Health-Index Calculation TRIZ Cases

Adaptive LASSO and npmtCBGPS reduce high-dimensional confounding and support less biased joint causal effect estimation for multiple exposures.

Machine-learned fusion of ultrasound and optical eye scans improves diagnostic confidence while reducing discomfort and exam complexity.

Continuous sensing extracts target persons and generates care assessment information, reducing manual monitoring effort in daily life.

A multiplex biomarker panel combines immune mediators and autoantibodies to forecast SLE activity and identify patients at risk of organ damage.

Aggregating multiple dental conditions on a 3D model pinpoints severity epicenters, helping clinicians find priority sites faster and explain findings clearly.

Approximate Entropy of skin temperature and heart rate enables individualized early heat strain warning before severe heat illness develops.

Passive data from phones, wearables, and social activity helps detect CBT progress continuously and adapt sessions to patient behavior.

Eigenvector-based template alignment and interpolation standardize wearable health data with mismatched formats and frequencies for better analysis.

Mendelian randomization links causal CpG methylation to aging traits, improving intervention assessment beyond correlation-based clocks.

Continuous sensor feedback and AI pain assessment adjust transdermal analgesic dosing to improve pain relief consistency and reduce side effects.

Sensors track lid and cell use, while light-based reward feedback improves medication adherence without adding complex user interaction.

Camera-based facial recognition measures cervical range of motion remotely, improving post-surgery monitoring without supervised visits.

Profiles tissue-specific cell-free RNA against reference transcriptomes to identify tissue origin and enable non-invasive health assessment.

Weighted hypoxemia scoring uses threshold events, AUC, and normalization to compare severity across contexts and predict health risks.

Multiple gene expression markers improve high-grade prostate cancer detection and help avoid unnecessary biopsies and overtreatment.

Blood-based circ-Magi1 detection enables earlier cerebral stroke diagnosis without CT radiation or MRI access limits.

A symbol-matching task with adaptive timing and changing rules turns subjective fatigue assessment into an objective cognitive measure.

Protein biomarker levels in blood enable earlier cerebral aneurysm detection, stage stratification, and inhibitor-guided treatment.

A clinical-importance scoring scheme hides non-essential physiological data so clinicians can read medical displays faster with less overload.

Global and localized ROH analysis is combined with polygenic and variant data to improve embryo risk scoring for consanguineous parents.

AI and LLM analysis of CAD incident data flags cumulative responder stress early, helping supervisors target mental health support.

Automatically switches between overview and subsystem avatar views when conditions are met, reducing manual display scanning in subject monitoring.

Clinician overload is reduced by ranking physiological parameters and showing only the most clinically important vital signs on the display.

Automatic 3D trunk muscle segmentation and weighted radiomics features predict risks in distant organs when target organ images are unavailable.

A six-gene-group expression index predicts breast cancer prognosis and chemotherapy response at lower cost across all age groups.

Reference wires near the catheter tip capture motion artifacts for subtraction, improving cardiac mapping signal accuracy during ablation.

Adaptive pre-wake scheduling uses alarm, wake-up history, and sleep state to capture transient blood pressure changes reliably.

Continuous comparison of expected and actual oximetry signals helps update cerebral autoregulation limits despite noise and patient-state changes.

Facial image analysis extracts PPG-based vital signs and health warnings without bulky sensors or professional interpretation.

Protein biomarker analysis enables earlier cerebral aneurysm detection and stage stratification than imaging alone, supporting targeted inhibitor treatment.

Optical profile slopes and decision trees turn breath or saliva spectrometer scans into rapid pathogen detection without reagent-heavy lab tests.

ML speech sequence models map voice records to physiological and emotional indicators with confidence levels and continuous patient updates.

Fusing nominal and real-time PPG signals lets a CNN+ANN model predict relative blood pressure and improve accuracy across individuals.

Adaptive sampling sends priority health data first, shortening sync waits so terminals can show health indicators sooner under unstable links.

Gene expression probe sets and PCA identify mCRC patients likely to benefit from bevacizumab, avoiding toxicity in non-responders.

Integrated ECG trends and prescription data help clinicians spot drug-linked arrhythmia risk earlier and intervene in heart failure care.

Patient behavior feedback is linked to AF burden patterns to identify likely triggers and suggest changes that help attenuate rising episodes.

A first-pass screen filters low-risk individuals, then intra-individual nucleic acid analysis improves rare condition detection accuracy.

Optical sensing of blood volume and oxygen saturation enables real-time, non-invasive estimation of tissue S-nitrosothiol levels.

Microsatellite repeat-length metrics and a trained classifier improve MSI detection from cell-free DNA when liquid biopsy signal is weak.

Segmenting sensing into pre-, in-, and post-exercise phases brings CPET-style cardiovascular assessment to everyday wearable training.

Thoracic voltage, ECG, and EEG features are fused with ANFIS to estimate cardiac output and inflammation without invasive catheter monitoring.

Dynamic risk scoring combines glucose level and trend to trigger earlier CGM alerts for impending hypo- and hyperglycemia.

Combining displacement data from lateral locations raises effective sampling for fast shear waves and improves velocity estimation in stiff tissue.

Video analysis detects accidents, classifies victim condition, and sends timely coping methods to speed initial emergency response.

Deep neural networks extract age and sex from short single-lead ECG recordings, reducing electrode count without sacrificing accuracy.

Policy-constrained AI reasoning adds explainability, multilingual support, and auditable privacy controls to clinical case management.

Controlled exhaled NO sensing plus vital signs improves sepsis risk assessment without invasive blood testing or unstable NO measurements.

Multiple sensor signals are fused through separate ML indicators to track cognitive capability more accurately and flag impairment in real time.

Time-aligned heart rate and motion data reveal how dental splint use changes sleep stages and grinding patterns through one comparison view.