Health-Index Calculation TRIZ Cases

Time-aligned heart rate and motion data reveal how bruxism events map to sleep stages and help compare dental splint effects.

A mobile app combines self-surveillance, trend scoring, and culturally relevant support to flag postpartum maternal risk early.

Multiple biofluid biomarkers are combined in a classifier to improve early pancreatic cancer detection with strong sensitivity and specificity.

Camera-based AI scores pet postures for mental and physical health, helping owners interact and care without behavior expertise.

Non-invasive fNIRS tracks task-related cerebral hemodynamics to classify cognitive health states and improve early dementia screening.

Audio and motion data are combined with machine learning to track shortness of breath continuously and more objectively than intermittent evaluation.

Collective community testing integrates individual results, infection indexes, and notifications to speed quarantine decisions and limit disease spread.

Confidence-weighted heart rate metrics from multiple wearables are combined into one reliable value, reducing conflicting readings and user uncertainty.

Ranking-based weighting improves electrical apparatus health index accuracy and exposes failure points without relying on subjective expert weights.

Targeted sequencing of cancer-relevant methylation regions cuts WGBS cost and DNA damage while improving ctDNA-based risk and tissue-origin evaluation.

A normative white matter tract atlas enables automated glioblastoma survival prediction by measuring tumor-driven disconnection with diffusion NMR.

A Bayesian feature sufficiency approach identifies the minimal patient data needed to keep ML predictions accurate while cutting acquisition time and cost.

A device-aware ML model auto-prioritizes local network traffic by device type and usage context to reduce congestion and manual QoS tuning.

Machine learning risk profiles predict trial risk scores and focus monitoring on high-risk sites to cut SDV effort while preserving data integrity.

A deep neural network analyzes 12-lead ECG, age, and sex to predict future atrial fibrillation risk and improve early screening.

Three wrist PPG sensors, a thermopile, and ECG electrodes improve clinical-grade monitoring of NIBP, SpO2, HR, RR, and temperature.

Targeted analysis of PRP-related gene polymorphisms plus age helps predict patient response and support personalized musculoskeletal treatment.

Multi-band EEG and neural-network analysis improve anesthesia depth tracking speed and accuracy while revealing patient consciousness and emotional status.

A unified EHR GUI uses history tabs and alert footers to keep patient records synchronized while simplifying access for medical professionals.

An implanted transmitter stores a private key for secure EMR access, balancing blockchain data integrity with patient confidentiality.

Eye fundus video and motion magnification enable vital sign measurement during screening while avoiding mydriasis-related discomfort.

Automated self-screening stratifies large populations by symptoms and risk factors to assign access codes faster without sacrificing accuracy.

Bone marrow donor-recipient genotype and phenotype data validate autoimmune genetic models with more human-relevant evidence than animal studies.

Phased attention, associative, and integrative training on a user terminal improves cognitive function with automatic progression and feedback.

RNA sequencing and composite scoring identify circulating tumor cells with higher metastatic potential for more targeted breast cancer management.

Continuous biological signal monitoring generates risk sequences and timely alarms for earlier sepsis and mortality risk detection.

Indicator microbe analysis turns variable gut microbiome data into personalized food guidance that improves gut and metabolic health.

Combining cardiac and acceleration sensing, this case derives walk-based biomarkers that improve unsupervised functional health assessment.

Temporary-state detection adjusts abnormality criteria and reporting to cut false alarms from exercise or drinking.

Image-based pet screening uses annotated oral and skin photos plus pet data to detect subtle conditions early and return care recommendations.

Temporal X-ray vessel images reveal cross-sectional changes over the cardiac cycle to estimate mechanical properties without invasive imaging.

Preprocessed multimodal medical inputs help an LLM extract diagnoses and therapies accurately while reducing manual documentation time.

Combining ambulatory BP, EHR, and medication compliance data helps predict renal denervation response and avoid unnecessary procedures.

Combining cardiac and motion sensing, this case automates exertion biomarker measurement to improve walk-test reliability without trained observers.

Heartbeat interval statistics and ML risk scoring detect metabolic syndrome early without relying on users to notice or report mild symptoms.

By identifying muscle fiber orientation and selecting target planes, shear wave elastography gains more reliable stiffness measurements in anisotropic tissue.

A confidence-assessed blackbox plus interpretable model helps set medical device control parameters while reducing patient safety risks.

Closed-loop XR eyewear uses physiological trends and machine learning to auto-adjust therapeutic settings and reduce manual intervention.

Comet-shaped trend graphics show rate and direction of physiological change, helping clinicians scan multiple patient parameters quickly.

Backscattered high-frequency ultrasound reveals cortical pore size, thickness, and sound speed without radiation or complex CT hardware.

Optical fingertip sensing plus accelerometer feedback filters sleep movement artifacts for earlier apnea and arrhythmia detection.

Combines sequencing, sample metadata, and AI analysis to identify 30-50 microorganisms in under 24 hours for earlier animal health intervention.

Combining foot temperature, weight, and bioimpedance trends helps detect heart failure decompensation earlier and support timely intervention.

Hemodynamic features from arterial pressure waveforms enable earlier sepsis screening and prediction without waiting for full SIRS criteria.

Medical imaging and machine learning predict lung cancer recurrence and treatment response without invasive biopsy trauma.

Non-invasive CADx combines medical images, biomarkers, and intervention history to predict lung cancer recurrence and treatment response.

Real-time medicament-on-board assessment helps automated insulin delivery adapt faster to changing needs while reducing prolonged poor glucose control.

AI analyzes video and contextual signals to detect distress across multiple subjects, prioritize risk, and alert caregivers without delay.

Verified therapeutic code is embedded in game-like digital content to improve patient compliance while preserving validation in non-medical environments.

Multi-sensor wearable monitoring combines abdominal biopotential and biometric data with ML to improve fetal outcome prediction without invasive electrodes.