7 results about "Comorbidity" patented technology

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.

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.

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.

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.

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.