36 results about "Therapy intervention" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

Methods of improving addiction in a patient in need thereof are provided. Methods include exposing the patient to drug related cues and measuring the patient's craving levels or physiological levels during or after cue exposure to determine the patient's status or condition. Such a determination can indicate whether the patient is at risk of engaging in the addictive activity and can guide therapeutic intervention.

An AI platform is used for developing a combination therapy for a patient afflicted with a tumor that has produced clones. The combination therapy, which includes at least two perturbations, is capable of targeting clones (including subclones) that have escaped therapeutic intervention due to resistance and / or evolution. The AI platform is trained with perturbation data obtained from at least one cell line that has similar characteristics to a clone of interest. The trained AI platform predicts how the clone of interest will respond to perturbations and ranks the perturbation responses from highest to lowest. At least one cell line may be an existing cell line from a well-established database or synthetic cell line generated by the AI platform. The AI platform may include one or more of a machine learning platform, deep learning platform, artificial neural network (ANN), convolution neural network (CNN), and generative adversarial network (GAN).

Laboratory apparatus for evaluation and modulation of ivermectin drug–induced neurotoxicity using therapeutic intervention agents

The invention discloses a heart rupture early warning model training method and device and a heart rupture early warning method and device. The training method of the heart rupture early warning model is applied to a data processing terminal, and comprises the following steps: obtaining a training sample set which comprises a plurality of training samples, a feature set of each training sample and a heart rupture warning label, the feature set comprises a plurality of features in population features, social economic features, past medical history features, disease expression features, test and examination features, treatment intervention features, patient life features and medical institution features; and training a preset model by taking the feature set of each training sample as an independent variable and the cardiac rupture warning label of each training sample as a dependent variable to obtain a cardiac rupture early warning model. According to the embodiment of the invention, the heart rupture early warning model with high accuracy can be obtained through training.

The application discloses an atrial fibrillation patient health risk prediction method based on AI and belongs to the medical information technology field, and specifically comprises the following steps: receiving physiological parameter records and intervention treatment records of atrial fibrillation patients, performing 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 to output a basic risk score; inputting the intervention response features and the basic risk score into an intervention effect separation network 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; and combining the basic risk score, the post-intervention risk score and the net effect value to generate an individual long-term risk prediction trajectory of the patient.

The present application provides a kind of for burn and scald wound exudate response self-shedding core-shell microgel powder and its preparation method, belong to medical material technical field.The core-shell microgel powder provided by the present application can quickly absorb a large amount of exudate and form a conformal hydrogel in situ after being applied to the wound, providing an ideal wet environment and physical protection for wound healing;Its exudate response characteristics not only can intelligently release active ingredients to actively regulate inflammation and promote repair, but also can achieve gentle self-shedding of the dressing in the later healing stage, greatly improving patient experience and treatment compliance.The product combines efficient liquid management, active therapeutic intervention and painless removal, providing a new and integrated solution for the clinical care of complex exudate wounds such as burns and scalds.

A method may include receiving objective information associated with a user that uses the at least one medical device during execution of a treatment plan, and based on the objective information, generating, using one or more terms defined in at least one medical terminology database, at least one input prompt. The method may also include providing, to an at least artificial intelligence engine configured to provide one or more predictions indicating one or more treatment interventions, the at least one input prompt. The method may also include receiving at least one prediction indicating one or more treatment interventions, and controlling, while the user uses the at least one medical device, and based on at least one aspect of the one or more treatment interventions indicated by the at least one prediction, the at least one medical device.

A method for in vivo detection, diagnosis, and therapeutic intervention of diseases is disclosed. The method includes utilizing synthetic sensors configured to interact with disease-specific signals, wherein the signals are selected from the group consisting of proteins, post-translational modifications (PTMs), RNA, DNA, metabolites, or combinations thereof; employing a diagnostic kit designed to collect and process signals excreted or released into body fluids, wherein the body fluids are selected from the group consisting of urine, saliva, sweat, tears, exhaled breath, or combinations thereof; and analyzing the collected signals using a reader configured to process the signals and integrate the data with an artificial intelligence (AI) platform to generate real-time diagnostic outputs.