Research On Key Techniques For Relationship Mining In Complex Medical Data Based On Temporal Networks

Healthcare big data,as an important component of Digital China,has risen to a national strategic height.Medical data contains rich medical relationship information,including disease and symptom,disease and disease,and disease and treatment.Effectively utilizing these relationships is of great significance for improving individual disease control,assisting doctors’clinical decisions,optimizing the service quality of medical institutions,and supporting national policy-making.It is an important way to realize the application of healthcare big data.However,relationships within medical big data are very complex,exhibiting characteristics such as obscurity,dispersion,and diversity.Existing methods for relationship mining face challenges in achieving global recognition and discovery,which hinders further utilization of these relationships.To address the challenges of complex and obscure relationships within medical big data,a complex medical data relationship mining technology system based on temporal network is proposed.This system solves the challenges of difficult fusion of multi-source heterogeneous medical information,difficult extraction of personalized patient characteristics,difficult modeling of refined health information,and insufficient transparency in health risk inference:(1)This method uses prior knowledge matching and conditional probability calculations to identify hidden correlations between features.By designing correlation constraint rules,it constructs a global temporal heterogeneous health information network to achieve global fusion of multi-source heterogeneous information.(2)To address the challenge of unclear personalized relationships and difficult extraction of personalized features within medical big data,a global collaborative representation learning method based on individual correlation recognition is proposed.This method uses a dual-channel temporal network and a global similarity measure to respectively learn personalized health traj ectory information and calculate patient similarity.By allowing information exchange between similar patients during the training process,the goal of global collaboration is achieved.(3)To address the challenge of diverse relationships and difficult modeling of fine-grained patient health records within medical big data,a cross-scale sequence modeling method based on event correlation recognition is proposed.This method uses a cross-scale temporal health network to parallel process medical event sequences with different time spans.It models the diverse relationships between high-dimensional event sequences in a fine-grained manner to enhance the accuracy of medical applications.(4)To address the problem of low model interpretability caused by difficult-to-explain relationships,a trusted reasoning method based on logical correlation recognition is proposed.This method designs a logic rule-aware network guided by temporal encoding to discretize input information into binary vectors.It learns the logical correlation between input and output through cyclic disjunctive and conjunctive operations,resulting in more transparent and interpretable disease progression reasoning.The proposed method was experimentally validated on the publicly available medical data set,MIMIC,through clinical prediction applications,parameter analysis,and module ablation experiments,which verified the effectiveness of the proposed method from multiple perspectives.The multi-source heterogeneous information fusion technology and refined modeling technology were deployed on the National Health and Medical Big Data Science and Technology Innovation Application Platform,which enabled the construction of the first provincial-level population life and health atlas.This platform supports large-scale applications such as medical big data fusion,individual health portrait analysis,and medical cohort studies.