From: Clinical data mining: challenges, opportunities, and recommendations for translational applications
Type | Clinical data mining questions | Case | PMID |
---|---|---|---|
Disease prevention | What are the risk factors associated with the development of the disease? | Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques | 37138014 |
 | Are there high-risk individuals who may benefit from preventive interventions or early screening? | A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease | 37443589 |
 | How do lifestyle and environmental factors influence the likelihood of developing the disease? | The Contribution of Genetic Risk and Lifestyle Factors in the Development of Adult-Onset Inflammatory Bowel Disease: A Prospective Cohort Study | 36695739 |
Disease diagnosis | What are the diagnostic markers or features that are most relevant for accurate disease identification? | Neutrophil-, Monocyte- and Platelet-to-Lymphocyte Ratios, and Absolute Lymphocyte Count for Diagnosis of Malignant Soft-tissue Tumors | 37351995 |
 | How can data-driven approaches be utilized to improve the accuracy of diagnostic tests or imaging techniques? | A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images | 36327654 |
 | Does data mining have the ability to differentiate between different subtypes or stages of the disease? | Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients | 35935976 |
Disease treatment | Which treatments or therapies are most effective for specific patient subgroups or disease stages? | Darolutamide Plus Androgen-deprivation Therapy and Docetaxel in Metastatic Hormone-Sensitive Prostate Cancer by Disease Volume and Risk Subgroups in the Phase III ARASENS Trial | 36795843 |
 | Can data mining be used to optimize treatment plans and personalize medicine based on individual patient characteristics? | Clinical Outcomes With and Without Plasma Exchange in the Treatment of Rapidly Progressive Interstitial Lung Disease Associated With Idiopathic Inflammatory Myopathy | 36729874 |
 | How do we predict treatment response and potential adverse reactions to specific medications? | Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma | 36414800 |
Disease prognosis | What are the key prognostic factors influencing disease outcomes and patient survival rates? | Construction and Validation of a UPR-Associated Gene Prognostic Model for Head and Neck Squamous Cell Carcinoma | 35707371 |
 | Can data mining assist in predicting disease progression and potential complications? | An inflammatory-related genes signature based model for prognosis prediction in breast cancer | 37304237 |
 | How can predictive analytics help to identify patients who are more likely to experience a recurrence or relapse of their disease? | Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests | 36934429 |
Population health | How does data mining contribute to public health initiatives and disease surveillance efforts? | Perceived Impact of Digital Health Maturity on Patient Experience, Population Health, Health Care Costs, and Provider Experience: Mixed Methods Case Study | 37463008 |
 | What patterns and trends emerge when looking at the occurrence and spread of disease across different populations or geographic regions? | Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress | 36698423 |