2 results about "Craniocerebral trauma" patented technology

This invention provides a method and system for establishing a prognostic model for traumatic brain injury (TBI) based on multimodal data fusion and AI, belonging to the field of medical artificial intelligence technology. The method includes collecting patient clinical text and CT image data, performing preprocessing, extracting clinical text features and image data features, and constructing three major models: a TBI prognostic prediction model, a clinical model of the predictive efficacy of edema expansion on TBI prognosis, and a TBI cerebral edema expansion prediction model. The final output of the prognostic model prediction results includes patient risk stratification results, neurological function prognostic prediction results, and cerebral edema expansion prediction results. The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a multimodal model construction module, and a result output module. This invention can accurately achieve risk stratification and prognostic prediction for TBI, effectively improving the accuracy and efficiency of diagnosis and treatment, reducing the risk of misdiagnosis and missed diagnosis, and providing strong support for improving patient prognosis.

The application discloses a kind of postoperative infection risk early warning system of craniocerebral trauma patient.The system uses "multimodal perception-privacy computing-interpretable early warning-closed loop intervention" four-layer integrated architecture, including multimodal perception and fusion module, privacy computing hub module, interpretable early warning engine and intelligent closed loop intervention module connected in turn.Communication connection is integrated millimeter wave radar, flexible sensor array and MS@SLET sensor chip in multimodal perception and fusion module, realize multidimensional data high-fidelity acquisition;Privacy computing hub module is based on improved FlexFair federated learning framework, and data privacy protection and group prediction fairness are considered;Interpretable early warning engine realizes accurate early warning and generates structured attribution report by the CNN-Transformer hybrid model of stage perception attention mechanism;Intelligent closed loop intervention module realizes hierarchical early warning, individualized scheme matching and model incremental optimization.The application solves the bottleneck of the prior art, such as insufficient timeliness of early warning, imbalance between data utilization and privacy fairness, uninterpretable decision and early warning intervention fragmentation, shortens the infection index detection time to within 30 seconds, the system response time is less than 200 ms, the infection-related mortality rate is reduced by 65.4%, fully meets the requirements of laws and regulations such as "Personal Information Protection Law", and has very high clinical application value.