169 results about "Patient population" patented technology

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of a free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of a free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg or less of bupropion hydrochloride, or a molar equivalent amount of the free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg or less of dextromethorphan hydrobromide, or a molar equivalent amount of the free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of a free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of a free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of the free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of the free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of a free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of a free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of the free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of the free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

This disclosure relates to administration of a combination of: 1) about 100-110 mg, about 104-106 mg, or about 105 mg of bupropion hydrochloride, or a molar equivalent amount of the free base form or another salt form of bupropion; and 2) about 40-50 mg, about 44-46 mg, or about 45 mg of dextromethorphan hydrobromide, or a molar equivalent amount of the free base form or another salt form of dextromethorphan in certain patient populations, such as patients having moderate renal impairment, patients receiving a concomitant strong CYP2D6 inhibitor, patients who are known CYP2D6 poor metabolizers, those in need of an NMDA antagonist that does not cause dissociation, and those at risk of QT prolongation.

Disclosed herein are computer-implemented methods and systems for assessing a risk value of a target patient, the method comprising: receiving, at a server, target patient data via a connected device; at the server, one or more risk values associated with the target patient data are estimated using one or more risk assessment models. The target patient data may include historical data, patient population level data, and real-time data. The risk assessment model may include one or more of the following: a historical data risk assessment model, a real-time data risk assessment model or a combined risk assessment model. Methods for training such models are described. The described systems and methods preferably enable continuous patient monitoring based on patient's historical data and continuous real-time physiological data.

The invention discloses a method and a system for evaluating movement of two lower limbs of a stroke patient, and relates to the technical field of movement function evaluation. The invention discloses a stroke patient double-lower-limb movement evaluation system which comprises a task guide module, a movement acquisition module, a movement scoring module, an interference evaluation module, a trend analysis module, a combination analysis module and a result output module. According to the method, the scoring model based on crowd category feature matching is constructed, exercise ability scoring is performed by adopting a machine learning algorithm or a fixed weighted combination, personalized evaluation is realized according to the group features of different patients, and the generalization ability and prediction accuracy of the scoring model are improved; group modeling is carried out by introducing features such as patient age, gender and motion level, and a scoring strategy is automatically switched according to the number of samples, so that the scoring mechanism also has reliability and clinical consistency in a family scene with limited data volume.

This disclosure relates generally to methods for treating an inflammatory bowel disease ("IBD", e.g., Crohn's disease or ulcerative colitis) in a patient. More particularly, this disclosure relates to methods for selecting a therapy for treating a patient suffering from an IBD. In embodiments, the foregoing can also be used to assess the severity of the IBD. The predictive aspects of said methods can facilitate and expedite the identification and stratification of IBD patient populations that are responsive to treatment with a RIPK2 inhibitor. The foregoing methods can further include treating the IBD by administering a RIPK2 inhibitor to the patient.

A method and algorithm for dosing single and multiple drugs for an individual patient includes the step of collecting data from the individual patient. The data includes drugs to be taken by the patient, the patient's age, medical condition, metabolism, and other factors. The method further includes the steps of analyzing the individual patient data in view of dosing criteria established based on patient population data. Based on the analysis, a dose of the single or multiple drugs is recommended. Alternative treatment options can also be recommended.

A implemented method, computing system and computer-readable medium for stratifying patient cancer risk using molecular data includes receiving molecular data; processing the molecular data using a machine learning model; and generating a matched treatment strategy for the patient based upon the patient's molecular data risk. A computer-implemented method, computing system and computer-readable medium for training a machine learning model to stratify patient cancer risk using molecular data includes receiving a patient training dataset, and a reference training dataset; selecting a cohort of patients; selecting a small subset of genes using univariate selection; generating a corrected reference training dataset; selecting a smaller subset of genes using multivariate selection; training a survival model; and (g) selecting a decision threshold to identify a patient population.

The invention belongs to the technical field of molecular biology, and particularly provides a biomarker for traumatic brain injury detection, the biomarker comprises NSE and GFAP, and preferably further comprises at least one of S100B, UCH-L1, P-tau and IL-6. On the basis of the biomarker, a reagent card, a kit, a system and other traumatic brain injury assessment products are provided, a rapid and non-invasive assessment alternative scheme is provided for traumatic brain injury, and assistance is provided for early intervention treatment. The radiation exposure risk of the patient is reduced, the diagnosis process is accelerated, and the illness state can be evaluated more comprehensively. Biomarkers are used in combination with the clinical severity of TBI and therapeutic care pathways, enabling better identification and hierarchical management of mild and severe patients. The biomarkers of the invention can provide extremely significant and highly credible test results for specific patient populations requiring admission treatment or at different clinical severity.

The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

A system receives feature parameters, each identifying possible values for one of a set of features. The system receives outcomes corresponding to the feature parameters. The system generates a simulated patient population dataset with multiple simulated patient datasets, each simulated patient dataset associated with the outcomes and including feature values falling within the possible values identified by the feature parameters. The system may train a machine learning engine based on the simulated patient population dataset and optionally additional simulated patient population datasets. The machine learning engine generates predicted outcomes based on the training in response to queries identifying feature values.

The invention relates to the technical field of serum specimen typing identification, in particular to a serum specimen typing identification system based on patient centralized detection. Comprising a data acquisition module, a data processing module, a data analysis module and a typing identification module, the data acquisition module is used for collecting and recording serum index data of a patient; according to the method, serum index data, including C-reactive protein, glucose, lactic dehydrogenase, TNF-alpha, IgG and IgM, of the patient are collected, an index matrix is constructed, and homogenization treatment is performed, so that a serum index distribution map of the patient is formed, the relationship between serum index values of the patient is conveniently and intuitively reflected, the synergistic reaction effect of each index on inflammation is reflected, and the sensitivity of the patient is improved. Meanwhile, a serum specimen of a new patient is subjected to typing, disease indexes are calculated, and medical staff can conveniently carry out targeted treatment on the patient.

The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

An embodiment collects a first set of patient data and a first set of treatment data associated with a patient population treated with spinal cord stimulation. The embodiment clusters the patient population into a plurality of cohorts. The embodiment generates a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts. The embodiment generates a plurality of actions using a second set of treatment data associated with the cohort. The embodiment determines, based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states. The embodiment generates, based on the plurality of probabilities, a stimulator action policy for a patient in the cohort.

An embodiment collects a first set of patient data and a first set of treatment data associated with a patient population treated with neuromodulation. The embodiment clusters the patient population into a plurality of cohorts. The embodiment generates a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts. The embodiment generates a plurality of actions using a second set of treatment data associated with the cohort. The embodiment determines, based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states. The embodiment generates, based on the plurality of probabilities, a neuromodulator action policy for a patient in the cohort.

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

Various embodiments of the present subject matter use health-related information to improve the monitoring and treatment provided to remotely managed patient populations by generating patient states to simplify the triaging of patient conditions. In an example, the system can monitor and analyze data, and can include identifying a first patient state from among a plurality of patient states, the plurality of patient states being defined by a state-based goal classification; identifying a second patient state from the plurality of patient states; capturing a change in patient state; and providing a monitoring platform to monitor the change in the patient state. In further examples, adaptive measures can be implemented to account for missing or withheld patient-related data used to generate patient states.

Disclosed herein are methods of using OX40 as a biomarker for predicting clinical sensitivity and therapeutic response of subjects having cancer to treatment with immunotherapeutic agents, and methods of using OX40 as a biomarker for selecting patient population for cancer treatment with immunotherapeutic agents. Disclosed herein are also methods of treating OX40-expressing cancers using immunotherapeutic agents. Further provided herein are kit for predicting the responsiveness of a subject having cancer to treatment with immunotherapeutic agents.

Various embodiments of the present subject matter use health-related information to improve the monitoring and treatment provided to remotely managed patient populations by generating patient states to simplify the triaging of patient conditions. In an example, the system can receive from a user via a user interface, criteria related to one or more goals to accomplish while receiving therapy to treat a condition. The system can translate, by at least one hardware processor, the criteria related to the one or more goals to a state-based classification defining a plurality of patient states defined by a plurality of parameters and receive a first set of parameter values. The system can further identify a first patient state from among the plurality of patient states using the first set of parameter values and generate an output including the first patient state.

Systems and methods for non-invasively measuring intracranial in accordance with embodiments of the invention are illustrated. One embodiment includes a method for estimating an intracranial pressure of a patient, including introducing a tracer into the blood of a patient, recording, using an imaging device, the traversal of the tracer through a brain of the patient as a kinetic curve, estimating a mean transit time (MTT) of the tracer through the brain of the patient based on the kinetic curve, measuring a mean arterial pressure (MAP) of the patient, calculating an intracranial pressure index as MAP−Ck / MTT, where C is a constant to scale proportional relationships based on healthy patient population characteristics, and k is a constant in pressure / time, and providing the intracranial pressure index. In various embodiments, regions of the brain unaffected by transient vascular abnormalities are identified and are used for data collection.

The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Various embodiments of the present subject matter use health-related information to improve the monitoring and treatment provided to remotely managed patient populations by generating patient states to simplify the triaging of patient conditions. In an example, the system can monitor and analyze data, and can include identifying a first patient state from among a plurality of patient states, the plurality of patient states being defined by a state-based goal classification; identifying a second patient state from the plurality of patient states; capturing a change in patient state; and providing a monitoring platform to monitor the change in the patient state. In further examples, adaptive measures can be implemented to account for missing or withheld patient-related data used to generate patient states.

The present application relates, inter alia, to anti-cMET antibody drug conjugates ("ADC") ("pH-ADC") having lower target-specific binding at lower pH relative to physiological pH; a pharmaceutical composition including the pH-ADC; a method for preparing the pH-ADC; methods of selecting a specific patient population for cancer treatment with an anti-cMET pH-ADC; the present invention relates to pH-ADCs, and methods of using the pH-ADCs in the treatment of cancer, including cMET + / cMET-expressing or overexpressing cancers, in particular non-small cell lung cancer ("NSCLC"), including cMET + / cMET-expressing or overexpressing cMET.

A method and apparatus for use as part of diagnostic analysis of the psychiatric or neurological health of a patient based on use of a normative database of fMRI data results and cognitive task results for a population of prior patients. Embodiments propose to acquire fMRI data of a particular set of one or more regions of the brain of a patient while the patient is performing a cognitive task, and to record at the same time results of the patient's performance in the cognitive task. A combination of both the cognitive task results and the fMRI data for the brain regions is used to provide an indicator as to a cognitive health status of the patient. More particularly, a normative database of fMRI data results and cognitive task results for a population of patients is used as a reference. This may store results both for healthy patients and pathological patients, to provide a normal and abnormal reference.

Disclosed is method and system for identifying treatment subpopulations within a patient population of a clinical study, by applying a causal ensemble model configured to output an ensemble Conditional Average Treatment Effect (eCATE).

Provided herein are methods and compositions for the treatment of ALS using chlorite or a pharmaceutical composition comprising sodium chlorite, for a target ALS patient population. Also provided herein are methods for identifying a target ALS patient population likely to be responsive to treatment with chlorite or a pharmaceutical composition comprising sodium chlorite, the methods including identifying ALS patients with elevated plasma C-reactive protein (CRP) levels and / or patients age 40-65, sporadic ALS pathology, or combinations thereof.