augmented examination instruments with neural networks trained to analyze examination imagery across diverse patient populations and variable image qualities, enabling automated analysis and classification of eye conditions during routine clinical examinations.

A cognitive assessment system includes a computing device, a server coupled to the computing device, and an eye tracking device coupled to the computing device. The computing device includes a cognitive assessment program configured to perform at least one cognitive assessment task to assess cognitive function of a user. The server includes a database. The database stores historical performance data of the user, a historical performance model of the user, task performance data of a healthy population, and task performance data of a patient population. The eye tracking device is configured to capture eye movement information of the user.

This application relates to, inter alia, anti-cMET antibody drug conjugates ("ADCs") having lower target-specific binding at low versus physiologic pH ("pH-ADCs"), pharmaceutical compositions including the pH-ADCs, methods of making the pH-ADCs, methods of selecting specific patient populations for cancer treatment with an anti-cMET pH-ADC, and methods of using the pH-ADCs to treat cancers, including cMET+ / cMET-expressing or cMET-overexpressing cancers, particularly including cMET+ / cMET-expressing or cMET-overexpressing non-small cell lung cancers ("NSCLCs").

The application belongs to the field of biological medicine, and particularly relates to a pharmaceutical composition for treating inflammatory bowel disease and application of the pharmaceutical composition. The disclosed pharmaceutical composition comprises anti-TNF monoclonal antibody, phosphoketolase-producing bacteria or a pharmaceutically acceptable viable bacterial preparation or extract thereof. The application comprehensively utilizes multi-dimensional data analysis and experimental verification of'microbe-immune', and finds that the phosphoketolase-producing bacteria can regulate host immune metabolic microenvironment, actively stabilize blood drug concentration of the anti-TNF monoclonal antibody and reduce immunogenicity, so as to achieve the purposes of stabilizing blood drug concentration of the anti-TNF monoclonal antibody and delaying clearance, significantly improve clinical response rate and remission rate, and is particularly suitable for a patient population with primary non-response or secondary non-response to the anti-TNF monoclonal antibody, thereby providing an effective solution for solving this clinical difficulty.

The invention discloses an application of a T cell subset in improving the treatment effect of AK112. The T cell subset is XCL1 + CCL3 + CD8 + T cells. The invention relates to an application of a specific T cell subset in improving the curative effect of AK112, a T cell agonist and AK112 are combined for use, and by synergistically activating the T cell function and reversing the immunosuppression microenvironment, the single drug resistance of AK112 is effectively overcome, the continuous remission time is remarkably prolonged, the treatment response is deepened, the applicable patient group is expanded, and meanwhile, the toxicity risk is reduced. The invention further develops a full-spectrum flow cytometry kit containing 13 antibodies, 13 key markers of the T cell subgroup can be simultaneously detected by one tube, patients sensitive to evoximab treatment (i.e., high expression of the subgroup) can be efficiently screened out, and a convenient tool is provided for accurate medication.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of a free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of a free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This invention discloses a rapid implantation system for a multi-module integrated surgical kit for daytime breast augmentation, belonging to the field of medical device control and computer-aided surgery technology. The system receives confirmation commands from a patient population identifier via a graphical user interface. Based on this identifier, it matches the corresponding target-specific surgical kit from a pre-set specific surgical kit library. Then, based on the target-specific surgical kit and the patient population identifier, it matches an adaptive implantation strategy from an implantation strategy library. The system further generates a module call sequence containing multiple sub-tasks based on the adaptive implantation strategy. Each sub-task is associated with a functional module identifier and execution parameters. Control commands are sent to the corresponding target device in sequence to drive the device to execute the sub-tasks. This invention achieves rapid and precise implantation control of a multi-module integrated surgical kit through population-characteristic-driven specific surgical kit selection and adaptive implantation strategy configuration, improving the standardization and operational efficiency of daytime breast augmentation surgery.

A method for automatically abstracting and selecting an optimal set of variance-related functions that are an indicator of an individual outcome in healthcare, wherein the method comprises: generating, by one or more processors, an abstracted set of variance-related candidate-patient functions, wherein the abstracted set of variance-related candidate-patient functions are temporally heteroscedastic functions; optimizing, by one or more processors, each patient function from the abstracted set of variance-related candidate-patient functions by identifying a time period in which the variances and heteroscedasticity of each patient function are maximized, wherein this optimization produces an optimal abstracted set of variance-related patient functions from the time period in which the variances and heteroscedasticity of each patient function are maximized;by one or more processors comparing the optimal abstracted set of variance-related patient functions with a historical dataset for a patient population to create a predictive set of variance-related patient functions, wherein the predictive set of variance-related patient functions predicts a health-related target outcome of the patient population; by one or more processors generating a current optimal patient set of variance-related patient functions for a current patient; by one or more processors comparing the optimal set of variance-related patient functions for the patient population with the current optimal patient set of variance-related patient functions for the current patient;In response to the optimal set of variance-related patient functions for the patient population, which matches the current optimal patient set of variance-related patient functions for the current patient within a predefined limit, one or more processors determine whether the health-related target outcome matches a predefined health-related target outcome for the current patient; and in response to the health-related target outcome matching the predefined health-related outcome for the current patient, one or more processors issue an alert regarding the predefined health-related outcome for the current patient.

Systems and methods for non-invasively measuring intracranial in accordance with embodiments of the invention are illustrated. One embodiment includes a method for estimating an intracranial pressure of a patient, including introducing a tracer into the blood of a patient, recording, using an imaging device, the traversal of the tracer through a brain of the patient as a kinetic curve, estimating a mean transit time (MTT) of the tracer through the brain of the patient based on the kinetic curve, measuring a mean arterial pressure (MAP) of the patient, calculating an intracranial pressure index as MAP−Ck / MTT, where C is a constant to scale proportional relationships based on healthy patient population characteristics, and k is a constant in pressure / time, and providing the intracranial pressure index. In various embodiments, regions of the brain unaffected by transient vascular abnormalities are identified and are used for data collection.

The present application relates to the field of diagnosis and treatment data processing, in particular to a kind of urology diagnosis and treatment data intelligent processing method and system.The method first obtains the diagnosis and treatment data of different dimensions of each patient in urology, obtains the important evaluation factor of each dimension of each patient according to the difference of the diagnosis and treatment data of the same dimension between each patient and other patients and the correlation of the diagnosis and treatment data of each dimension, obtains the similarity between patients based on the difference of the diagnosis and treatment data of the same dimension between patients and the difference of important evaluation factor, and carries out iterative clustering to each patient, obtains multiple clustering results, analyzes the clustering effect of each clustering result, and selects the best clustering result, and then stores the diagnosis and treatment data of each dimension of the patients in the same clustering cluster in the best clustering result to database.The present application can improve the personalized classification storage effect of the diagnosis and treatment data of patient population.

The application provides application of serum sIL-2R as an ADAM10 inhibitor companion diagnostic marker and a corresponding kit, including two core application scenarios: before treatment, screening of a patient population with high activity of an ADAM10-sIL-2R pathway and most likely to benefit from ADAM10 inhibitor treatment by detecting the serum sIL-2R level of the patient; during and after treatment, evaluating the inhibition effect of the ADAM10 inhibitor on the target pathway and the treatment efficacy by dynamically monitoring the change of the serum sIL-2R level; the liquid companion diagnostic strategy will provide an indispensable supporting tool for the precise application of the ADAM10 inhibitor in pancreatic cancer, and significantly improve the precision and effectiveness of clinical medication.

A method and apparatus for generating a customized cardiovascular catheter shape based on an individual patient anatomy is disclosed. As a result, the on-demand shaping of the custom catheter can be done in the context of a cardiovascular catheter procedure. This can also allow an existing catheter to be reshaped and used even for patient populations with unique or extreme anatomy or with anatomy that is not common for the type of surgery being performed.

An intelligent dosing platform incorporating comprehensive predictive analytics and forecasting capabilities for injectable medication administration. The platform includes predictive analytics modules configured to generate forecasts for financial costs, medication demand patterns, and adverse event probabilities using machine learning algorithms and statistical modeling techniques. The system features financial modeling modules that analyze cost patterns associated with medication procurement, administration infrastructure, and insurance reimbursements, while demand forecasting modules predict medication needs based on seasonal trends, patient population changes, and treatment protocol modifications. Adverse event prediction modules identify risk factors using patient risk profiles and clinical outcome histories. The platform incorporates machine learning capabilities that continuously improve prediction accuracy using patient outcome data and administration patterns. Comprehensive reporting modules generate customized predictive reports for healthcare administrators, pharmaceutical suppliers, and insurance providers, while optimization modules recommend actions for cost reduction and patient safety improvement based on predictive insights.

The utility model relates to the technical field of leg supporting, in particular to a leg supporting device for bone surgery, which comprises a hollow column, fixing and clamping mechanisms are movably arranged on two sides of the hollow column at the bottom end, and each fixing and clamping mechanism comprises a lifting rod, a wedging groove, a fixing screw, a threaded rod and a rotating handle. The lifting rods are pulled out of the hollow columns up and down, then the rotating handles on the two sides of the bottom ends of the two hollow columns are rotated, the threaded rods move towards the interiors of the hollow columns to be fixed, the corresponding height is determined, and one ends of the threaded rods are clamped into the wedging grooves in the two sides of the lifting rods to be fixed. At the moment, the patient can place the whole leg on the U-shaped bearing block, the gravity of the leg downwards extrudes a buffer spring to enable the buffer spring to generate elastic deformation, supporting is conducted through the brought reverse acting force, the device is provided with a supporting mechanism capable of being adjusted in a lifting mode, the device can be suitable for being used by patients of different age groups, and the optimal supporting and fixing point can be found conveniently.

The invention relates to a method for realizing AFD phase inversion risk prediction based on function connection and a graph neural network, and the method comprises the following steps: preprocessing an image, extracting a time sequence of each brain region based on a predefined brain map, and calculating a whole brain FC matrix; constructing the FC matrix into graph structure data, and inputting the graph structure data into a graph neural network; the Euclidean distance between the feature representation of the AFD patient to be evaluated and the average feature representation of the BD patient population is calculated. The method, the system, the device, the processor and the medium for realizing AFD phase inversion risk prediction based on the function connection and the graph neural network are high in prediction precision, combine GNN with an edge weight attention mechanism, can capture high-order topological characteristics of a brain network, have early warning capability, and can predict and output FC and brain regions which are most critical to decision. The technology fusion innovativeness is high, the brain connection omics, the graph neural network and representation learning are seamlessly fused, and the clinical transformation potential is large.

The invention discloses a pharmaceutical composition for treating KRAS G12D mutation cancer and application of the pharmaceutical composition, and belongs to the technical field of biological medicine. The pharmaceutical composition comprises a KRAS G12D inhibitor and a PARP (poly-ADP-ribose polymerase) inhibitor. The core of the invention lies in that a KRAS G12D inhibitor (such as MRTX1133) can down-regulate expression of homologous recombination (HR) repair related proteins (such as BRCA1, RAD51 and RPA32) and induce HR defects, so that tumor cells carrying KRAS G12D mutation have high sensitivity to a PARP inhibitor (such as Olaparib), and a synthetic lethal effect is formed. The combined strategy not only shows synergistic anti-tumor activity in a model sensitive to the KRAS G12D inhibitor, but also is effective in a model resistant to the KRAS G12D inhibitor. The invention provides a new effective scheme for treating cancers such as pancreatic ductal adenocarcinoma with KRAS G12D mutation, and is expected to expand the applicable patient population of the PARP inhibitor.

A method is provided for predicting a disease outcome using a machine learning model that generates training data for training the machine learning model useful for implementing a cellular disease model.SOLUTION: A method for predicting a disease outcome using a machine learning model includes implementing an ML-enabled cellular disease model to validate an intervention, identifying a patient population likely to be a responder to the intervention, and developing a therapeutic structure activity relationship screen. To generate a cellular disease model, data from human genetic cohorts, the literature, and generic cellular or tissue-level genomic data are combined to elucidate a set of factors (e.g., genetic, environmental, cellular factors) that cause a particular disease. A series of factors are used to manipulate invitro cells to generate training data for training a machine learning model useful for implementing a cellular disease model.SELECTED DRAWING: FIG. 1B

An automated system that selects an optimal combination of risk models for a target patient population. The selected combination may be monitored by clinicians to determine which patients are at greatest risk for adverse events or clinical deterioration. The system may compare risk model data for hundreds or thousands of models to data collected on a target patient population to determine which combination of models is the best fit for this target group. An illustrative selection method may minimize a cost function that measures the deviation between a model combination and desired features for an optimal combination. Illustrative factors in the cost function may include differences between the predicted risk distributions for the target group, using the model risk function, and the risk distributions for the dataset used to train the model, and correlation among risks predicted by the models in the combination.

The application discloses a kind of cerebral apoplexy patient double lower limbs movement evaluation method and system, it is related to motor function evaluation technical field.A kind of cerebral apoplexy patient double lower limbs movement evaluation system, including have: task guide module, motion acquisition module, motion scoring module, interference evaluation module, trend analysis module, combination analysis module and result output module.The application is matched by constructing the scoring model based on population category characteristics, and motion ability score is carried out using machine learning algorithm or fixed weighting combination, and personalized evaluation is realized according to different patient population characteristics, to improve the generalization ability and prediction accuracy of scoring model;By introducing patient age, gender, movement grade and other characteristics for grouping modeling, and automatically switching scoring strategy according to sample quantity, the scoring mechanism also has reliability and clinical consistency in the limited data amount of home scene.

Systems and methods are providing for detecting and monitoring thoracic fluid, air-trapping and ventilation assessment in real time, wherein data obtained from a non-invasive electrode patch is analyzed using analysis algorithms for an electrical equivalent model that have been personalized for a patient's physiologic characteristics, medical condition and / or historical medical information using machine learning trained on a dataset representative of a large and diverse patient population. The systems and methods provide a simple, real-time, highly sensitive and specific, non-invasive, bedside solution for fluid level assessment, checking for increased air trapping, and ventilation assessment. The described methods include a variety of use cases for the inventive system and methods.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of a free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of a free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

An example system includes a memory configured to store cardiac data associated with a patient population within a Bayesian network structure describing cardiac health events for the patient population. The system may apply new patient data to an Artificial Intelligence model (AI model) trained using the Bayesian network structure to output a risk-benefit assessment specific to a particular patient implantable medical device (IMD). The output may include selectable configuration recommendations and a corresponding risk probability for each configuration. Subsequent to the output, the system may receive clinician input selecting one of the configuration recommendations for the patient IMD. Responsive to receipt of the clinician input, the system may configure delivery of a therapy via the patient IMD using the one of the multiple configuration recommendations selected according to the clinician input.

The current disclosure provides for novel therapeutic methods by identifying patient populations that may be treated effectively by immunotherapies. It was found that responders of immunotherapeutic treatments have a higher amount of B-cell exosomes in their blood than non-responders. Aspects of the disclosure relate to a method of treating cancer in a subject comprising administering to the subject immune checkpoint blockade (ICB) therapy after B-cell exosomes have been detected in a biological sample from the subject.

The application discloses a kind of cross-modal chronic disease prediction methods based on artificial intelligence, belong to medical artificial intelligence field.The method first acquires four kinds of multi-source heterogeneous information of medical image, biochemical index, behavior data and text data;Second, construct the multi-modal encoder consisting of 3D-CNN, LSTM and BERT, respectively extract spatial, time sequence and semantic features;Subsequently, the cross-modal attention mechanism is used to fuse each modal feature, and the nonlinear law of chronic disease progression is captured by LSTM+attention network;Further introduce causal inference module, distinguish the causal effect and correlation of risk factors;Finally, according to the characteristics of patient population dynamically adjust model weight, realize the personalized risk prediction and progress curve estimation of chronic disease.The application solves the problem of ignoring data multi-modal, time sequence and causal confusion in the prior art, significantly improves the accuracy, explainability and model generalization ability of chronic disease prediction.

The invention provides a fibromyalgia syndrome intelligent diagnosis system and device driven by artificial intelligence, and relates to the technical field of fibromyalgia syndrome intelligent diagnosis, the system comprises a data receiving module, a data processing module and a result generation module; the data receiving module is used for receiving historical medical record data and medical record data of a tested patient; the data processing module comprises a patient population division and feature extraction sub-module and a disease and syndrome type prediction sub-module; the patient crowd division and feature extraction sub-module is used for dividing tested patients into subtype groups with clinical significance and extracting a core symptom set of each patient crowd; and the disease and syndrome type prediction sub-module is used for performing FMS diagnosis and traditional Chinese medicine syndrome type classification through a disease diagnosis and traditional Chinese medicine syndrome type classification model based on the core symptom set to obtain a prediction result. According to the scheme, the diagnosis system is constructed through a machine learning technology, so that the objectivity and accuracy of FMS diagnosis are remarkably improved.