108 results about "Patient response" patented technology

A computer-implemented method includes receiving patient responses over a network from a recorded screening interview, utilizing a custom Large Language Model (LLM) to generate transcriptions and insights from the audio, performing video sentiment analysis to assess emotional states, and based on these data, generating an AI model to predict risk levels for various mental health conditions. The method further comprises presenting personalized questions based on previous screening insights, creating longitudinal summaries of patient histories, continuously improving the AI models through reinforcement learning, and integrating a recommendation engine to suggest targeted interventions. Additionally, the method includes tracking outcomes over time and enabling causal inference across multiple screenings, thereby providing a comprehensive platform for mental health assessment, intervention, and outcomes tracking.

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 identified 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 being configured to 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.

An approach to detecting patient limb movement during electrostimulation can include delivering an electrostimulation waveform, e.g., generated by an electrostimulation electronics unit, having a frequency within a range of 500 Hz to 10,000 kHz, and having an average root-mean squared current exceeding 5 milliamps. Concurrently, motion data can be received from an inertial measurement unit (IMU) during the therapy session. Based on this motion data, a timing, frequency, or amplitude of limb movements, exhibited by the patient over the therapy session, can be estimated such as to provide insight into patient response and potential symptoms of a sleep disorder.

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.

Device for personalized radiotherapy using AI-controlled dosimetry, comprising: - a patient data acquisition unit configured to receive real-time data on patient-specific health information, including medical imaging data from the patient's CT, MRI and PET scans, biometric data from wearable or hospital-grade monitoring devices, electronic health records (EHRs) and previous treatment history; - The medical image processing unit, which analyzes and processes the captured image data, performs various functions, from tumor segmentation and identification of organs at risk to the spatial alignment of multimodal images for radiotherapy planning; - a CPU is configured to preprocess other collected data by (a) normalizing imaging and physiological data, (b) aggregating imaging and physiological data, (c) encoding imaging and physiological data for subsequent analysis, and (d) ensuring the compatibility and consistency of data from different sources; - an artificial intelligence (AI) controlled dosimetry machine that is communicatively connected to the central processing unit, wherein the AI-controlled dosimetry machine is configured to use machine learning algorithms to evaluate patient-specific parameters, optimize the distribution of a radiation dose and predict responses to treatment; - a visualization and treatment planning interface coupled with the AI-driven dosimetry machine, the visualization interface being configured to provide radiation dose distributions, patient-specific treatment recommendations, and comparative dose plans that enable data-driven adjustments by clinicians; and - a visualization interface with a feedback mechanism that allows real-time input from practicing clinicians and continuous automatic recording of patient response, which is used for iterative improvement of both the dosimetry model and the treatment plan.

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.

The invention provides a device and system implanted outside epidural under ultrasonic guidance for temporary electrode stimulation of spinal cord, and the device comprises a risk assessment module which carries out the comprehensive assessment of a patient through a multi-modal risk assessment model, and outputs a risk level or probability; the configuration selection module is used for analyzing the parameter information of the patient according to the parameter information of the patient when the evaluation result is passed, and setting guide configuration requirements; the probe guiding module guides the temporary electrode through an ultrasonic thermodynamic diagram; the effect test module is used for testing electrode functions by using a stimulator and adjusting electrode positions; the fixing and closing module fixes the electrode to avoid displacement; and the recording and monitoring module is used for recording electrode implantation depth, stimulation parameters and patient response. The system can reduce invasiveness and ultrasonic guidance accurate positioning, reduces errors and complications, is easy to popularize and apply, is relatively simple to operate, and is suitable for being popularized and applied in more medical places.

Computerized systems and methods for digital histopathology analysis are disclosed. In one embodiment, a series of deep learning networks are used that train, in succession, on datasets of successively increasing relevance. In some examples, learned parameters from at least a portion of one deep learning network are transferred to a next deep learning network in a succession of deep learning networks. In some examples, at least one of the deep learning networks includes a self-supervised learning network. In some examples, at least one of the deep learning networks includes an attention-based learning network. These and other examples and details are disclosed herein in various contexts including, for example evaluating genotypes of cancer tissue (e.g., bladder, prostate, or lung cancer) using histopathology images. In some examples, the context is to assist in predicting presence or absence of certain cancer genotypes and / or predicting patient responses to a new treatment.

An infusion control test system includes a patient bio-simulator configured to generate first simulated biophysical data; an infusion control system configured to determine a first infusion profile in response to the first simulated biophysical data; and an infusion pump simulator configured to simulate a first administration of medication according to the first infusion profile. The patient bio-simulator is further configured to: simulate a patient response to the simulated first administration of medication; and generate second simulated biophysical data by adjusting the first simulated biophysical data according to the patient response.

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.

The invention relates to an intelligent follow-up visit method and system based on a large model, and the method comprises the steps: obtaining follow-up visit data of a patient, and guiding a large model to generate a patient follow-up visit problem for the disease type of the patient through employing an instruction type prompt word based on a preset follow-up visit problem corresponding to the disease type of the patient. And calling the large model to construct a BERT classification model based on the patient follow-up problem and the corresponding patient reply content. And calling the BERT classification model to judge whether the reply content of the patient meets a preset standard, and generating reasonable feedback content when the reply content of the patient meets the preset standard. And performing formatted information extraction on the reasonable feedback content and the patient follow-up problem to identify and extract key medical information, and performing standardized arrangement on the key medical information to generate a structured follow-up record. According to the method, the follow-up work of the patient is automatically processed based on a large model, the demand and health state of the patient are recognized through multiple rounds of dialogues, the follow-up strategy is adjusted in real time, and the follow-up processing efficiency and flexibility of the patient are improved.

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.

Methods for treating a patient having a solid tumor are disclosed, comprising: (a) obtaining a tumor sample from the patient; (b) assessing the sample for levels of biomarkers for at least one of innate immune cells and adaptive immune cells; and (c) administering an effective amount of an immunotherapy to the patient if the sample comprises elevated levels of CD68+ PD-L1+ macrophages and CD8+ T cells compared to control and / or elevated levels of CD20+ B cells compared to control and / or low levels of CD163 expression compared to control and PD-L1 expression of greater or equal to (≥) 50%.

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 methods for treating a patient having a solid tumor, the methods comprising: (a) obtaining a tumor sample from the patient; (b) evaluating the sample for the level of a biomarker for at least one of innate and adaptive immune cells; and (c) administering to the patient an effective amount of immunotherapy if the sample contains elevated levels of CD68+PD-L1+ macrophages and CD8+ T cells relative to a control and / or elevated levels of CD20+ B cells relative to a control and / or low levels of CD163 expression and 50% or greater (≧) PD-L1 expression relative to a control.

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.

The present invention relates to the field of oncology and provides compounds, compositions, uses and methods for treating cancer, particularly stroma-rich tumors, by selectively targeting cancer-associated fibroblasts (CAFs). CAF targeting is one strategy for enhancing the effectiveness of immunotherapy and can improve the patient response rate. Furthermore, this specificity in the target and related pathways reduces damage to normal tissues.

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.