Medical Data Mining TRIZ Cases

This case uses wearable sensing, salient feature extraction, and database retrieval to support memory recovery and retention.

Pre-intervention forecasts and post-intervention comparisons identify independent donors for less biased synthetic control estimates.

Different feature subsets at each clustering tier improve binary and one-hot grouping while reducing orphaned data and redundant processing.

Analyze patient utterances, emotional states, and test details to predict interruption risk and support proactive care.

mRNA sequencing detects oncogenic pathogens without additional diagnostic assays.

Distributed patient feedback trains a model for customized spinal plans and real-time navigation, supporting precise implant placement.

Temporal sequence models select vaccine candidates by predicting molecular sequences and validating responses across pathogenic strains.

Modular cloud registries and privacy controls help AI process evolving COVID-19 data for diagnosis and personalized treatment.

A smartphone-based pupillometry case uses pupil frequency spectra to detect drug use and physiologic conditions objectively.

Functional connectivity and machine learning turn brain signals into portable, objective impairment measurements beyond breathalyzers.

A unified interface captures actions and sentiments together, reducing keystrokes and power use in health logging and coaching.

This case coordinates multiple AI models, aggregates their outputs, and presents unified medical imaging analysis.

A parent-child block-game dataset and 2sG-ALSTM model capture spatiotemporal motion for more accurate ASD screening.

Evaluation activities map brain functionality so audiovisual games can be tailored to stimulate selected regions and refine treatment.

A wearable ring, wristband, and dashboard combine multi-vital tracking, stored histories, and customizable alerts for remote care.

This case combines electronic health records and structured data to improve transition predictions and identify progression factors.

A smartphone otoscope and cloud AI combine ear images with symptoms to support accurate remote ear infection diagnosis.

Pretrained models identify response time points and relevant clinical datasets before extracting intervention outcomes from clinical text.

Model relationships among DNA, RNA, and protein patterns to distinguish disease samples from controls and predict outcomes.

Large, diverse cohorts and SNP filtering train population-specific PRS models, improving prediction consistency across ancestral groups.

Schema-based retrieval gives LLMs current clinical context for more precise differential diagnoses and treatment plans.

Targeted panels and tiered machine learning improve differential diagnosis, prognosis, and disease progression monitoring.

This case uses static and dynamic EHR data, predictive questions, and local training to reduce clinical AI compute demands.

Automated entity and assertion replacement expands annotated medical data, reducing manual annotation demands for clinical NLP training.

An estimation model identifies likely infections, then guides non-specialists to collect targeted missing patient information.

Electroosmotic pumping adapts drug delivery and limits flow blockages.

Historical meal records set individualized baselines and thresholds, helping detect unusual pet eating changes without manual tracking.

Stacked CGM models combine glucose history and user events to predict future levels and support proactive diabetes management.

Sensors assess measurement reliability and health trends to tailor alerts and coaching for seniors and people with chronic conditions.

A workstation extracts image deltas against standard sets before cloud AI analysis, reducing transfer time and patient data exposure.

Contact or contactless PPG analysis uses time and frequency domains to predict sleep-state transitions in real time with lower complexity.

This case compares gait during dual-tasking and normal walking to automate frequent cognitive health alerts.

A cognitive platform analyzes health artifacts and patient tone to steer personalized care plans while reducing manual EMR review.

A sensorized surgical end effector measures tissue force and displacement to identify tissue more accurately during robotic surgery.

Automated audio classification separates regular and irregular heart sounds, then refines murmur severity to support faster MMVD diagnosis.

This case uses automated medication-regimen analysis to stratify health risks, identify high-risk patients, and guide interventions.

Recursive Wasserstein learning captures clonality, ancestry, and subclone fitness to tailor treatment for high-risk tumor clones.

Biometric signals and stored posture data estimate calibration across positions, supporting accurate continuous blood pressure monitoring.

A trained classifier analyzes glucose, insulin, and meal data to detect missed boluses and support proactive diabetes management.

Sensor-based ECG feature extraction compares patient trends with cohort references to predict post-procedural cardiac dysfunction.

Head-mounted sensors analyze pupil size and movement against prior eye data, triggering timely notifications for possible concussions.

This interactive tool integrates medical-record and research data to compare patient trajectories with cohorts and refine personalized risk estimates.

Multi-dimensional image and report comparison helps an LLM create accurate, consistent interpretation documents from similar patients.

Routine blood and clinical measures feed machine learning models that detect disease risk early and track progression over time.

A seven-metabolite blood panel combined with AI and clinical data targets early lung cancer screening without biopsy.

A learned rating model ranks factor-parameter combinations, reducing exhaustive computation for prostate prognosis estimation.

This case uses wearable motion sensing, baseline sip calibration, and drink context to estimate hydration continuously.

A multi-axis sensor detects the imaged dentition area, narrowing tooth type and position candidates to improve accuracy.

Feature extraction simplifies voltage-time data for myocarditis detection.

Accelerometers, gyroscopes, and magnetometers infer voice activity without recording speech to track cognitive changes.