Health-Index Calculation TRIZ Cases

Weighted glucose and pump data automatically refine ICR, correction factor, and split bolus timing to better match insulin needs and lower hypoglycemia risk.

Cross-recurrence analysis of breathing and locomotion signals detects subtle COPD worsening earlier than conventional classification.

Combined optical and ultrasound eye imaging aligns both scans into a hybrid image, improving diagnosis confidence while reducing exam discomfort.

A four-protein classifier improves short-term mortality prediction in cardiogenic and septic shock beyond basic tests and clinical risk scores.

Tracks primary sleep and daytime naps across 24 hours so wearable sleep and readiness scores reflect total sleep more accurately.

Behavioral signals from music and media use are converted into health scores and tailored content recommendations to track and modify user conditions.

A multivariate biomarker panel improves sensitivity and specificity for predicting MS disease activity and lesion progression.

Combining genotype markers, mitochondrial haplogroups, and machine learning improves neurodegenerative risk prediction for targeted intervention.

Wearable gait sensors and physical data estimate frailty and fall risk in daily life without motion capture complexity.

Two earbuds capture synchronized heart sounds and cross-check results to improve measurement reliability without clinical setup.

MFCC-based word and syllable segmentation improves correct-word counting and speech-rate assessment for cognitive and speech motor impairment.

Concurrent interval tracking links transmissions, docket reports, and service dates across care modalities to cut follow-up misses and billing errors.

Pre-injection risk prediction and live sensor feedback help detect contrast media adverse events and support safer imaging workflows.

Core temperature logs replace image-only skin analysis to improve estimation accuracy and support future condition prediction with tailored advice.

Time-frequency analysis of thoracic bio-impedance extracts richer signal features, improving non-invasive stroke volume and cardiac output estimation.

Algorithms convert implant vibration and patient-reported data into loosening and infection scores for more reliable recovery assessment.

Cardiac cycle analysis using short- and long-term heart rate averages helps detect sleep apnea with fewer false positives.

Real-time pharmacy AI flags overlapping prescriptions, dosage escalation, and refill anomalies while preserving auditable intervention records.

Blood-derived DNA methylation markers classify colorectal neoplasms by stage, reducing reliance on invasive biopsy and observer error.

A flexible solid-contact intraoral pH sensor replaces fragile glass electrodes to enable continuous, biocompatible monitoring and feedback.

Local ANN processing on a wearable NPU combines multimodal sensor data to predict cardiovascular events with lower power use and less data exposure.

Aggregated app, device, and plug measurements replace self-reports to track screen dependence accurately over time.

Near-infrared tissue sensing enables continuous hemoglobin tracking without blood draws, improving stability against physiological variation.

Passive room sensors and medical data detect anomalies during emergencies and nocturnal transitions to generate timely patient care instructions.

AI analysis of oral cavity images and symptom data improves remote strep diagnosis, reducing in-person visits and antibiotic overprescription.

By isolating high-frequency coupling sleep periods, this case uses heart rate trends to screen for suboptimal cardiovascular function.

Genetic profiling from cell-free fluids predicts treatment response and resistance evolution, helping clinicians choose personalized cancer therapy.

Near-infrared spectroscopy tracks brain water changes with polysomnography to measure glymphatic activity non-invasively in real time.

Short- and long-term heart rate averages define apnea-related cycles, reducing false positives in cardiac signal monitoring.

AI analyzes images, video, questionnaires, and wearable data to digitize capillary exposure for early remote varicose vein risk assessment.

Genetic profiling from cell-free fluids predicts treatment response and resistance early, helping personalize cancer therapy and survival planning.

Autoencoder analysis turns survey responses into state vectors, corrects missing values, and improves diagnosis precision without losing ease of use.

Voice-to-text, patient metadata, and an LLM turn clinical conversations into accurate SOAP reports with less manual documentation time.

Separate deep learning models process spectral and spatial eye imaging data to reduce subjective diagnosis time and improve reliability.

Severity indexes from user-entered symptoms help predict medical conditions remotely and route urgent and non-urgent cases appropriately.

A skin-worn transdermal alcohol sensor uses a microporous membrane and fuel cell sensing to deliver continuous, non-invasive BAC monitoring.

Wearable gait sensors and physical data are combined to estimate frailty and fall risk without complex motion capture equipment.

Footwear sensor data and user physical data are combined in an estimation model to assess frailty and fall risk during daily walking.

Nucleic acid probes label rare cell transcripts for FACS enrichment, enabling sensitive variant detection from scarce cells in maternal blood.

Deriving 2D or 3D T-wave loops from one or two cardiac signals tracks repolarization changes faster for earlier cardiac risk alerts.

CD34+ peripheral blood scRNA-seq replaces invasive biopsy by modeling HSPC metacells to detect marrow pathology and predict blasts.

A risk matrix links pathogen load, detection limits, and disease thresholds to deliver timely livestock disease alerts within the intervention window.

Sole temperature sensors use interpercentile range analysis to detect ulcers or pre-ulcers in a single foot with convenient self-monitoring.

Multi-gene mutation signatures classify lung cancer patients as CPI responders or resistant cases, improving treatment selection.

ECG-derived timing points clean electrical signals to isolate pulse waves for continuous cardiovascular monitoring with fewer motion artifacts.