Medical Data Mining TRIZ Cases

User-specific prompt templates and output evaluation help control LLM chatbots for safe, targeted chronic-care coaching.

Bio-potential sensor data is completed, corrected, and classified to support faster fetal heart monitoring decisions.

Multiple DNNs fuse ECG, EGM, imaging, and history data to support personalized ablation decisions and reduce recurrence.

AI standardizes diverse trial formats, selects metadata-matched visualizations, and supports compliant, iterative analysis.

Immunoassays measure TIMP1, HA, and PIIINP in blood to assess IPF presence, severity, predisposition, and progression.

A controller switches basal injection sequences while an electroosmotic pump alternates pressure for adaptable, accurate drug delivery.

This case aggregates clinical feedback across disparity factors to generate confidence scores for bias in AI clinical data products.

A joint longitudinal-survival model uses cumulative change rates and EM estimation to quantify biomarker effects on survival risk.

Electrodes compare body-segment impedance and extracellular-to-total-body-water ratios to detect worsening heart failure non-invasively.

A hybrid collaborative filtering model uses ICD codes and prior searches to recommend relevant EHR terms, reducing manual lookup effort.

Adjustable panels and a patient flowsheet reduce EMR navigation while linking clinical history, procedures, payments, and insurance rules.

This case integrates cell-specific embryonic transcriptomes, clinical traits, and mutation burden scores to classify MRKH syndrome.

A recurrent network and spacetime attention weigh longitudinal health-record features to capture temporal dependencies in disease prognosis.

Metadata-enriched text chunks route large datasets to specialized stores for accurate retrieval.

A 3-marker microbial and 5-marker non-microbial blood panel supports PDAC risk stratification with CA19-9.

NLP and machine learning organize entities from diverse sources into a knowledge graph for consistent, targeted biomedical queries.

A trained AI model converts 2D/3D echocardiography into CAD predictions, reducing reliance on invasive angiography and radiation.

Trained AI analyzes ECG and ABP waveform features across temporal windows to predict instability and issue early alerts.

Clinical feedback and disparity models generate multidimensional confidence scores to monitor bias across patient populations.

OCT and OCTA images generate personalized visual field priors, reducing iterative thresholding while supporting test accuracy.

This case combines ECG/HRV and hemodynamic monitoring with synchronized RF exposure and CEST-MRI for cancer diagnosis and treatment.

Selective meal-data retention removes normal-range meal information and preserves critical data for faster blood-sugar analysis.

A random-walk knowledge graph weights confirmed and unconfirmed symptoms plus examination results to improve automated disease prediction.

This case combines autonomic, neurologic, psychiatric, and endocrine indices to assess epilepsy and comorbidity states systematically.

Machine learning scores skin conditions by facial zone, maps severity, and overlays colors to guide personalized skincare recommendations.

A strange-feeling index compares AI predictions with expert judgment, improving alignment without reviewing every output manually.

A trained model analyzes ECG data to estimate pulmonary vein reconnection and support personalized repeat ablation recommendations.

PVC burden and morphology criteria trigger EGM storage or transmission, preserving useful analysis while reducing resource use.

This case uses concept links and action scores to generate ranked bioactive therapy hypotheses from scientific literature.

This sensing device detects eating events with motion sensors, reducing manual logging while supporting real-time behavioral feedback.

Pre-trained edit-space representations improve text similarity analysis for subtle language changes in Alzheimer’s monitoring.

Integrated LLM actions and content tools generate accurate clinical trial protocols from user inputs, reducing manual document preparation.

Active learning targets unlabeled medical samples for expert labeling during model upgrades.

Observational health data and changing duration thresholds reveal when medication benefits plateau or health risks rise.

This case uses brevican, neurocan, versican, and aggrecan epitopes to guide MHC-matched T cell and B cell responses against gliomas.

Historical prescriptions and diagnostic features are compared to explain alerts for anomalous radiotherapy prescriptions.

Gaussian processes blend population ATE data with patient data for precise CATE estimates.

Dynamic reference waveforms and similarity scoring distinguish deformed arrhythmia signals from noise during ECG analysis.

This case combines time-synchronized ECG and physiological signals with spatiotemporal analysis to improve non-invasive CAD diagnosis.

This case uses graphical and textual displays to clarify insulin infusion status and blood glucose data on a compact control interface.

Deep learning pre-annotates radiology findings for review, reducing manual actions and improving consistency across studies.

The monitoring system calculates scores at different frequencies and displays them over time, helping staff notice patient changes sooner.

An AI classification model selects anatomy-specific zoom levels and views, reducing setup time and missed pathology risk.

Short and long cardiac electrogram models combine rapid screening with accurate heart rhythm disorder detection.

A camera captures paper ECG images while computing models extract signal metrics for faster analysis without manual specialist review.

Temporal patch masking trains ECG models directly from textual signals, reducing memory use and processing demands.

Simulate heart activity to convert nonstandard ECG leads into reliable 12-lead data.

Replace unreliable manual ACL tests with imaging-based tibial bone density analysis for graded integrity assessment.

Eligibility rules turn uniform claims into nested care episodes, linking parent and sub-episodes for detailed patient-care analysis.

Statistical population analysis sets cached obfuscation parameters, reducing preprocessing for rapid quasi-identifier anonymization.