39 results about "Adverse outcomes" patented technology

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The present invention relates to immunoassays for detecting the whole endotrophin peptide. The assay can be used to detect and / or monitor a disease in a patient and / or assessing the likelihood of, or the severity of a disease in a patient. The disease may be selected from cardiovascular disease, idiopathic pulmonary fibrosis (IPF) or chronic kidney disease. The cardiovascular disease may in particular be heart failure, and especially heart failure with preserved ejection fraction. The immunoassay may be for assessing the likelihood of adverse outcomes of the cardiovascular disease, chronic kidney disease and all cause mortality.

A method of treating or preventing adverse outcomes associated with tissue plasminogen activator (tPA) administration, cerebral edema-related side effects, cerebral edema associated with radiation therapy, or migraine headaches by administering an effective amount of a SUR1-TRPM4 channel inhibitor, such as glyburide, and optionally the co-administration of a second therapeutically active agent, to a subject in need thereof. Adverse outcomes associated with tPA include cerebral hemorrhage, cerebral edema, physical impairment or death. The administration of the SUR1-TRPM4 channel inhibitors occurs prior to the radiation therapy, during the radiation therapy, after the radiation therapy, or combinations thereof. The SUR1-TRPM4 channel inhibitor is administered prior to surgical excision of a brain tumor, CAR-T therapy, or administration of flutarabine. Alternatively, or in addition, the SUR1-TRPM4 channel inhibitor is administered prior the onset of the cerebral edema-related side effects.

The present application relates to the medical technical field, specifically relates to a kind of early postoperative adverse outcome prediction method for craniopharyngioma patient, comprising: extracting preoperative data and label data of patient, store in database;The data in database is preprocessed, is randomly divided into training set and verification set according to preset proportion, and the training set is handled using the minority class oversampling technique, obtain the training set after processing;From the training set after processing, the most predictive value feature subset is identified and screened;Predictive model is constructed;The predictive model is evaluated and compared, and the best model is selected as the final deployment model;Receive new preoperative data of patient, input final deployment model, output prediction result.The present application fills the blank of existing prediction craniopharyngioma postoperative early overall adverse outcome model, overcomes the single data processing method in existing prediction technology, feature selection is not accurate enough, prediction accuracy is not high and lacks clinical usability and other problems.

A method of determining a therapeutic regimen in a patient with cancer comprising determining in a sample from the patient the tumor mutation burden (TMB) and loss of heterozygosity (LOH), wherein high TMB in combination with no LOH is indicative of a positive outcome when treated with a checkpoint inhibitor and high TMB with LOH is indicative of a poor outcome, is provided herein.

Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.

Predictive models are built for the estimation of adverse health likelihood by identifying candidate model risk variables, constructing a model form for an outcome likelihood model that estimates the likelihood of an adverse outcome type using a group of risk variables selected from the set of candidate model risk variables and by classifying each selected risk variable into either a baseline group or a dynamic group. Additionally, predictive models are built by constructing separate baseline and dynamic outcome likelihood model forms and by fitting the constructed model forms to a training data set to produce final models to be used as scoring functions that compute a baseline outcome likelihood and a dynamic outcome likelihood for patient data that is not represented in the training data set. The predictive models can be used with alerting and attribution algorithms to predict the likelihood of an adverse outcome for individuals receiving care.

The present invention discloses a method, system, device and storage medium for processing radiomics data of recurrent nasopharyngeal carcinoma. The radiomics data processing method comprises: obtaining MRI image data of patients with recurrent nasopharyngeal carcinoma; extracting image data for analysis, segmenting lesions, extracting image features, screening radiomics key features; inputting radiomics key features into the random forest model obtained through training to predict the risk of nasopharyngeal necrosis. The present invention provides accurate guidance for the prediction of the risk of nasopharyngeal necrosis and the formulation of treatment plans for patients with recurrent nasopharyngeal carcinoma, so that a part of patients with high risk of nasopharyngeal necrosis can avoid adverse outcomes after re-radiotherapy, and promote individualized and accurate diagnosis and treatment of recurrent nasopharyngeal carcinoma.

An Artificial Intelligence (AI) based computational system is used to non-invasively estimate the presence of a range of aneuploidies and mosaicism in an image of embryo prior to implantation. Aneuploidies and mosaicism with similar risks of adverse outcomes are grouped and training images are labelled with their group. Separate AI models are trained for each group using the same training dataset and the separate models are then combined, such as by using an Ensemble or Distillation approach to develop a model that can identify a wide range of aneuploidy and mosaicism risks. The AI model for a group is generated by training multiple models including binary models, hierarchical layered models and a multi-class model. In particular the hierarchical layered models are generated by assigning quality labels to images. At each layer the training set is partitioned in the best quality images and other images. The model at that layer is trained on the best quality images, and the other images are passed down to the next layer and the process repeated (so the remaining images are separated into next best quality images and other images). The final model can then be used to non-invasively identify aneuploidy and mosaicism and associated risk of adverse outcomes from an image of an embryo prior to implantation.

Method and systems for sorting potential hematopoietic stem cell transplant (HSCT) donors as a donor or non-donor using immunophenotyping of blood samples. The methods and systems can be used to identify a donor for a HSCT or to choose a donor to generate a HSCT blood product that is not likely to result in bad outcomes for a recipient. Also provided herein are methods and systems for training machine learning models that can be used in methods and systems for sorting potential HSCT donors.

FIELD: molecular biology.SUBSTANCE: method for predicting a high risk of adverse outcome in a patient with purulent lung disease (PLD) infected with gram-negative bacteria of the Klebsiella pneumoniae species is described. The method involves isolating DNA from peripheral venous blood and determining variants of the AQP4 rs1058424 gene. When the AQP4 rs1058424 AA genotype is detected in a patient with PLD and the presence of signs of Klebsiella pneumoniae infection, a high risk of death is determined.EFFECT: based on the determination of the AQP4 rs1058424 genotype for patients with PLD and identified gram-negative bacteria, it becomes possible to fairly early divide hospitalized patients with purulent infection into high-risk and low-risk groups for a fatal outcome.1 cl, 4 ex

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

The invention discloses six probiotics for preventing severe heatstroke and application of the six probiotics. A probiotic composition is prepared from bacillus coagulans XY2, lactobacillus plantarum KL100, lactobacillus rhamnosus TR08, lactobacillus helveticus TR05, lactobacillus casei TR01 and bifidobacterium longum TR17. The invention discloses novel application of the probiotic composition in prevention of severe heatstroke, heatstroke combined with liver injury, enhancement of intestinal mucosal barrier function, heatstroke combined with lung injury and heatstroke combined with myocardial injury. The sextuple probiotic preparation can effectively protect the state of an organism, enhance the resistance of the organism to high temperature, maintain the balance of a body temperature regulation mechanism, and prevent or relieve the phenomenon of severe heatstroke of the organism, and has the effects of preventing severe heatstroke and relieving heatstroke combined liver injury, lung injury and myocardial injury. Meanwhile, the six probiotic preparations can effectively enhance the intestinal mucosal barrier, and a new method and thought are provided for preventing the adverse outcome of extreme high temperature and complications thereof.

Targeting immune checkpoints, such as Programmed cell Death 1 (PD1), has improved survival in cancer patients by unleashing exhausted CD8+ T-cell thereby restoring anti-tumor immune responses. Most patients, however, relapse or are refractory to immune checkpoint blocking therapies. Here, the inventors show that NRP1 is recruited in the cytolytic synapse of PD1+CD8+ T-cells, interacts and enhances PD-1 activity. In mice, CD8+ T-cell specific deletion of Nrp1 improves spontaneous and anti PD1 antibody anti-tumor immune responses. Likewise, in human metastatic melanoma, the expression of NRP1 in tumor infiltrating CD8+ T-cells predicts poor outcome of patients treated with anti-PD1 (e.g. pembrolizumab). Finally, the combination of anti-NRP1 and anti-PD1 antibodies is synergistic in human, specifically in CD8+ T-cells anti-tumor response. Thus the therapeutic inhibition of NRP1 alone or combined with an immune checkpoint inhibitor (e.g. anti-PD1 antibody) could efficiently repress tumor growth in human cancer. The present invention also relates to multispecific antibodies comprising at least one binding site that specifically binds to an immune checkpoint molecule (e.g. PD-1), and at least one binding site that specifically binds to NRP-1. The present invention also relates to a population of cells engineered to express a chimeric antigen receptor (CAR) and wherein the expression of NRP-1 in said cells is repressed.

A method of determining a therapeutic regimen in a patient with cancer comprising determining in a sample from the patient the tumor mutation burden (TMB) and loss of heterozygosity (LOH), wherein high TMB in combination with no LOH is indicative of a positive outcome when treated with a checkpoint inhibitor and high TMB with LOH is indicative of a poor outcome, is provided herein.

A computer implemented method includes retrieving data from a patient's updated electronic medical record relevant to a diagnosis of a disease. The data includes medical images and at least one of the patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, and medical insurance information. The method includes generating available treatment path options including at least one surgical treatment or intervention, using at least one model to predict at least one implication for each of the available treatment path options based on the data and simulated adverse outcomes of the at least one surgical treatment or intervention simulated by predictive models including a biomechanical model based on the medical images. The method includes interactively updating and displaying a decision tree including the available treatment path options each with a corresponding at least one objective function.

The invention relates to the technical field of medical treatment, in particular to a postoperative early-stage bad result prediction method for a craniopharyngeal tubuloma patient, which comprises the following steps: extracting preoperative data and label data of the patient, and storing the data in a database; preprocessing the data in the database, randomly dividing the data into a training set and a verification set according to a preset proportion, and processing the training set by adopting a synthetic minority class oversampling technology to obtain a processed training set; identifying and screening a feature subset with the highest prediction value from the processed training set; constructing a prediction model; the prediction models are evaluated and compared, and the model with the best performance is selected as a final deployment model; and receiving preoperative data of a new patient, inputting the preoperative data into the final deployment model, and outputting a prediction result. According to the method, the blank of an existing model for predicting the early-stage overall bad result after the craniopharyngeal tubuloma operation is filled, and the problems that in the existing prediction technology, a data processing method is single, feature selection is not accurate enough, prediction precision is not high, clinical usability is lacked and the like are solved.

Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.

The invention relates to an application of lncRNAs Foxo6os in preparation of a medicine for detecting, preventing, relieving and / or treating heart failure. Compared with the prior art, the method has the advantages that the serum sample data of the patient at the early stage of heart failure is included, the exosomes are extracted from the serum in the queue for verification, auxiliary serological indexes are provided for the early diagnosis of the patient at the mental failure, and meanwhile, a novel and powerful biological detection target is provided for the early diagnosis of the heart failure. The result can assist clinicians in early accurate diagnosis and prevention of the heart failure phenotype and pertinently intervene the occurrence of bad outcomes, and also provides an important theoretical basis for the development of targeted drugs.

A method comprising: receiving or otherwise obtaining input data for a subject with suspected or confirmed pre-eclampsia, wherein the input data comprises data representing a plurality of input variables comprising clinical data variables and / or other input variables associated with the subject and / or healthcare setting, wherein the input variables are grouped into a plurality of variable data groups, wherein the method comprises: processing the input data to identify each of the plurality of variable data groups as one of: empty, partially complete or complete; performing a data completion process to complete the input data for the one or more partially complete variable group; combining the data of the complete variable group and the completed data of the partially complete variables groups to form combined data; performing a model selection process based on the received or otherwise obtained input data to select at least one model from a plurality of trained models; applying the selected model to at least some of the combined data to obtain at least a risk level and / or probability associated with one or more adverse maternal outcomes for a subject with suspected or confirmed pre-eclampsia.

Embodiments relate to addressing complications associated with performing multiple healthcare action items for a patient or a visitor of a medical station. Applications executed on the medical station generate initial healthcare action items using patient and diagnostic information. Complications arising from the initial healthcare action items are identified, and the initial healthcare action items are converted into revised healthcare action items that reduces the complications. The revised healthcare action items may reduce adverse outcomes compared to the initial healthcare action items, thereby improving the healthcare service provided by the medical station.

Embodiments relate to addressing complications associated with performing multiple healthcare action items for a patient or a visitor of a medical station. Applications executed on the medical station generate initial healthcare action items using patient and diagnostic information. Complications arising from the initial healthcare action items are identified, and the initial healthcare action items are converted into revised healthcare action items that reduces the complications. The revised healthcare action items may reduce adverse outcomes compared to the initial healthcare action items, thereby improving the healthcare service provided by the medical station.

The invention discloses a VTE risk intelligent dynamic assessment method and device based on a large language model, which can process medical record writing habits of different medical institutions, consider semantic understanding and logic inference, and support dynamic risk prediction in combination with low-cost conventional indexes, so as to simply and conveniently integrate into clinical work, reduce medical care burden and improve the risk assessment efficiency. The problems of VTE risk factor definition standardization, information extraction accuracy, user interface interaction friendliness and VTE event dynamic monitoring ability in the prior art are solved, effective help and support are practically provided for patient management, the patient is helped to recover early, and bad outcome is reduced. The method comprises the following steps: (1) calling and preprocessing data; (2) identifying and judging risk factors; (3) performing dynamic risk prediction and joint modeling; (4) triggering and early warning; (5) performing doctor checking and error correction feedback; and (6) performing individualized intervention suggestion and disposal recommendation.

Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.

The invention provides a harmful outcome path-based chemical reproductive toxicity transfer learning model prediction method, which comprises the following steps of: collecting test data of male and female reproductive toxicity of chemicals in different levels in a harmful outcome path, and preprocessing the test data; randomly splitting the preprocessed test data into a training set, a verification set and a test set, training a source model by using a deep neural network, and obtaining a target model after migration; evaluating the prediction performance of the target model by taking the area under the curve, the F1 score and the balance accuracy as indexes; representing an application domain of the target model according to the index similarity density and the weighted rugged property; and predicting whether the chemicals in the application domain have reproductive toxicity to the living body or not by using the target model. The transfer learning model fully fuses multi-level in-vitro test toxicity data of chemicals from molecular initial events to harmful outcomes, and can accurately predict in-vivo reproductive toxicity.

A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies / interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.

Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.

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.

The present invention relates to the technical field of anti-cancer drugs, and specifically relates to the application of intestinal microbiota in stabilizing intestinal glutamine and in the preparation of drugs for treating cancer. The large production of glutamine caused by environmental pollutant-induced intestinal microbiota dysbiosis is absorbed through the intestine and enters the body, promoting the development and adverse outcomes of breast cancer in the body. Based on a mouse model, this solution studied the patterns of environmental pollutant-induced intestinal flora dysbiosis, excessive glutamine production, and breast cancer development. Combining metagenomic and database analysis, beneficial microorganisms - Blautia ovata and Clostridium butyricum that can inhibit the progression of breast cancer were screened. The above probiotics can achieve anti-cancer activity by regulating glutamine production and can be used for the treatment of breast cancer. By orally supplementing Clostridium butyricum or Blautia ovata, the intestinal microecology induced by cadmium exposure can be improved and the glutamine content in the intestine can be regulated, thereby effectively antagonizing the occurrence and development of breast cancer, and having ideal application prospects and value.

The invention discloses a dangerous stratification model for hypertrophic cardiomyopathy and a preparation method and application thereof, and belongs to the technical field of biological medicine. The invention provides application of a reagent for detecting the expression level of PDCD5 protein. Researches find that expression increase of the serum PDCD5 protein is not only a risk factor of bad outcome of HCM patients, but also is independent of other clinical factors, and a survival curve of risk stratification according to the serum PDCD5 protein also has a good discrimination degree. The PDCD5 protein improves a risk stratification model of HCM established based on clinical factors. The non-synonymous variation of the VWCE, SPAG5, PDGFRB and PITX3 genes is independently related to the increase of the adverse outcome risk of HCM patients; on the basis of a score 2 established based on serum PDCD5 protein and clinical factors, the non-synonymous variation of the genes is combined, and the established multi-dimensional risk stratification model has good prediction efficiency on the adverse outcome of the HCM patient.