US-20260123894-A1 - Multimodal Predicate Diagnostic System for Hospital Stroke Detection

Abstract

A multimodal predicate diagnostic system for hospital stroke detection utilizes TEE-secured mobile devices to monitor patients in clinical environments. The system aggregates motion data using optimized sampling and cryptographic validation, applying a hybrid CNN-decision tree model optimized via transfer learning and data augmentation against an adaptive gait baseline. It computes risk confidence scores in real-time and generates alerts with hospital network integration.

Inventors

• George William Bickerstaff, III

Assignees

• George William Bickerstaff, III

Dates

Publication Date

20260507

Application Date

20260105

Claims (10)

1. 1 . A computer-implemented system for predicate diagnostic detection of stroke and TIAs in hospital settings using TEE-secured mobile devices, comprising: a sensor data aggregator configured to collect and preprocess multimodal motion data with optimized sampling from a mobile sensor array, utilizing cryptographic software; a predicate analysis engine configured to apply predicate rules via a hybrid CNN-decision tree model and an adaptive gait baseline while cross-referencing environmental precision data; a stroke risk classifier configured to compute a cryptographically validated risk confidence score in less than one second; and an alert module configured to generate a real-time alert with less than two-second latency transmitted via a TEE-secured channel with hospital network integration.
2. 2 . A method for predicate diagnostic detection of stroke and TIAs in hospital settings using TEE-secured devices, comprising: collecting multimodal motion data and preprocessing with optimized sampling triggers; applying predicate rules using a hybrid machine learning model based on an adaptive gait baseline; computing a validated risk confidence score in less than one second; and generating an alert with a clinical location tag.
3. 3 . A non-transitory computer-readable medium storing instructions that, when executed by a processor within a TEE, cause the processor to: collect motion data from a hospital-based array; apply predicate rules via a hybrid CNN-decision tree model and adaptive gait baseline; compute a risk confidence score; and generate a real-time alert with hospital team routing data.
4. 4 . The system of claim 1 , wherein the predicate analysis engine refines predicate rules using a machine learning model initialized via transfer learning and fine-tuned on an optimized dataset of stroke patterns utilizing generative data augmentation to maintain 95% sensitivity.
5. 5 . The system of claim 1 , wherein the alert module integrates with hospital connectivity protocols to automatically log risk scores in an electronic health record.
6. 6 . The system of claim 1 , wherein the sensor data aggregator utilizes specialized clinical hardware integration to ensure continuous monitoring during patient transport.
7. 7 . The system of claim 1 , wherein the cryptographic software utilizes AES-256 and SHA-3 hashing.
8. 8 . The method of claim 2 , further comprising implementing a feedback loop to refine the adaptive gait baseline using real-time clinician-verified data.
9. 9 . The medium of claim 3 , wherein the instructions validate device synchronization using a protocol with less than 10 ms delay.
10. 10 . The system of claim 1 , wherein the alert module routes alerts to specific medical teams based on integrated hospital department routing logic.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS None. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT Not applicable. INCORPORATION BY REFERENCE None. FIELD OF THE INVENTION The present invention relates to mobile health monitoring and cryptographic diagnostic systems within clinical environments. Specifically, it relates to a computer-implemented predicate diagnostic framework that analyzes multimodal ambulatory data from Trusted Execution Environment (TEE)-secured devices using adaptive gait baselines for hospital-based stroke and TIA detection. BACKGROUND OF THE INVENTION Stroke and transient ischemic attacks (TIAs) require constant, high-precision monitoring in hospital wards to prevent secondary events and ensure rapid response. Traditional diagnostics often rely on intermittent nurse observations or stationary equipment. Existing mobile solutions lack the integrated cryptographic security and real-time clinical system connectivity required to safely monitor ambulatory patients within a high-density hospital network environment. SUMMARY OF THE INVENTION The invention provides a predicate diagnostic system for detecting stroke and TIAs in hospital settings using TEE-secured devices. The system comprises a sensor data aggregator optimized for clinical hardware, a predicate analysis engine with an adaptive gait baseline, a stroke risk classifier, and an alert module. By utilizing hospital connectivity protocols, hardware-level encryption, and an AI architecture optimized via transfer learning and data augmentation, the system identifies physiological deviations while maintaining strict HIPAA-compliant confidentiality. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1: SYSTEM ARCHITECTURE FIG 1A: SENSOR DATA AGGREGATOR CONFIGURATION—Illustrates the TEE-secured setup for collecting multimodal hospital data via clinical hardware. The configuration manages high-fidelity sensor inputs while balancing hospital network bandwidth. Hardware-level isolation is maintained throughout the aggregation process to protect patient privacy.FIG. 1B: PREDICATE ANALYSIS ENGINE FLOW—Depicts the workflow for applying predicate rules using a hybrid CNN-decision tree model optimized for high-density hospital data. The engine processes multimodal inputs in real-time to identify acute clinical anomalies. Logic paths are detailed for differentiating between standard patient movement and acute stroke indicators.FIG. 1C: STROKE RISK CLASSIFIER ALGORITHM—Shows the algorithm computing cryptographically validated risk scores in less than one second. The diagram highlights the interaction between the adaptive gait baseline and real-time sensor streams. Security protocols are embedded within the classifier to ensure risk data integrity.FIG. 1D: ALERT MODULE INTEGRATION—Displays Integration With hospital local area networks utilizing less than two-second latency. The module manages the prioritized delivery of alerts to specific medical response teams. Network routing logic ensures notifications reach appropriate clinical terminals without interruption.FIG. 1E: DEVICE SYNCHRONIZATION PROTOCOL—Describes the low-latency protocol for syncing hospital device data across multiple clinician terminals. The protocol maintains data continuity as patients move between clinical departments. Synchronization occurs within the secure environment to prevent interception on the local area network. FIG. 2: DATA PROCESSING WORKFLOW FIG. 2A: MOTION DATA COLLECTION PIPELINE—Outlines the pipeline for gathering multimodal hospital data with optimized sampling triggers. The pipeline includes specific stages for noise reduction and cryptographic signing. Real-time sensor fusion occurs here to prepare data for higher-level predicate analysis.FIG. 2B: PREDICATE RULE APPLICATION LOGIC—Details the logic for evaluating stroke/TIA thresholds for patients in clinical settings. The logic incorporates environmental precision data to handle hospital-specific movement variables. The system provides actionable data to clinicians without making autonomous medical decisions.FIG. 2C: RISK SCORING COMPUTATION—Illustrates the computation of cryptographically validated risk confidence scores. The calculation integrates patient-specific medical history with current physiological movement data. Results are formatted for immediate inclusion in digital medical records.FIG. 2D: ALERT GENERATION SEQUENCE—Shows the Sequence of steps to generate and route alerts to medical teams. The sequence initiates immediately upon a risk score exceeding the predefined threshold. Automated logs are created to track the response time and clinical outcome of each alert.FIG. 2E: DATA VALIDATION CHECKPOINT—Depicts Validation utilizing adaptive baselines and cryptographic signatures. This ensures movement data is correctly attributed to specific patients in crowded wards. Validation steps are performed within the TEE to maintain high-level security. FIG. 3: USER INTERFACE DESIGN FIG. 3A: REAL-TIME MONITORING