21 results about "Comorbidity" patented technology

The invention relates to the technical field of medical health and artificial intelligence, in particular to an old people common disease occurrence and development risk prediction method based on integrated machine learning, which comprises the steps of constructing a standardized data set, screening key variables, training a base learner, combining prediction results, dynamically evaluating risks and the like. According to the method, multi-dimensional data features are integrated, a prediction model is constructed by using algorithms such as a random forest and a support vector machine, model parameters are optimized in combination with a verification set, and a high-precision co-disease risk prediction result is finally output. According to the invention, accurate assessment of the co-illness risk of the old people can be realized, and a scientific basis is provided for personalized health management.

The invention relates to the technical field of health management of old people, and discloses a method and a system for predicting multiple diseases of old people. The method comprises the following steps: acquiring multi-modal health monitoring data of a target old person in a preset time period, wherein the multi-modal health monitoring data comprises physiological index time sequence data, a medication record sequence and a daily activity ability evaluation result; and performing cross-modal correlation analysis on the multi-modal health monitoring data to generate a disease interaction characteristic matrix containing metabolic disease correlation degree, circulatory system disease coordination index and neurodegenerative disease progress rate. And performing hierarchical clustering processing on the feature matrix by adopting a dynamic weight distribution algorithm, and outputting potential common disease combinations of the target old people and a priority score of each common disease combination. And generating a personalized intervention strategy set including a drug interaction avoidance scheme, a rehabilitation training intensity adjustment scheme and a nutrition intake ratio scheme according to the priority score.

The invention discloses a critical patient risk prediction method based on big data, and the method comprises the following steps: S1, carrying out the butt joint of a hospital multi-department electronic medical record system, organizing medical information personnel and department experts to formulate a unified data interface standard, and regularly capturing data through an interface; s2, noise data are removed through data mining recognition, a disease association map is constructed and trained through a graph neural network framework, and recessive common disease association is mined; s3, aligning a recessive common disease association time sequence by using a time sequence causal inference model, inputting common disease feature training, identifying and eliminating false association, and retaining a true causal relationship; s4, when the difference between the true causal and the actual causal exceeds a set threshold value, triggering reverse updating of the model, and adjusting a corresponding common disease association weight value in the graph neural network according to a result; therefore, early warning and personalized intervention suggestions can be pushed in real time, medical workers are assisted in timely intervention, and the severe illness treatment efficiency is improved.

A computer-implemented system includes one or more processing devices configured to receive comorbidity information that includes a plurality of comorbidities or comorbidity-related conditions associated with a user, generate a selected set of the comorbidity information, determine, based on the selected set of the comorbidity information, respective probabilities of a plurality of different outcomes related to the comorbidity information, and generate, based on the respective probabilities and the selected set of the comorbidity information, a treatment plan comprising one or more exercises directed to changing the respective probabilities. A treatment apparatus is configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

A new computational approach may provide improved detection of disease conditions and comorbidities, such as PTSD, Parkinson's, Alzheimer's, depression, etc. For example, in an embodiment, a computer-implemented method for detecting a disease condition may comprise receiving a plurality of data streams, each data stream representing a measurement of a brain activity comprising physical and chemical phenomena and performing pattern analysis on the plurality of data streams to detect at least one fundamental code unit of a brain code corresponding to a disease condition based on a combination of the plurality of data streams.

A new computational approach may provide improved detection of disease conditions and comorbidities, such as PTSD, Parkinson's, Alzheimer's, depression, etc. For example, in an embodiment, a computer-implemented method for detecting a disease condition may comprise receiving a plurality of data streams, each data stream representing a measurement of a brain activity comprising physical and chemical phenomena and performing pattern analysis on the plurality of data streams to detect at least one fundamental code unit of a brain code corresponding to a disease condition based on a combination of the plurality of data streams.

The invention relates to a remote electrocardio telemetering method and system, a terminal and a medium, and belongs to the technical field of electrocardio monitoring. The remote electrocardio telemetering method comprises the steps that a cloud end distributes monitoring equipment and initial parameters according to patient information; the edge end receives real-time electrocardiogram data, obtains environment data in combination with the position of a patient, collects physiological data through a wearable sensor, constructs a spatio-temporal context sensing map, fuses the data to generate situation enhanced electrocardiogram signal representation, inputs a comprehensive risk prediction model to obtain a dynamically updated prediction risk value and uploads the prediction risk value; the cloud judges the predicted risk value, if the predicted risk value is larger than a threshold value, a matched disease type is called, and differential prompts are generated and pushed to the patient and the doctor in combination with the co-disease relation network; if the electrocardiogram data does not exceed the threshold value, carrying out deep analysis on the electrocardiogram data, identifying abnormity, determining an abnormity level, generating a personalized report in combination with treatment history, pushing the personalized report, and triggering a clinical response protocol. The method has the beneficial effect of feeding back the abnormal electrocardiogram condition of the patient in time.

This application provides a method, apparatus, and computer device for joint risk assessment of chronic diseases and comorbidities, relating to the field of medical and health technology. The method includes: performing hierarchical preprocessing on health examination-related data from multiple subjects to generate a standardized dataset containing single-disease labels and comorbidity combination labels; inputting the target data into a single-framework multi-objective collaborative ensemble model to obtain single-disease and comorbidity prediction values; the single-disease and comorbidity prediction values ​​are obtained synchronously through a single inference by the single-framework multi-objective collaborative ensemble model; calculating a comorbidity risk gain value based on the single-disease and comorbidity prediction values, and generating a structured assessment result based on the comorbidity risk gain value. This method can improve the accuracy of joint risk assessment of chronic diseases and comorbidities.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

This invention relates to the field of medical data-driven dose prediction technology, and discloses a method, system, device, and storage medium for individualized dose prediction of hypothyroidism based on medical data. The method includes: constructing a standardized feature vector based on the child's weight, age in days, corrected age in months, current L-T4 dose, TSH value, FT4 value, previous TSH value, TSH rate of change, feeding method, month of consultation, etiology of hypothyroidism, comorbidity status, previous dose adjustment magnitude, and age at which TSH first reached target levels; extracting TSH dynamic trajectory features from the child's TSH time-series data from previous follow-ups; obtaining a basic recommended dose using a gradient boosting decision tree model constructed with counterfactual filtering training data; and correcting the basic recommended dose to obtain an individualized recommended dose. This method improves the prediction accuracy of the gradient boosting decision tree model and allows the individualized recommended dose to simultaneously take into account multiple clinical confounding factors.

This invention discloses a method for identifying comorbidity relationships in ophthalmology based on zero-model correction and adaptive node selection, belonging to the fields of medical big data processing, real-world electronic medical record analysis, disease comorbidity relationship mining, and graph data mining. The method includes: acquiring diagnostic data from ophthalmology patients' electronic medical records and performing diagnostic cleaning, coding standardization, and patient-level disease set construction; constructing a patient-disease bipartite graph based on the correspondence between patients and diseases, and statistically analyzing real disease co-occurrence relationships; while maintaining the distribution of the number of patients with diseases and the distribution of disease incidence frequency, randomly reconnecting the patient-disease bipartite graph to generate multiple zero-model background networks, and calculating the background correction advantage score of disease pairs relative to the random background; obtaining the initial association strength by combining the number of co-occurring individuals, relative risk, lift, and significance test results of disease pairs; calculating the adaptive node selection threshold based on the incidence frequency of disease nodes, the number of candidate edges, the distribution of candidate edge weights, and the zero-model advantage distribution; further calculating the importance score of the two-end nodes and the resampling stability score of disease pairs, fusing them to obtain a comprehensive credibility score for disease pairs; selecting candidate comorbidity relationships and classifying their credibility levels based on the comprehensive credibility score, and outputting the disease pairs, scoring results, credibility levels, and reasons for removal. This invention can reduce the impact of high-frequency diseases, differences in the number of patient diagnoses, and random co-occurrence on the results of ophthalmic comorbidity identification, avoid the excessive background connections of high-frequency diseases and the accidental deletion of low-frequency specific relationships caused by traditional uniform thresholds, and improve the reliability, stability, and interpretability of ophthalmic comorbidity identification.

The invention discloses a gastrointestinal tumor patient postoperative weakness risk correlation analysis method and system fused with a co-disease map, and belongs to the field of medical care informatics, and the method comprises the following steps: obtaining clinical pathological characteristics, co-disease assessment, serum biomarkers and social and psychological assessment scale data of gastrointestinal tumor patients; obtaining statistical effect values of homocysteine and health attainment, and executing isomorphic mapping; identifying a chained intermediary conduction path and generating a coupling strength factor, and performing weighted correction on the initialized weight matrix; inputting the multi-source data as a risk source excitation signal into the atlas, executing correction calculation, and outputting a risk prediction score; a maximum weight subgraph search is performed to generate risk attribution analysis data. According to the method, the multi-dimensional heterogeneous association map is constructed to fuse the multi-source data of the patient and the medical priori knowledge, and the risk conduction is quantified through the path topology analysis and the matrix correction operation, so that the accurate prediction of the postoperative weakness risk and the interpretable attribution of the key pathogenic path can be realized.

The invention discloses a cerebral apoplexy early-stage clinical outcome prediction method, system, device, medium and product, and relates to the field of clinical outcome prediction.The method comprises the steps that an electronic medical record of an old ischemic cerebral apoplexy patient is collected, and medical features and medical feature values are extracted; according to the discharge diagnosis information in the electronic medical record, the medical history and disease diagnosis information of the senile cerebral arterial thrombosis patient are extracted, the number of common diseases is calculated, and different common disease modes are constructed; evaluating according to the physical examination condition of the old ischemic stroke patient during discharge, determining an mRS score, and determining a post-stroke clinical outcome dichotomy result according to the mRS score; constructing a machine learning model according to the medical characteristic value, the common disease mode and the post-stroke clinical outcome dichotomy result; the early clinical outcome result of the cerebral apoplexy of the patient is predicted according to the machine learning model, a personalized early treatment strategy is formulated based on each medical feature, and the clinical outcome result can be accurately predicted in an early stage.

A method for constructing a comorbidity prediction model is provided. The method includes receiving a sample dataset, filtering and analyzing the dataset, and using harmonic centrality and betweenness centrality to identify critical core diseases and bridge diseases, thereby establishing a comorbidity prediction model.

This document relates to methods and materials for assessing and / or treating obese mammals (e.g., obese humans). For example, methods and materials for using one or more interventions (e.g., one or more pharmacological interventions) to treat obesity and / or obesity-related comorbidities in a mammal (e.g., a human) identified as being likely to respond to a particular intervention (e.g., a pharmacological intervention) are provided.

The invention belongs to the field of disease diagnosis, and discloses application of AQP1 as a depression-premature ovarian failure co-disease marker. PPI analysis shows that the AQP1 is located at a high connection center and is enriched in functional modules such as water channel activity, transmembrane transport, cell membrane structure and the like. GO enrichment analysis further shows that AQP1 mainly participates in water transport and transmembrane signal regulation and is closely related to the hypothalamic-pituitary-ovary (HPO) axis and brain tissue homeostasis. AQP1 expression is in significant negative correlation with POF, has no multi-effect and heterogeneity interference, and supports the potential causal effect of the POF. The AQP1 not only shows differential expression in the depression-ovarian function premature senility co-disease, but also shows dynamic change consistent with pathological severity in an MISS model simulating the depression-ovarian function premature senility co-disease state, and the AQP1 protein is related to the depression-ovarian function premature senility co-disease. By detecting the expression condition of the AQP1 protein of a subject, the depression-premature ovarian failure co-disease condition can be well determined, and an objective biomarker is provided for MDD diagnosis.

The invention relates to the field of medical information processing, in particular to a stroke home rehabilitation guidance method and system based on multi-dimensional classification. The method comprises the following steps: acquiring basic conditions of a stroke patient, and classifying disease characteristics, neurological function defect parts, severity and complications to form a classification result; executing patient group mapping based on a classification result, and establishing corresponding key monitoring related indexes; change collection and change analysis are carried out according to key monitoring related indexes, and stage rehabilitation scheme suggestions are generated; and carrying out abnormal judgment and online guidance according to the stage rehabilitation scheme suggestions, forming adjustment suggestions, and writing back the adjustment suggestions. According to the invention, the classification result is used as an organization basis throughout the whole process, so that a consistent closed-loop processing link is formed by monitoring, analysis, guidance and write-back, and the continuity, traceability and collaborative stability of the scheme generation and adjustment process in the stroke home rehabilitation guidance process are improved.

Methods, systems, and apparatus for assessing a state of an epilepsy disease or a comorbidity thereof are provided. The methods comprise receiving at least one autonomic index, neurologic index, stress marker index, psychiatric index, endocrine index, adverse effect of therapy index, physical fitness index, or quality of life index of a patient; comparing the at least one index to at least one reference value; and assessing a state of an epilepsy disease or a body system of the patient based on the comparison. A computer readable program storage device encoded with instructions that, when executed by a computer, perform the method described above is also provided. A medical device system capable of implementing the method described above is also provided.

Embodiments are described herein for automated tracking of disease progression with personalization for different cohorts or clusters of individuals and predictions of future progression with different treatment regimens. A dataset may be received related to individuals with a medical condition and a comorbidity. Training data may be extracted from the dataset for training a supervised learning model to classify individuals into a class of comorbidity progression. Correlations and / or the recommendations for treatment may be determined for different clusters or cohorts of individuals using an unsupervised learning model. Recommendations for treatment of the medical condition and / or the comorbidity may be based on the cluster or cohort with which an individual is associated.

The invention provides a health management method for common diseases of mental diseases. The health management method comprises the following steps: acquiring multi-source health data related to the mental diseases; based on the multi-source health data, extracting a first association relationship between diseases, constructing a modular network taking the diseases as first nodes and taking the first association relationship as a first connection edge, extracting a second association relationship between the diseases and influence factors, constructing a modular network taking the diseases and the influence factors as second nodes and taking the diseases and the influence factors as second connection edges; a second association relationship is used as a second connection edge of the two-mode network; based on a first-mode network and the second-mode network, determining a typical common-disease mode of common diseases of mental diseases through a community discovery algorithm, wherein the typical common-disease mode comprises at least one mental disease; and generating a health management strategy corresponding to the typical common disease mode based on the typical common disease mode in combination with a dynamic burden quantitative model and real scene modeling. According to the method, the problem of high implementation difficulty in the aspect of management strategy landing in the existing mental disorder common disease management is solved.

The invention belongs to the technical field of animal experiment model construction, and particularly relates to a construction method of a hypothyroidism and depression combined mouse model. Comprising the following steps: giving drinking water containing methimazole to a mouse, detecting the serum TSH level every two weeks, carrying out synchronous behavioral evaluation, and determining a modeling end point according to continuous increase of TSH and stable occurrence of depression behaviors so as to obtain the mouse model with hypothyroidism and depression. Through three innovations of methimazole free drinking water administration, double-week TSH-behavioral dynamic monitoring and eight-week multi-index end point verification, a mouse model with thyroid degeneration and depression combined with thyroid degeneration and depression, which can be operated in a standardized manner and has traceable pathology, is established for the first time, and the problems of tissue damage, complicated operation, rough monitoring and model deficiency in the prior art are solved; and an irreplaceable platform tool is provided for co-disease research and drug development.