43 results about "Patient response" patented technology

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The present invention relates to a computer-implemented method and device for predicting a patient response to an agent, comprising receiving an avatar of the patient and respective data of organoid response for said patient, said avatar comprising biological data and clinical data of said patient, said avatar and said respective data of organoid response defining at least one patient feature and at least one organoid response feature respectively; receiving a first ensemble of one or more trained learning models; calculating the first output vector by providing one or more of said at least one patient feature and one or more of at least one organoid response feature as input to said first ensemble of one or more trained learning models; obtaining at least a PFS and / or an OS for the patient using the first output vector; and obtaining a prediction of the patient response to the agent using said obtained PFS and / or OS.

Computer-implemented methods for providing interactive electronic treatment assistance for benign prostatic hyperplasia and advanced prostate cancer are disclosed. The methods can include automatically sending, by a disease and disorder treatment device, an electronic pathway sign-up link to a smart mobile device. The method can include sending a plurality of inquiries about the BPH condition and / or the advanced prostate cancer condition, to the smart mobile device. The method can include receiving a plurality of patient responses from the smart mobile device. The method can include processing and storing the plurality of patient responses. The method can include providing, by the disease and disorder treatment device, patient education about various treatment therapies for advanced prostate cancer. The method can include automatically monitoring a patient having the advanced prostate cancer condition during the various treatment therapies. The method can include intervening with the various treatment therapies using the smart mobile device.

This disclosure is directed to devices, systems, and techniques for controlling electrical stimulation. In some examples, a system includes a user interface and processing circuitry. The processing circuitry is configured to output, for display by the user interface, a message requesting the patient perform a set of actions, receive, from the user interface, user input indicative of a patient response associated with the set of actions, and determine, based on the user input, one or more adjustments to a control policy which controls electrical stimulation delivered by a medical device based on a plurality of evoked compound action potentials (ECAPs) sensed by the medical device.

This disclosure presents systems and methods for baroreflex activation therapy (BAT) to regulate inflammation by modulating autonomic nervous system activity. Electrical stimulation is delivered to one or more baroreceptors, and real-time physiological signals are monitored, including inflammatory biomarkers (e.g., TNF-α, IFN-γ, IL-1, IL-6, IL-12, CRP) and autonomic parameters such as heart rate variability (HRV). A closed-loop feedback module analyzes biomarker fluctuations and autonomic balance changes and dynamically modifies stimulation parameters, including amplitude, pulse width, frequency, and duty cycle, to suppress systemic inflammation and restore autonomic balance. In certain embodiments, multi-channel stimulation architectures and AI-driven algorithms or machine learning models analyze biomarker trends and historical patient response patterns to optimize therapy. The disclosed BAT systems and methods may be used to treat inflammatory diseases, autoimmune disorders, cancer progression, chronic inflammatory pain conditions, arrhythmias associated with inflammation, and metabolic dysfunction.

Aspects of the disclosure relate to PD-1 binding proteins comprising immunoglobulin domains which bind specifically to PD-1 and comprise at least three or six specific complementarity determining regions (CDRs) and which comprise a specific feature in a variable domain framework region(s), e.g., heavy variable domain framework 1, heavy domain framework 4, and / or light chain variable domain framework 3 region(s). In some embodiments, the PD-1 protein comprises the substitution(s) L108G and / or T110R of the heavy chain reference sequence and / or I58R relative to the light chain reference sequence(s). The PD-1 binding proteins are useful, e.g., as immunologic adjuvants, for detecting and quantifying PD-1, monitoring patient responses to therapies, diagnosing PD-1 related conditions, and treating or preventing disorders involving PD-1 expressing cells, such as, e.g., cancers and autoimmune diseases. Also provided herein are antigen binding proteins comprising amino acid substitutions in the heavy chain framework 4 region for improved stability and solubility.

The application relates to the technical field of electroencephalogram rehabilitation, and particularly discloses a depression neural regulation method and system based on TMS-EEG and machine learning driving, which comprises the following steps: S1: collecting electroencephalogram signals before transcranial magnetic stimulation of a patient; S2: pre-processing the electroencephalogram signals and extracting biomarker features; S3: inputting the extracted biomarker features into a trained machine learning model to obtain decision reference data; S4: using the above parameters as auxiliary reference data to adjust the transcranial magnetic stimulation control parameters; and S5: collecting the electroencephalogram signals again after the transcranial magnetic stimulation and using the electroencephalogram signals for optimizing subsequent personalized transcranial magnetic stimulation parameter suggestions. The application converts multi-time-point electroencephalogram signals into quantitative biomarkers, thereby providing doctors with objective decision reference data throughout the whole diagnosis and treatment process. This enables doctors to predict the patient's reactivity before stimulation regulation, thereby avoiding invalid stimulation; and the doctors can dynamically optimize the parameters according to the neurophysiological feedback during the stimulation, thereby realizing personalized and accurate regulation.

A computing system includes a medical application for patient follow-up and education after a healthcare visit. The computing system includes a database with educational materials relating to diagnosis, medications, procedures, etc. A computing system obtains a selection from a healthcare provider, using a provider application or web portal, of the educational materials relating to a patient diagnosis or treatment plan. The computing system provides access to the selected educational materials to the patient using a patient application or web portal. The computing system generates a comprehension test relating to the educational materials and a symptom questionnaire relating to the patient diagnosis or treatment plan. The computing device may also generate a compliance questionnaire relating to patient compliance with the treatment plan. The computing device receives a patient response to the comprehension test, the symptom questionnaire and the compliance questionnaire and provides the patient response to the healthcare provider.

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. Here, we used molecular profiling data to identify biomarker signatures (biosignatures) that predict a tumor primary lineage, cancer category or type, organ group and / or histology. The signature may use genomic and transcriptome level information.

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

Systems and methods are disclosed to evaluate patient response to cardiac rhythm management therapy and remotely reprogram an implantable medical device, including receiving physiologic information of a patient in a first time period responsive to a first cardiac rhythm management therapy, determining a first patient response metric indicative of patient response to the first cardiac rhythm management therapy, generating a reprogramming recommendation for the implantable medical device including a second cardiac rhythm management therapy based on the first patient response metric, and remotely reprogramming the implantable medical device to provide the second cardiac rhythm management therapy to the patient in a second time period.

The invention provides a method of assessing the appropriate therapeutic dose of 1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylic acid to administer to a patient in need thereof, comprising the steps of:(i) testing whether or not the patient has the poor metabolizer genotype; and(ii) if the patient does not have the poor metaboliser genotype, administering 1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylic acid, or a pharmaceutically acceptable salt thereof, to the patient at the standard therapeutic dose; and(iii) if the patient does have the poor metaboliser genotype, either(a) administering 1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylic acid, or a pharmaceutically acceptable salt thereof, to the patient at a therapeutic dose below that of the standard therapeutic dose; or(b) not administering 1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylic acid, or a pharmaceutically acceptable salt thereof, to the patient.

The application relates to the technical field of intelligent medical auxiliary diagnosis and automatic evaluation of nerve functions, in particular to an NIHSS score automatic evaluation system for stroke patients. The system comprises a multi-modal induction and perception unit, which is used as a front-end data inlet and collects patient responses in real time to generate a video stream containing depth and color information and a synchronous audio stream; a dynamic reference calibration unit, which extracts kinematic characteristics to construct an individualized neural reference template; a neural motion frequency spectrum decomposition unit, which generates a frequency spectrum pathological characteristic vector distinguishing muscle weakness and ataxia; a contralateral mirror inhibition analysis unit, which generates compensation and driving confidence representing real neural driving intentions; a cross-modal reflection analysis unit, which generates a sensory pathway integrity index according to the difference in nerve conduction velocity; and a collaborative scoring decision engine, which maps multi-modal characteristics to a standardized NIHSS score. The application effectively eliminates the interference of age and basic physical fitness on the score, and can also provide high-precision comparison references for subsequent abnormality determination of the affected side.

Disclosed are methods for assessing the presence or absence of a therapeutic concentration of a deuterated docosahexaenoic acid during treatment of a patient with a retinal oxidative disease.

An AI-driven medical intake assistant system includes a computing device having a processor and memory configured to execute an intake module, an abnormality detection module, and an electronic health record (EHR) integration module. The intake module presents a conversational interface that dynamically selects patient intake questions using a rule-based branching decision tree with clinical topic tags and transition rules. One or more connected diagnostic devices automatically capture patient vital sign measurements, including blood pressure, temperature, pulse oximetry, or weight, and transmit the measurements to the computing device. The abnormality detection module compares captured measurements to predefined threshold values or historical baseline data retrieved from the EHR and generates provider-facing alerts when deviation thresholds are exceeded. The EHR integration module maps patient responses and vital sign measurements into predefined structured EHR fields prior to a provider consultation.

This invention relates to the field of medical data analysis technology, specifically to a patient response assessment method and system based on chemotherapy data analysis. The method includes the following steps: accumulating drug administration time and dose to form a cumulative exposure sequence; matching biomarker time and concentration to form response pairs; extracting adjacent differences and dividing assessment intervals according to correlation; calculating the interval change ratio to obtain an assessment coefficient sequence; and combining the drug administration time window and the response time window to calculate the chemotherapy response result. In this invention, by time matching and difference analysis of cumulative exposure and tumor marker concentration data, the relationship between exposure changes and indicator changes is characterized, reducing fixed threshold assessment bias, improving the utilization of time series data, enhancing the accuracy and individual adaptability of assessment results, improving the reliability of chemotherapy response assessment, and stabilizing assessment consistency.

1. Name of the product in this design: Main body of a erythrocyte sedimentation rate analyzer. 2. Purpose of this design: The erythrocyte sedimentation rate (ESR) analyzer is used as laboratory equipment to measure and analyze blood as an indicator of patient inflammation, anemia, and patient response to drugs such as chemotherapy. The main body of the erythrocyte sedimentation rate analyzer is used as part of the erythrocyte sedimentation rate analyzer. 3. The key design features of this product are the shape of the portion shown by the solid line. 4. The image or photograph that best illustrates the design's key points: a 3D model.

A system and method for predicting, proposing and / or evaluating suitable medication dosing regimens for a specific individual as a function of individual-specific characteristics and observed responses of the specific individual. Mathematical models of observed patient responses are used in determining an initial dose. The system and method use the patient's observed response to the initial dose to refine the model for use to forecast expected responses to proposed dosing regimens more accurately for a specific patient. More specifically, the system and method uses Bayesian averaging, Bayesian updating and Bayesian forecasting techniques to develop patient-specific dosing regimens as a function of not only generic mathematical models and patient-specific characteristics accounted for in the models as covariate patient factors, but also observed patient-specific responses that are not accounted for within the models themselves, and that reflect variability that distinguishes the specific patient from the typical patient reflected by the model.

Methods of predicting response of a subject suffering from cancer to an anti-PD-1 / L1 immunotherapy, as a monotherapy or combination therapy, comprising calculating a resistance score for factors expressed by the subject, summing the resistance score to produce a total resistance score, wherein a total resistance score beyond a predetermined threshold indicates a subject is predicted to be resistant to the anti-PD-1 / L1 immunotherapy as a monotherapy or combination therapy, are provided.

An intelligent dosing platform incorporating comprehensive integration capabilities for non-syringe medication delivery devices including intelligent insulin pumps, intravenous delivery systems, and automated infusion devices. The platform features device integration modules configured to interface with pump-based delivery systems, dosage monitoring modules that track administration through continuous delivery mechanisms, and coordination modules managing treatment schedules across multiple device types. The system includes flow rate management capabilities for controlling infusion rates and pump delivery speeds, safety monitoring modules detecting occlusions and system malfunctions, and alert generation systems providing notifications for delivery interruptions and maintenance requirements. Data synchronization modules maintain consistent treatment records across pump systems and healthcare information networks, while remote monitoring capabilities enable healthcare provider oversight of device performance and patient response indicators. The platform incorporates compliance tracking, patient authentication, and predictive analytics modules for forecasting maintenance needs and optimizing multi-device treatment delivery coordination.

A system and method for modeling patient-specific spinal cord stimulation (SCS) is disclosed. The system and method acquire impedance and evoked compound action potential (ECAP) signals from a lead positioned proximate to a spinal cord (SC). The lead includes at least one electrode. The system and method determine a patient-specific anatomical model based on the impedance and ECAP signals, and transform a dorsal column (DC) map template based on a DC boundary of the patient-specific anatomical model. Further, the system and method map the transformed DC map template to the patient-specific anatomical model. The system and method may also include the algorithms to solve extracellular and intracellular domain electrical fields and propagation along neurons. The system and method may also include the user interfaces to collect patient responses and compare with the patient-specific anatomical model as well as using the patient-specific anatomical model for guiding SCS programming.

This invention relates to an instrument for treating neurological or psychiatric disorders using i-wave periodicity-dependent four-pulse theta burst stimulation (iQPS). The system includes a transcranial magnetic stimulation (TMS) stimulator configured to deliver test pulses to a target motor cortex region and therapeutic theta bursts to a treatment region, wherein the pulse frequency is customized according to the patient's individual i-wave periodicity. An electromyography (EMG) device records motor evoked potentials (MEPs) from the target muscles and analyzes the intervals between MEPs to determine the i-wave periodicity. Personalized stimulation enhances cortical plasticity by synchronizing stimulation with the brain's intrinsic neural timing. This invention is applicable to neurological and psychiatric conditions including depression, schizophrenia, stroke recovery, and neuropathic pain. Advantages include improved therapeutic efficacy, reduced patient response variability, and a non-invasive adaptive therapy model aligned with the patient's unique neurophysiological characteristics, thus providing a precise and scalable approach to brain stimulation.

Disclosed are methods of predicting a radiotherapy success in a method of treating a lung cancer of a patient, the use of specific markers for predicting a radiotherapy success in a method of treating a lung cancer of a patient, a database comprising the markers, and a computer program product for use in such a method.

Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

A system for remote assessment of clinical response and dynamic optimization of the chemotherapy regimen in oncology, consisting of: a device for clinical assessment of patient response, designed as a physical machine structure with a housing for use on the body or at the bedside; a housing-mounted physiological sensor unit configured to record continuous or periodic measurements of heart rate, heart rate variability, respiratory rate, peripheral oxygen saturation, body temperature, physical activity and sleep-related parameters of an oncology patient during chemotherapy; a biochemical sensor unit integrated into the housing, configured to detect biochemical indicators related to response to chemotherapy and toxicity, including inflammatory markers, metabolic indicators, hematological trends or drug-related metabolites; a patient interaction unit configured to receive symptom data reported by the patient, corresponding to treatment tolerability and functional status; an embedded processing unit that is operationally connected to the physiological sensor unit, the biochemical sensor unit, and the patient interaction unit, and is configured to perform signal conditioning, temporal alignment, data integrity verification, and encryption of the acquired patient data; a communication unit that is operationally connected to the embedded processing unit and configured to transmit the processed patient data to a remote processing infrastructure using secure communication protocols; a remote processing system comprising at least one processing unit and a storage unit for storing executable instructions, wherein the processing unit is configured to aggregate longitudinal patient data across chemotherapy cycles, analyze correlations between recorded physiological data, biochemical indicators, patient-reported outcomes, and parameters of chemotherapy administration, and calculate a clinical assessment of response; and A system for interacting with physicians, configured to display the results of the clinical response assessment and generate recommendations for optimizing the chemotherapy regimen, which are subject to review and approval by the physician.

In one aspect, a data processing system includes a computer-readable memory containing computer-executable instructions; and at least one processor configured to execute executable logic, the executable logic including at least one artificial neural network trained to predict one or more reactions to a chemical substance by identifying one or more discrete biological tissue components in a biological image. While the at least one processor is executing the computer-executable instructions, the at least one processor is configured to perform operations including: receiving spatially arranged image data representative of a biological image of a patient; generating spatially arranged tile data representative of a plurality of image tiles; processing the spatially arranged tile data through the one or more data structures storing one or more portions of executable logic included in the artificial neural network to predict one or more reactions of the patient.

Disclosed herein are methods of determining whether a subject suffering from a cancer (e.g., ovarian cancer) is sensitive to treatment with a PARP inhibitor (e.g., olaparib). In some embodiments, the method comprises obtaining a machine learning model trained on genetic information, clinical data and true expression, applying the machine learning model to reconstruct expression data for an individual tumor (or tumor model systems) using as input clinically available clinical and DNA data associated with the cancer subject and tumor sample or tumor model system or circulating tumor cell or tumor DNA in order to generate a novel signature indicating sensitivity of the subject to the PARP inhibitor, and predicting the treatment outcome in the cancer subject if treated with the PARP inhibitor.

The present invention provides a method for predicting whether a patient having, suspected of having, or at risk of developing an inflammatory condition will respond to treatment with a disease-modifying antirheumatic drug (DMARD) comprising measuring the level of at least one specialized pro-resolving mediator (SPM) in a DMARD naïve patient sample and classifying the patient as a predicted DMARD responder or a predicted DMARD non-responder. The present invention also provides a method of assessing the efficacy of a disease-modifying antirheumatic drug (DMARD) for use in the treatment of an inflammatory condition in a patient. The present invention also provides a method of predicting which rheumatoid arthritis pathotype a patient having, suspected of having, or at risk of developing rheumatoid arthritis will develop.

The utility model relates to the technical field of medical instruments, in particular to a disposable anti-splashing bronchoscope drape which comprises a drape body, the drape body is of a rectangular structure, the middle of the front portion of the drape body is provided with a peripheral mouth portion, a plurality of evenly-distributed miniature air holes are formed in the outer wall of the peripheral mouth portion in a penetrating mode, and the drape body can effectively cover the eyes. The eye patch is used for preventing splashed secreta from entering the eyes and the area around the head, preventing dripping liquid from entering the eyes during operation of the bronchoscope, preventing a patient from feeling nervous when directly seeing the operation process of the bronchoscope, and preventing most secreta sprayed through the mouth and the like due to the fact that the mouth peripheral part covers the mouth peripheral area below the nose of the patient. Meanwhile, the micro air holes can ensure that the patient breathes and ventilates through the mouth, the reaction of the patient and discharge of secretions in the mouth in the operation process can be observed more conveniently and clearly through the transparent film area around the mouth, the operation hole is used for being placed above the nose, the nasal cavity can be fully exposed, and lens entering through the nasal bronchoscope is ensured.

Systems and methods for generating a patient-specific clinical assessment are disclosed. A processor receives patient health data corresponding to a patient. The patient health data is analyzed using a trained large language model (LLM) configured to process the patient health data. Clinical insights are determined based on the analysis of the patient health data using the trained LLM. A set of interactive prompts is generated for a patient interface based on the identified clinical insights. The set of interactive prompts may obtain additional information associated with the patient. A set of patient responses responsive to the generated set of interactive prompts are received. The clinical insights and the set of patient responses are displayed on a clinician interface.