83 results about "Patient response" patented technology

Disclosed are systems and methods for improving processes for developing cell therapies by applying machine learning to data including manufacturing process data and clinical measurements (e.g., patient response and treatment data) to determine parameters and settings for a manufacturing process for engineering cells for use in cell therapy. Parameters and settings for a manufacturing process for genetically engineered T-cells including, but not limited to, Chimeric Antigen Receptor (CAR) T cells can be determined. A method can include receiving a set of process parameters of a cell engineering process, predicting a clinical response associated with an output of the cell engineering process by applying a machine learning model on the received set of process parameters, where the machine learning model is trained on process parameter data and clinical response data, and generating a visualization for use in a graphical user interface of the predicted clinical response.

Embodiments disclosed herein include methods and systems for managing outcomes for weight-loss GLP-1 receptor agonist therapies using biochemical data-trained models and / or other patient centric analysis. In some embodiments, one or more biomarkers or other patient data are employed for training AI / ML or other computational models and / or as inputs to AI / ML or other computational models to evaluate patient response to GLP-1 receptor agonist therapy. For example, the computational models may be configured to perform classifications with respect to success of GLP-1 receptor agonist therapy and / or adverse effects during GLP-1 receptor agonist therapy.

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.

A system for managing treatment of a patient is provided with a computer in data communication with a database storing a plurality of prompts, medical content, and patient medical history. A user device in data communication with the computer presents a first prompt to the patient and receives the patient reaction to the first prompt. Software executing on the computer parses the patient reaction into discrete and non-discrete portions, with a generative artificial intelligence program parsing the non-discrete portions to extract a key data set. A determinative artificial intelligence program then correlates the key data set and discrete response portions with the content in the database to create a determinative data set. A generative artificial intelligence program receives the determinative data set and non-discrete portions of the reaction and generates a second prompt to be displayed to the patient to refine their treatment.

Various methods and systems for efficiently providing pharmacogenetic guidelines is disclosed. A drug prescription and a patient genotype can be analyzed to provide a drug therapy recommendation. The analysis can include comparing the patient genotype and the drug name to a drug profile database, a molecular biomarker database, hospital drug reaction records and omics databases, and calculating the patient response to the drug.

A system may include a neurostimulator and a processing system configured to provide a stimulation effects map by testing stimulation parameter sets from a plurality of available stimulation parameter sets, acquiring clinical effect data indicative of a patient response to electrical energy delivered to the tissue using at least a first subset of the tested stimulation parameter sets, acquiring sensed data indicative of a sensed response to the electrical energy delivered to the tissue using at least a second subset of the tested stimulation parameter sets, and evaluating parameter sets from the plurality of untested stimulation parameter sets including for each of the evaluated parameter sets, determining an estimated response by estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquired sensed data. A stimulation parameter set may be chosen based on the map.

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) L 108G and / or THOR of the heavy chain reference sequence and / or 158R 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 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.

Disclosed are systems and methods for improving processes for developing cell therapies by applying machine learning to data including manufacturing process data and clinical measurements (e.g., patient response and treatment data) to determine parameters and settings for a manufacturing process for engineering cells for use in cell therapy. Parameters and settings for a manufacturing process for genetically engineered T-cells including, but not limited to, Chimeric Antigen Receptor (CAR) T cells can be determined. A method can include receiving a set of process parameters of a cell engineering process, predicting a clinical response associated with an output of the cell engineering process by applying a machine learning model on the received set of process parameters, where the machine learning model is trained on process parameter data and clinical response data, and generating a visualization for use in a graphical user interface of the predicted clinical response.

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.

The present invention provides a novel system of hardware designed to capture and process in real time clinical observations of patient responses and reactions in different clinical and patient settings and situations, comprising: a patient biometric data recording system in a clinical office, a mobile real time episode or event data recording device system or a wearable device recording system.

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.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.

A non-invasive ophthalmic light-based detection device for detecting pathologies associated with Alzheimer's disease (AD) in the retina of an eye. The device uses two imaging modalities, one of which directs a specific area to be interrogated by the second imaging modality. A hyperspectral reflectance imaging unit detects light reflected and / or backscattered from the retina from a broadband light source, which is used to determine one or more regions of interest. A Raman spectroscopy unit detects light from a laser that is re-emitted by the retina through the phenomenon of Raman scattering, which is targeted to the region of interest determined based on the hyperspectral reflectance information. The detection information from the hyperspectral reflectance imaging unit and the Raman spectroscopy unit is used to determine the presence of the one or more pathologies associated with AD. The detection device allows identification of high-risk groups, diagnosis and tracking of patient response to treatment.

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.

A system and method for patient diagnosis and implementation of orthodontic apparatuses and treatments. The patient can be located remotely from a remote treatment system, thereby allowing for automatic diagnosis and treatment of the patient without interaction with a medical professional or associated staff. A neural network of an artificial intelligence engine (AI engine) of the remote treatment system can utilize images and other data captured by a patient, as well as patient responses to questionnaires, to derive information regarding the patient's symptoms, and the severity of those symptoms. The AI engine can further be adapted, including via use of historical information regarding a plurality of patients and information from the patient, to obtain a diagnosis and treatment recommendation. The AI engine can further be configured to determine, based on analysis of patient provided images, an appropriately sized orthopedic apparatus, which can be sent to the patient for implementation.

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.

The present invention relates to an apparatus for treating neurological or psychiatric disorders using i-wave periodicity-dependent Quadri-Pulse Theta Burst Stimulation (iQPS). The system comprises a transcranial magnetic stimulation (TMS) stimulator configured to deliver test pulses to a target motor cortical area and therapeutic theta bursts to a treatment area with pulse frequency tailored to the patient's individual i-wave periodicity. An electromyography (EMG) device records motor evoked potentials (MEPs) from a target muscle and analyzes the intervals between MEPs to determine the i-wave periodicity. The personalized stimulation enhances cortical plasticity by synchronizing stimulation with the brain's intrinsic neural timing. The invention is applicable to neurological and psychiatric conditions that include depression, schizophrenia, stroke recovery and neuropathic pain. Advantages include improved treatment efficacy, reduced variability in patient response and a non-invasive, adaptive therapy model that aligns with the patient's unique neurophysiological characteristics, offering a precise and scalable approach to brain stimulation.

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.

The invention relates to a system for carrying out intraoperative functional mapping of at least one neuronal function of a patient during an operation on the patient, which system comprises: a stimulation unit for electrically stimulating a brain area of ​​the patient at a stimulation site during the operation; a second stimulation unit for providing at least one optical and / or acoustic stimulation for the patient during the electrical stimulation of the brain area of ​​the patient by the first stimulation unit; a synchronization unit for synchronizing the first stimulation unit and the second stimulation unit; at least one recording unit for recording a response of the patient during the electrical stimulation of the brain area of ​​the patient by the first stimulation unit and the stimulation of the patient by the second stimulation unit;an evaluation unit for evaluating the patient's response recorded by the recording unit as to whether the electrically stimulated brain area of ​​the patient is relevant for the performance of a neural function; and a communication and data transmission network for conducting non-verbal communication between users of the various units of the system during surgery on the patient and for exchanging data between the units of the system.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.