Medical Data Mining TRIZ Cases

Electrical nerve stimulation and muscle-response sensing give surgeons real-time guidance to avoid under- or over-decompression.

Automatically links diagnosis and treatment records to relevant examination results and highlights matching items on one screen for easier review.

Masked overlapping ECG temporal patches enable self-supervised pretraining on unlabeled signals while avoiding image conversion overhead.

Unstructured biomedical standards are converted with OCR, NLP, and knowledge fusion into machine-readable data elements for cross-dataset sharing.

Graph-based ML complements missing treatment data to widen the eligible patient pool and improve clinical trial candidate selection.

Interval-based CPR and ventilation summaries flag out-of-range TBI patient metrics, helping rescuers improve feedback quality without constant monitoring.

Unstructured clinical notes are turned into deployable EHR predictions, improving readmission and mortality forecasting in real workflows.

3D posture and time-series motion analysis replaces manual scoring to deliver remote, objective musculoskeletal assessment at lower cost.

A matrix view organizes patient data by information type and acquisition time, helping clinicians review complete records faster without missing key details.

Wireless reflections capture heartbeat and breathing features to classify stress without wearables, even when users move freely.

Generative AI screens cross-disciplinary compound data to find synergistic natural therapy combinations with higher efficacy and lower toxicity.

Patient-specific efficacy models are deformed from generic treatment functions to optimize control variables with limited patient data.

Role-based access and secure updates help professionals act on incapacity and end-of-life plans without exposing unnecessary confidential data.

Optical tissue sensing replaces sEMG skin electrodes to reduce motion artifacts and decode muscle movement, position, and force.

Maps one CGM target to corresponding metric targets using PCA and classification to support consistent glycemic control in insulin delivery.

Flexible eyelid heating strips enable blink-compatible meibomian gland therapy while AI tracking supports prognosis and personalized treatment.

AI analyzes wearable and EHR data to predict disease risk early and trigger alarms, reducing delays, errors, and missed interventions.

Synthetic patient data preserves real data statistics to power in silico clinical studies, improve privacy, and reduce study cost.

Classifier and ROC-based CGM target mapping links one glycemic metric to others, helping insulin delivery balance trade-offs in control.

Context-driven sensor and algorithm selection improves respiration rate accuracy when posture, activity, and ambient noise affect signal quality.

A tagged data exchange unifies medical, behavioral, and social records to visualize SDoH risks without duplicative intake.

Predicting eGFR decline helps coordinate CKD interventions earlier, improving treatment timing, care continuity, and resource efficiency.

Self-supervised ECG pre-training uses unlabeled signals to cut manual labeling time and cost while improving classification accuracy.

Shared embeddings link ECG signals with EHR context, enabling self-supervised pretraining that improves diagnostic accuracy and efficiency.

Modular XR modules combine AI, avatars, and real-time data feedback to personalize healthcare training, diagnosis, therapy, and care delivery.

Clustering gravity-center movement from furniture sensors improves low back pain prediction for users with frequently changing sitting postures.

Hash-based de-identification links genomic and claims data into analyzable datasets for more accurate treatment history and survival analysis.

Age-matched normalized ocular measurements are combined in a refractogram to improve centile accuracy and track myopia progression over time.

Long physiological recordings are split into shorter labeled windows to improve ground truth quality and support accurate continuous human state monitoring.

Outlier removal and hard-easy sample grouping cut labeling effort and speed medical image AI model adaptation without hurting accuracy.

Probing potential modulation extracts calibration parameters from CGM sensor currents to improve glucose accuracy, cut warm-up time, and limit drift.

Multiple wearable sensors compare the same physiological parameter across body locations to improve continuous monitoring and early differential diagnosis.

Deep learning compares pre-operative and live surgical images to identify anatomy accurately and help robotic systems avoid collisions.

Calculates safe sun exposure time from skin pigment, family history, and SPF, then sends alerts when UV exposure reaches the limit.

Flux-interval plots, III, and cluster RMSE help PPG signals distinguish AFib from other arrhythmias with fewer false positives.

Adaptive alert thresholds and caregiver feedback help AI patient monitoring cut false alerts while improving trust and care coordination.

A polygenic score built from 11,000+ variants improves type 2 diabetes and hyperglycemia risk stratification for earlier, targeted screening.

An AI layer bridges legacy RIS and CRM data to generate physician-tailored radiology summaries, reducing errors and staff workload.

Patch-based AI aggregates features from 3D tissue volumes to improve clinical endpoint prediction without manual annotation.

Biodynamical coronary motion data and machine learning cluster similar patients to predict treatment options with greater precision and efficiency.

Long physiological recordings are segmented into short labeled windows to improve continuous human state monitoring accuracy and response time.

ML feature clustering and run-length encoding cut robotic procedure video bandwidth while protecting sensitive medical images.

Stacked ML models combine CGM history with meal, exercise, and insulin event signals to improve glucose forecasts and support timely alerts.

Combining small variant, whole genome, and methylation cfDNA features improves early cancer detection sensitivity and specificity.

Real-time contrast tracking in cath lab imaging compares cumulative use with a patient-specific maximum to support safer procedure planning.

Electrophysiology data from multiple brain paths is scored against proven tissue activation volumes to guide DBS probe placement beyond STN anatomy.

Patient identifiers are stripped from modular surgical data so hubs can coordinate energy, fluid, and procedure records without added OR complexity.

Unstructured clinical notes are parsed into AD-related indicator phrases, improving risk prediction by combining NLP-derived phenotypes with structured EHR data.

Machine learning uses limited emergency department parameters to detect sepsis onset earlier and improve ICU versus ED patient stratification.

Blood cfDNA methylation analysis uses machine-learned Alzheimer indicator genes to improve diagnosis without invasive lumbar puncture or imaging.