117 results about "AdverseEvent" patented technology

A method for assessing and analyzing one or more drugs, adverse effects and associated risks, and patient characteristics resulting from the use of at least drug of interest is disclosed. The method comprises the steps of selecting one or more cases for analysis, said cases describing the behavior between at least one drug of interest and a patient genotype; profiling statistically derived values from multiple cases related to the safety of the at least one drug, wherein at least one filter is employed for deriving said values; at least one data mining engine; and an output device for displaying the analytic results from the data mining engine. A system for performing the method is likewise disclosed.

Biomarkers are described for predicting the efficacy, risk of relapse, risk of an immune related adverse event (irAE), or combination thereof for a CTLA-4 blockade treatment, such as ipilimumab, in a subject with melanoma. Biomarkers are also described for predicting the efficacy and clinical benefit for a PD-1 blockade treatment, such as a PD-1 blocking antibody, in a subject with melanoma.

The invention discloses application of a product for detecting ceramide in preparation of a kit. The application of the kit is at least one of the following (a1)-(a3): (a1) screening or assisting in diagnosing heart failure patients; (a2) evaluating or assisting in evaluating the prognosis risk of heart failure patients; and (a3) evaluating or assisting in evaluating the adverse event risk of heart failure patients. Research results show that the ratio of plasma ceramide cer (d18: 1/16: 0) to cer (d18: 1/24: 0) is an important predictive factor for the occurrence of adverse events of heart failure in patients with heart failure at the final stage, and is independent of currently used lipid markers, which facilitates the identification of high risk patients requiring more positive therapeutic interventions.

The invention provides a biodegradable bracket with multiple drugs, belongs to the field of medical equipment and relates to a biodegradable bracket with a single coating. Besides support function, the bracket further comprises the single coating which contains anti-proliferative drugs, anti-inflammation and anti-immune drugs, drugs for promoting endothelial cells to grow and a developing agent. The bracket main body is made of biodegradable polymers of which mechanical strength is high. After implanted on a pathological position, the bracket has strong support force, so that action of supporting the pathological vessels in short time can be achieved. Because of the developing agent, doctors can accurately obverse the position and the expansion consistency of the bracket during operation, and displacement or other adverse events can be prevented; the anti-proliferative drugs can prevent the vessels from becoming narrow in short time after the bracket is implanted; and the drugs for promoting the endothelial cells to grow can promote the endothelial cells to grow on the surface of the bracket and can cover the bracket in the endothelium of the vessels, so that embolism phenomenon caused by degradation fragments of the bracket dropping in blood can be prevented.

The present invention is directed to methods and compositions for determining the presence or absence of polymorphisms within an ABCC2, UGT1A1, and/or SLCO1B1 gene and correlating these polymorphisms with activity levels of their gene products and making evaluations regarding the effect on their substrates, particularly those substrates that are drugs. In addition, there are methods and compositions of evaluating the risk of an individual for developing toxicity or adverse event(s) to an ABCC2, UGT1A1, and/or SLCO1B1 substrate. In some embodiments, the invention concerns methods and compositions for determining the presence or absence of ABCC2 3972C>T variant and predicting or anticipating the level of activity of ABCC2 and determining dosages of an ABCC2 drug substrate, such as irinotecan, in a patient. Such methods and compositions can be used to evaluate whether irinotecan-based therapy, or therapy involving other ABCC2 substrates, may pose toxicity problems if given to a particular patient or predicting their efficacy. Alterations in suggested therapy may ensue based on genotyping results.

A method, a system, and a computer program product are provided. A machine learning model is generated to process adverse event information and produce multiple corresponding medical codes associated with the adverse event information, wherein the multiple medical codes are semantically and hierarchically related in a medical taxonomy. The machine learning model includes multiple parallel output layers, each of which is associated with a corresponding medical code. The machine learning model is trained with training data elements, each of which includes adverse event information mapped to respective multiple medical codes, wherein results from each of the output layers adjusts the machine learning model. After completing the training, information pertaining to an adverse event is applied to the machine learning model to determine the corresponding multiple medical codes within the medical taxonomy.

Disclosed are methods of inducing formation of a liver organoid from precursor cells, such as iPSC cells. The disclosed liver organoids may be used for screening for a serious adverse event (SAE), such as liver failure and/or drug induced liver injury (DILI), and/or drug toxicity. The disclosed liver organoids may also be used to treat an individual having liver damage, or for identifying a preferred therapeutic agent.

Methods for inducing anti-phosphorylated Tau antibodies without inducing a severe adverse event in humans are described. The methods include administering to the subject an effective amount of liposomes including a toll-like receptor 4 agonist and a Tau phosphopeptide presented on the surface of the liposome.

For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, andoutcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, includingdrug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring,etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

A method, a system, and a computer program product are provided. A training set of adverse event text fragments assigned to medical codes is analyzed to determine first text fragments having frequently occurring medical code assignments and second text fragments having infrequently occurring medical code assignments. The training set is modified to undersample the first text fragments and to oversample the second text fragments such that the text fragments of the modified training set correspond to a substantially uniform assignment of the medical codes. At least one machine learning model is generated and trained with the modified training set. Some parameters of the at least one machine learning model are updated based on errors detected during the training. After completing the training, an adverse event text fragment is applied to the at least one machine learning model to assign at least one medical code.

The invention discloses an early warning method for pressure sore adverse events, and the method comprises the steps: firstly obtaining early warning indexes of the pressure sore adverse events, and constructing an early warning index system of the pressure sore adverse events (especially performing feature information mining on clinical real pressure sore adverse events by using a hybrid researchmethod combining semi-structure interviews, association rules and Delphin Method, and constructing the early warning index system of the pressure sore adverse events); secondly, performing feature extraction on the constructed early warning index system of the pressure sore adverse events(especially performing feature extraction based on an ultra-short text vector feature extraction method of a random up-down sampling text convolutional neural network RS-CNN); and finally, establishing a prediction model by using an algorithm of a support vector machine, and outputting an early warning result. The early warning method for the adverse pressure sore events is objective, effective and practical.

The invention discloses a hospital adverse event management system, relates to the technical field of medical information, and overcomes the problems and defects existing in the traditional hospital adverse event reporting process. The system specifically comprises a party end, a department responsible end, a quality management office end, a related functional department processing end and an archive management end; the party end declares an adverse event, the adverse event is approved by the department responsible end and the quality control office end and sent to the related functional department processing end for processing, the department responsible end and the quality control office end are approved again after processing, and the file management end files the event; and the adverse event declaration content comprises an event occurrence date, result of whether the event encompasses a patient, an event occurrence place and/or an event error type. The hospital adverse event management system provided by the invention standardizes the reporting process of the adverse events, and strictly controls the quality and efficiency of processing the adverse events and the gateway for analyzing whether the adverse events are in place or not.