63 results about "Therapy intervention" patented technology

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The present invention provides a sensitive, accurate, bioactive skin barrier / wafer usable in ostomy systems and wound drainage collectors for identification of pathogens at critical thresholds of presence on peristomal skin at stoma sites and exuding wounds before clinical signs and symptoms of infection present. A method of fabricating a bioactive skin barrier / wafer intended for identification of pathogen presence in ostomy systems is provided, that delivers a rapid response observable alert. The present invention utilizes biomarkers to detect pathogens in real time, where biomarkers are characteristics objectively measured as indicators of health, disease, or a response to an exposure or intervention, including therapeutic interventions.

The invention discloses an AI-based atrial fibrillation patient health risk prediction method, and belongs to the technical field of medical information, and the method specifically comprises the steps: receiving a physiological parameter record and an intervention treatment record of an atrial fibrillation patient, carrying out the time sequence alignment operation, and forming structured patient data; extracting risk-related features from the structured patient data, wherein the risk-related features are divided into basic risk features and intervention response features; inputting the basic risk features into a basic risk prediction network, and outputting a basic risk score; inputting the intervention response characteristics and the basic risk score into an intervention effect separation network together to generate a post-intervention risk score; calculating a net effect value of treatment intervention according to the difference between the basic risk score and the post-intervention risk score; combining the basic risk score, the post-intervention risk score and the net effect value to generate a patient individualized long-term risk prediction trajectory; and accurate prediction of the long-term health risk of the atrial fibrillation patient is realized.

The disclosed method and system pertain to stratifying a patient population for precision medicine in Inflammatory Bowel Disease (IBD) treatment. The method involves accessing a multi-omic dataset comprising genomic, transcriptomic, and proteomic profiles of patient data. Machine learning algorithms are employed to analyze the dataset and identify biomarkers associated with a response to a drug for treating IBD. The patient population is stratified into phenotypic groups based on the identified biomarkers using unsupervised clustering. A patient population predicted to respond to the drug is defined based on the stratification and further analysis of patient metadata. The system includes a data storage unit and a processor configured to perform the method. The method and system can be used to optimize therapeutic interventions in IBD management.

A prediction system includes: a terminal; and a processor, communicatively coupled to the processor. The processor is configured to: acquire medical treatment information including images captured at at least one of before, during, immediately after, and after a predetermined period from treatment of a patient with a symptom of a vascular disease; classify a condition of the patient based on a classification image; learn the medical treatment information in association with the condition of the patient; and predict at least one of a treatment effect and a prognosis when treatment intervention is performed for the patient based on a learning result.

The invention provides application of dynamic cortex inhibition imbalance in clinical progress prediction of early Parkinson's disease. The key function of bilateral cortex inhibition balance in adjusting the motion compensation ability of the Parkinson's disease in the early stage is clarified. By analyzing the change of the two inhibition pathways, the change of delta SICI ' / LICI' is found to be a sensitive index capable of predicting the disease progress of the patient with the early Parkinson's disease. ROC results show that the delta SICI ' / LICI' has very high discrimination capability in the aspect of predicting disease progression, the area under curve (AUC) is 0.841, the sensitivity is 87.5%, the specificity is 72.73%, and the Eyden index is 0.602. The research of the invention makes deeper understanding of the pathophysiological mechanism of Parkinson's disease, and highlights the importance of cortex inhibition balance in disease evolution. These observations also support the development of more targeted therapeutic interventions to alleviate symptom progression.

Provided is a method of digital therapeutics for modulating one or more Parkinson's disease-related markers, symptoms, or disease progression in an individual, on a standalone basis or by combining them with medicinal agents, particularly dopaminergic agents. Also provided are corresponding systems for implementing, delivering, and adapting such therapeutic interventions.

An electronic system for multimodal diagnostic measurements and therapeutic interventions includes a plurality of element network layers vertically stacked one with another on a flexible substrate, each element network layer being bi-axially stretchable and comprising a plurality of elements configured in an addressable, interconnected array formed in a multilayered structure and operably performing a distinct function. The electronic system is a multimodal, multiplexed soft electronic system in a multilayered configuration that supports capabilities ranging from high-density spatiotemporal mapping of temperature, pressure and electrophysiological parameters, to options in programmable high-density actuation of thermal inputs and / or electrical stimulation, drug elution, radio frequency ablation, and / or irreversible electroporation ablation.

Some embodiments provide for a method for identifying an intervention for a subject in furtherance of preventing development of cardiovascular disease in the subject, the method comprising: obtaining cardiometabolic health data for the subject; determining, using at least some of the cardiometabolic health data, a first trained machine learning (ML) model and for each of multiple time intervals, one or more measures of risk that the subject develops cardiovascular disease to obtain multiple measures of risk; determining, using the multiple measures of risk that the subject develops cardiovascular disease and at least one second trained ML model, benefit of administering to the subject one or more therapeutic interventions designed to reduce risk of cardiovascular disease by targering one or more modifiable causes of the cardiovascular disease; and identifying, using the determined benefit of administering the therapeutic intervention(s), at least one therapeutic intervention to recommend being administered to the subject.

Certain disclosed embodiments relate to a physiological parameter monitoring system. The system may include a sensor and sensor electronics connected to the sensor. The system may also include a transmitter operably connected to the sensor electronics. The transmitter is positioned or configured to be positioned at a first location, at least a portion of which is adjacent and / or in contact with an external surface of the host's body during a sensor session, and is further configured to wirelessly transmit sensor information using intrabody communication. The system may also include a first display device including a display and a receiver. The receiver is positioned or configured to be positioned at a second location, at least a portion of which is adjacent and / or in contact with an external surface of the body during a sensor session, and is further configured to receive sensor information from the transmitter using intrabody communication.

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Provided is a method of digital therapeutics for modulating one or more Parkinson's disease-related markers, symptoms, or disease progression in an individual, on a standalone basis or by combining them with medicinal agents, particularly dopaminergic agents. Also provided are corresponding systems for implementing, delivering, and adapting such therapeutic interventions.

The present invention provides a pneumoperitoneum maintenance device that facilitates the retention of air-filled material within a body cavity. [Solution] According to various embodiments, a probe can be inserted into a body cavity to perform a diagnostic intervention, a therapeutic intervention, or both. The probe can be inserted through a naturally occurring or artificial, intentional, or accidental body opening. The body opening can form a seal surrounding the probe, thus effectively holding the insufflation device within the body cavity and allowing the operator to perform the intervention. However, there may be leakage of the insufflated material. The insufflation holding device is configured to contact and form an effective seal adjacent to the body opening, providing a passage for introducing the probe into the body cavity, thus enabling a diagnostic or therapeutic intervention, or both.

There is provided herein a method of treatment comprising: (i) a first treatment cycle comprising administration of a therapeutically effective amount of a therapeutic agent, or a pharmaceutically acceptable salt thereof, to a subject in need thereof, wherein the therapeutic agent directly or indirectly induces muscle wasting; and (ii) a second treatment cycle following the discontinuation of the first treatment cycle, wherein the second treatment cycle comprises administration of a therapeutically effective amount of a β2-adrenergic receptor agonist, or a pharmaceutically acceptable salt thereof, to the subject, wherein the first and second treatment cycles are within the same therapeutic intervention.

The application relates to the technical field of medical devices and particularly discloses a pain dynamic evaluation and treatment linkage system based on three-dimensional somatosensory interaction, which comprises the following modules: a human body modeling module for constructing a three-dimensional human body dissection model; a somatosensory interaction module for receiving pain area marking instructions and pain intensity scores; an infrared sensing module for monitoring human body infrared thermal imaging data of a patient in real time; a physiological monitoring module for monitoring heart rate variability indexes and skin electric reaction indexes of the patient based on wearable devices; a region marking module for obtaining a calibrated pain area of the patient based on the pain area marking instructions and abnormal temperature zones in the human body infrared thermal imaging data; a pain prediction module for predicting a pain intensity prediction curve in a future period based on the pain intensity scores, the heart rate variability indexes, the skin electric reaction indexes and the calibrated pain area of the patient; and a treatment linkage module for generating treatment parameter control instructions based on the pain trend prediction curve and a multi-dimensional pain characteristic vector, so that precise and dynamic treatment intervention is realized.

The present invention executes a process based on cognitive behavior therapy for chronic diseases by means of a treatment assistance application installed on a patient terminal 1 for use by a patient. As a functional configuration for executing the process, a treatment assistance device comprises: a provocation content presentation unit 111 for presenting, to the patient and on the patient terminal 1, a content that provokes a symptom or a negative feeling; a reaction information acquisition unit 112 for acquiring information indicating a reaction from the patient during or after the presentation of the provocation content, through an input operation by the patient to the patient terminal 1 or a detection process by the treatment assistance application; and an intervention process determination unit 113 for determining, on the basis of the acquired reaction information, a treatment intervention process to be executed. As a result, the need for using a VR / AR projection device as a means for presenting the provocation content is eliminated, and the need for using an electroencephalograph or a specialized medial apparatus as a means for acquiring the reaction information from the patient with respect to the presentation of the provocation content is also eliminated.

A method for developing novel drug therapies utilizing properties of existing drug therapies, the method comprising the steps of: developing a novel therapeutic mathematical model of a target biological network and synthesizing a drug therapy based on the novel therapeutic mathematical model. The novel therapeutic mathematical model is capable of generating novel therapeutic time-course progressions, which include time-course progression features found in existing therapeutic time-course progressions of existing therapeutic mathematical models of the target biological network. The time-course progression features can be correlated with the outcomes of the target biological network. Each new therapeutic intervention constant can be used to synthesize a drug regimen, and these drug regimens collectively constitute the novel drug therapy.

A bioresorbable acoustic microrobot (BAMs) designed for precision medicine applications. Fabricated through two-photon polymerization, BAMs incorporate 7-diethylamino-3-thenolycoumarin, poly(ethylene glycol) diacrylate, pentaerythritol tetraacrylate, and Fe3O4 nanoparticles for magnetic steering, alongside encapsulated therapeutic agents. These microrobots feature an asymmetric dual-opening bubble-trapping cavity, enhancing acoustic propulsion and ultrasound imaging capabilities. The unique dual-surface layer chemistry ensures efficient operation in biological fluids, while hydrolysis-driven biodegradability minimizes residual harm. BAMs demonstrate sustained drug release, precise navigation, and effective tumor targeting, offering significant advancements over conventional microrobots in therapeutic interventions.

The present disclosure provides methods for determining a probability that after any of a number of therapeutic interventions, an initial state of a subject, such as somatic cell mutational status of a subject with cancer, will develop a subsequent state. Such probabilities can be used to inform a health care provider as to particular courses of treatment to maximize probability of a desired outcome for the subject.

A system for assessing pressure injuries in a healthcare facility. The system creates patient metadata for a patient and associates the patient metadata with medical equipment including a patient support apparatus having a controller that performs pressure injury prevention functions and a surface positioned on a frame of the patient support apparatus. The system records usage data from the medical equipment via a network interface. The usage data including duration and frequency measurements of pressure injury prevention functions. The system receives sensor data from sensors positioned on the patient support apparatus. The sensor data includes weight distribution and patient movement measurements. The system pairs the usage data and the sensor data to the patient metadata to create survey data that correlates patient risk factors with therapeutic interventions performed by the medical equipment and stores the survey data in a database accessible for automated completion of a survey.

A self-contained extended reality (XR) device is disclosed for real-time detection and mitigation of substance use disorder symptoms. The headset includes integrated biometric sensors for capturing physiological signals such as pupil dilation, heart rate, and head movement. A locally stored machine-learned model analyzes these signals to classify cognitive or affective states indicative of withdrawal, craving, or relapse risk. Upon detecting a threshold state, the headset automatically renders an immersive therapeutic environment selected to mitigate the user's condition. Interventions may include spatialized audio, visual modulation, and avatar-guided interactions. The device further supports adaptive personalization, secure local data storage, and optional transmission of session summaries to authorized care providers. The system operates without external computing devices and is optimized for privacy-preserving, user-specific behavioral health support.

This information processing device comprises a model acquisition unit, a data of interest acquisition unit, and a determination execution unit. The model acquisition unit acquires a trained model in which medical record data before or during intervention of a predetermined treatment for patients with a specific disease is set as an explanatory variable and the necessity of palliative care for the patients is set as an objective variable. The data of interest acquisition unit acquires medical record data for a patient of interest. The determination execution unit: determines the necessity of palliative care for the patient of interest by inputting the medical record data for the patient of interest to the trained model; and causes an output device to output the result of the determination.

The present disclosure relates to a COPD ventilator dependence risk prediction method and device, an intelligent decision-making method and system, the method comprising: constructing a quantitative index system of a respiratory supply and demand balance model; obtaining multi-modal data of a patient, calculating features of the multi-modal data according to the quantitative index system to obtain a supply-side feature vector, a demand-side feature vector and a load-side feature vector; processing the feature vectors using a trained deep learning model to obtain a ventilator dependence risk prediction result; obtaining the ventilator dependence risk prediction result and a respiratory homeostasis index of the patient; performing cluster analysis on the feature vectors to identify a ventilator dependence phenotype and calculate a subtype probability distribution; and determining an individualized treatment intervention strategy according to the ventilator dependence risk prediction result, the respiratory homeostasis index and the ventilator dependence phenotype. The present disclosure can realize accurate assessment of ICU ventilator dependence risk and intelligent generation of intervention strategies.

The invention relates to the technical field of bone metabolism disease related markers, in particular to application of CASQ1 protein in preparation of bone metabolism related disease diagnosis and / or screening products. It is found that when the muscle function is abnormal or damaged, the content of CASQ1 protein in exosomes secreted by muscle cells is increased, the exosomes can be transferred to adjacent bone tissue, the osteogenic differentiation of bone marrow mesenchymal stem cells is inhibited, and then bone metabolism disorder is caused. The invention provides a diagnostic kit and a pharmaceutical composition taking CASQ1 protein as a detection target. The diagnostic kit and the pharmaceutical composition are used for early screening and diagnosis of bone metabolic diseases such as myogenic osteoporosis, fracture healing disorder and bone dysplasia. By detecting the content change of the CASQ1 protein in tissues or body fluids, quantitative evaluation of muscle dysfunction related bone metabolism abnormality can be realized, and a new detection means and a biological target are provided for clinical diagnosis and treatment intervention of related diseases.

The invention discloses an ankylosing spondylitis spinal injury severity prediction model construction method and system, and the method comprises the following steps: S1, carrying out the data collection of an ankylosing spondylitis patient, the data including epidemiological data, clinical phenotypes and laboratory detection indexes; s2, layering the risk layers, and dividing a spinal injury severity high-risk group and a spinal injury severity low-risk group by taking mSASSS = 25 as a threshold value; s3, eliminating radiology dependent variables through logistic regression analysis, and screening out independent predictive factors; s4, constructing a spinal injury severity prediction column graph model based on the independent prediction factors; s5, vertically mapping the measured value of each index of the patient to a scale axis corresponding to the column diagram to obtain a single score; and after the total score of the six items is accumulated, positioning is performed on a risk coordinate axis to obtain a predicted risk probability. The method improves the disease prediction capability, is high in clinical universality, is convenient and efficient to operate, and has a treatment intervention guidance value.

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Some embodiments provide for a method for identifying an intervention for a subject in furtherance of preventing development of cardiovascular disease in the subject, the method comprising: obtaining cardiometabolic health data for the subject; determining, using at least some of the cardiometabolic health data, a first trained machine learning (ML) model and for each of multiple time intervals, one or more measures of risk that the subject develops cardiovascular disease to obtain multiple measures of risk; determining, using the multiple measures of risk that the subject develops cardiovascular disease and at least one second trained ML model, benefit of administering to the subject one or more therapeutic interventions designed to reduce risk of cardiovascular disease by targering one or more modifiable causes of the cardiovascular disease; and identifying, using the determined benefit of administering the therapeutic intervention(s), at least one therapeutic intervention to recommend being administered to the subject.

A compression garment (1) for post-liposuction patients integrates real-time monitoring and therapeutic intervention to prevent deep vein thrombosis (DVT) and embolism. The stretchable, biocompatible garment applies 20-30 mmHg of compression and comprises of blood oxygen saturation (SpO2) sensors (2), respiratory rate sensors (3), and motion / posture sensors (4). A Doppler ultrasound transducer system (50) monitors venous blood flow, while a biochemical patch detects D-dimer levels in sweat or micro-lacrimal samples. Pneumatic micro-bladders cyclically inflate to promote venous return upon detecting elevated embolism risk. A detachable control module (5) processes sensor data, computes a risk index using AI-driven analytics, and triggers alerts via haptic or wireless communication to external devices or hospital EMR systems.