Health-Index Calculation TRIZ Cases

Multi-parameter cancer cell assays quantify metastatic behavior to predict spread risk and identify drugs that inhibit metastasis.

Quantifying deep vessel density changes in OCTA images enables earlier, more objective glaucoma progression prediction than conventional OCT.

EMG and gyroscopic sensing with machine learning enables accessible early detection of unstable movement patterns for routine self-assessment.

EEG-derived sleep metrics such as REM duration are used to estimate seizure risk earlier and more robustly across different patient populations.

Continuous cloud monitoring flags early interstitial lung disease signs and alerts clinicians in real time for faster symptom management.

Combining mental and physical questionnaires with hormonal measurements enables more reliable stress modeling over time and reduces single-source bias.

Digital twin patient models flag anomalous prescription ordering, misuse, and diversion while improving monitoring accuracy and treatment decisions.

Concurrent interval tracking links cardiac transmissions to care modalities, improving follow-up timing and date-of-service accuracy.

Real-time physiologic and environmental data drive protocol selection and caregiver instructions to speed trauma intervention in chaotic settings.

AI analyzes virtual home visit data and health status to score fall risk, guide home modifications, and support safer discharge.

A wearable ECG with LII and LIII leads uses AI to estimate ejection fraction, heart failure, and sleep apnea without costly imaging.

A targeted tumor gene expression biomarker stratifies early-stage melanoma prognosis to guide PD-1 antagonist treatment and avoid unnecessary exposure.

Iterative rule-in and rule-out population segregation improves PPV, NPV, and detection for low-prevalence risk prediction with less computation.

Paired saliva and dental plaque sequencing improves early ASD risk classification and supports personalized microbial cocktail design.

A limited set of immune cell measurements plus dynamical systems analysis estimates immune age and predicts illness and mortality risk.

Specific blood amino acid markers replace complex genetic testing to assess dementia risk more accurately and support prevention planning.

Combining NEFL, MOG, CXCL9, and related markers improves sensitivity and specificity for subtle MS activity prediction.

Scalable microservices combine echocardiogram text, medical history, and visit data to score heart disease severity and flag urgent patients.

Integrated ECG metrics and prescription data reveal drug-linked rhythm trends, helping clinicians adjust heart failure treatment earlier.

Blood biomarker analysis with aged-rat thrombectomy models and machine learning predicts infarct, edema, and intervention needs after ischemic stroke.

Real-time dashboard monitoring aggregates disparate healthcare system data to detect workflow bottlenecks early and trigger corrective actions.

Autonomous glucose control modifies unsafe manual insulin instructions, sends emergency doses, and builds backup therapy from tracked delivery history.

3D facial tracking on a personal device measures cervical motion at home, enabling unsupervised monitoring and remote recovery follow-up.

Family-history analysis in EHRs flags probable inherited disease during registration, helping clinicians confirm diagnoses earlier.

A two-level CNV and gender learning model improves cancer type prediction accuracy while avoiding many separate gender-specific models.

A GUI marks EGJ location and artifact events in EndoFLIP diameter maps to compute usable distensibility and maximum diameter data.

Disinfection data is used to predict infection degree and gate vehicle sharing, helping prevent disease spread while maintaining service availability.

A transductive learning approach tailors wearable gait inference models to each user, improving accuracy without subject-specific labeled data.

sTM, VCAM-1, and PaO2/FIO2 are combined to identify pediatric septic shock patients at high risk of persistent SA ARD.

Deidentified vitals from mapped devices are matched to donor records by time and location to reduce entry errors in blood donation screening.

Audio signal prediction replaces complex inhaler sensors to assess technique, track adherence, and monitor dosing status accurately.

Defined sound reflectors let an implanted cardiac support sensor calculate blood sound speed, improving Doppler flow measurement accuracy.

Combining fibrosis-linked polymorphisms with obesity and platelet-rich plasma data improves risk prediction for preventive treatment planning.

Dynamic sensor sampling uses uncertainty estimates and phase-locked loops to cut wearable power use while preserving biometric signal accuracy.

Metatranscriptomic taxon clustering and functional module profiling reduce microbiome artifacts and improve dental caries prediction.

Urine gDNA methylation and mutation testing improves early urothelial carcinoma detection and recurrence monitoring without invasive cystoscopy.

Hydroxymethylation profiling enriches rare 5hmC signals in cell-free DNA to assign tissue of origin with higher specificity.

Portable DSC with disposable sensors and remote evaluation expands access to detailed biological sample analysis while cutting wait times.

Continuous glucose data and fasting-period detection are used to personalize insulin titration, easing dose decisions and reducing hypoglycemia risk.

Genetic search and clustering identify interpretable feature sets that predict phenotypic traits without exhaustive real-patient testing.

Trinucleotide-context error grouping and hypothesis testing improve MRD sequencing calls by separating true mutations from sample-specific errors.

Multi-sensor physiologic signals feed a risk model that predicts future atrial arrhythmia and adjusts monitoring intensity to cut false alarms.

Multiple sensor inputs are converted into a shared 2D evaluation image, enabling more accurate subject assessment across locations and sensor types.

Selective IL-23 antibody dosing improves psoriatic arthritis symptoms while preserving cytokine-specific disease targeting and safety.

Body-worn sensors and cloud analysis detect stroke-related limb changes early, enabling faster alerts and emergency response.

Behavior clustering with activity and sleep features improves early prediction of depression recurrence and worsening for timely intervention.

A 2D EndoFLIP GUI marks EGJ location, filling events, and catheter artifacts to isolate usable data for distensibility and diameter analysis.

Real-time spinal sensing uses user-defined recalibration and haptic alerts to maintain accurate posture feedback across exercises.

Circadian rhythm-derived features help wearable sleep staging classifiers improve accuracy without relying only on conventional detection.

Automated checks of response speed, emotion recognition, and pronunciation improve learning evaluation in portable digital therapy.