Health-Index Calculation TRIZ Cases

Selective activation of health monitoring devices cuts redundant data, battery drain, storage use, and privacy risk while preserving timely assessment.

Continuous glucose data is converted into a glycemic variability index and patient status alerts to improve timely detection of hypo- and hyperglycemia.

Combining cardiac and body temperature measures helps detect out-of-range glucose states while reducing spurious alerts.

Continuous dynamic ECG monitoring and deep learning predict atrial fibrillation risk earlier, enabling timely alarms and intervention.

Neural-network ECG triage identifies myocardial infarction risk and routes emergency patients to PCI-capable hospitals without delay.

Nasal airflow, breath duration, and variability are analyzed to indicate Parkinson's disease and track progression during treatment.

Hair and nail selenium, manganese, and calcium ratios feed a discriminant model that classifies long-term exposure risk before diagnosis.

Proximity-based monitoring flags outdated physiological alarm thresholds, helping reduce false alarms without missing true patient deterioration.

Tracking blood glucose difference rates helps detect meal and exercise events automatically, enabling closed-loop infusion adjustment.

A central monitoring array combines patient data from separate record systems to trigger earlier disease risk alerts across care sites.

Automated lesion detection, measurement, and machine-learning analysis cut ultrasound diagnosis time while improving objectivity for breast lesions.

Pre-signals let medical devices coordinate alarm timing and sound properties, reducing ICU noise while keeping critical alarms distinct.

A BERT-based NLP pipeline structures tumor size, dimensions, and anatomical location from radiology reports for accurate EHR integration.

Segmented ECG filtering removes noisy wearable data before analysis, improving arrhythmia detection and cardiac metrics accuracy.

Segmented respiratory signals and local thresholds detect apnea events and estimate AHI with less sensor burden and fewer sleep-lab constraints.

Object tracking, DSP, and ML guide stethoscope placement and verify signals so patients can capture reliable remote exam data.

Automated blood pressure capture and personalized hypertension alerts reduce manual input, speed tracking, and conserve battery power.

Site-specific glycoprotein analysis with mass spectrometry and machine learning improves early cancer and NASH detection while lowering invasiveness and false positives.

A two-stage screening workflow filters low-risk individuals, then uses baseline-corrected nucleic acid analysis to improve rare condition detection.

Reliability scoring gates CGM treatment recommendations, helping prevent unsafe insulin advice when input data is incomplete or uncertain.

Recorded patient actions are clipped into standardized video and audio segments to speed neurological scoring and support timely stroke care.

Frequency-based HRV analysis captures irregular pulse patterns for real-time atrial fibrillation and glucose regulation monitoring.

Correlates EEG signals with taps, swipes, gestures, and sounds on a portable device without synchronized clocks for event-based neuronal analysis.

Sequencing SNP patterns in cell-free DNA separates donor and recipient signals for non-invasive transplant monitoring and therapy adjustment.

Combined ambulatory BP, EHR, and medication adherence data help predict renal denervation response before treatment.

Serum biomarker detection and algorithmic scoring replace invasive ileocolonoscopy for serial Crohn's mucosal healing assessment.

Multimodal sensors track heart rate, breathing, movement, and facial cues to detect patient panic during imaging and alert staff in time.

Movement sensors and ML models flag early cattle health issues, improving detection speed while avoiding overly complex monitoring.

Selected food panels and gender-specific IgG thresholds improve depression sensitivity testing by reducing false positives and negatives.

A bistable insulin pump display holds status screens with minimal power while using color and guided menus to keep therapy changes visible.

Combining speech retelling with hand-eye tasks simplifies cognitive assessment, reduces subject resistance, and improves screening efficiency.

Physiological and interaction data are matched to task demands to detect crew overload, adapt task lists, and trigger alerts or automation.

Short overlapping and non-overlapping observation windows extract feature values that reveal biological state differences with less sampling time.

Serverless spirometry processing links devices, mobile apps, and cloud alerts to detect pulmonary changes early while protecting patient privacy.

By analyzing high-frequency QRS reference points and amplitude changes, this case improves non-invasive detection of myocardial ischemia.

Bayesian peak validation filters motion-corrupted PPG waveform peaks using predetermined sensor data to improve physiological assessment accuracy.

Telemetry-based NIV monitoring uses statistical indicators and machine learning to score ventilation quality and flag patients needing attention.

Video analysis detects accidents, assesses injured persons, and sends coping guidance quickly to support immediate first response.