Track 23: Big data on health and data mining
What is big data and data mining in healthcare?
Big data analysis uses data mining algorithms to discover patterns and correlations in large pre-existing databases. Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or datasets using advanced search techniques and algorithms.
As technology advances, the need for technology becomes increasingly important in all fields. The amount of data generated by the healthcare industry is becoming difficult to manage and analyze efficiently for future use. A massive amount of data is generated in the healthcare field, ranging from individual patient information to health history, clinical data, and genetic data. The analysis of patient data is becoming increasingly important in order to evaluate the patient’s medical condition and to prevent and take precautions in the future. Data can be analyzed more efficiently with the help of technology and computerized automation of machines. Many issues arise when dealing with massive amounts of data, including data security, data integrity, and inconsistency. Process mining and data mining techniques have opened up new avenues for disease diagnosis. Similarly, data mining can be used to provide effective treatment for a disease’s triennial prevention.
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Genomic Data:
- Advances in genomics have led to the generation of massive datasets related to individuals’ genetic makeup.
- Data mining can help identify genetic markers associated with diseases, enabling personalized medicine and targeted therapies.
- Medical Imaging:
- Medical imaging technologies generate large datasets, such as CT scans, MRIs, and X-rays.
- Data mining can assist in the analysis of medical images to identify patterns indicative of diseases, assist in early detection, and support treatment planning.
- Wearable Devices and Remote Monitoring:
- Wearable devices and sensors generate real-time health data, including heart rate, activity levels, and sleep patterns.
- Data mining can help analyze this continuous stream of information to detect trends, predict health issues, and provide personalized health recommendations.
Big data are data sets whose size, diversity, and complexity necessitate the development of new architecture, techniques, algorithms, and analytics to manage and extract value and hidden knowledge from them. When the size of data exceeds a critical point, quantitative issues in data capture, processing, storage, analysis, and visualization become qualitative issues. Although big data is frequently defined as the four Vs—volume, velocity, variety, and veracity—the definition of big data extends beyond the scope of data type characteristics such as size or volume. Along with the 4 Vs. of data, the potential to represent the real world almost without bias, to be linked with other datasets, to be useful and reused, to accumulate value over time, and to innovate a multidimensional, systems-level understanding should be considered. Despite the fact that big data has massive datasets, the information they provide may be unsatisfactory for what a specific researcher is looking for, and value creation, which cannot be expected with individual datasets, can be achieved through the potential of linking with other datasets.
What is special about medical big data?
The diversity of health-related ailments and their co-morbidities; the heterogeneity of treatments and outcomes; and the subtle complexities of study designs, analytical methods, and approaches for collecting, processing, and interpreting healthcare data all contribute to the complexity of healthcare. Administrative claim records, clinical registries, electronic health records, biometric data, patient-reported data, the internet, medical imaging, biomarker data, prospective cohort studies, and large clinical trials are all sources of medical big data. The integration of these data sources results in data with complementary dimensions such as large size (smaller than big data from other disciplines, but larger than clinical epidemiology data), disparate sources, multiple scales (seconds to years), inconsistencies, incompleteness, and complexity. There is no universal protocol for modelling, comparing, or benchmarking the performance of different data analysis strategies.
Sub-tracks of Big data on health and data mining
- Health history
- Research studies
- Lifestyle choices
- Pregnancy and births
- Life expectancy and deaths
- Environmental factors
- Health Psychology
- Medicine and Health
- Minority-centered Resources
- Women-centered Resources
- Behavioural interventions
- Communicable diseases
- Health and wellbeing
Benefits of Healthcare:
- Hospitalisation Benefits
- Ambulance Cover
- Domiciliary Treatment
- Cashless Claims
- Alternative Treatment
- Convalescence Benefit
- Expenses of Organ Transplant
- Dental Treatment
- Free Health Check-Up
- Increased Access to Health Services
- Early Management of Health Conditions
- Reduced Need for Specialist Care
Healthcare Associations:
- American Association of Medical Dosimetrists
- American Association of Medical Society Executives
- American Association for Physician Leadership
- American Association for Respiratory Care
- American Association of Critical-Care Nurses
- American Association of Healthcare Administrative Management
- American Association of Immunologists
- American Association of Occupational Health Nurses
- American Association of Oral and Maxillofacial Surgeons
- American Case Management Association
- American Health Information Management Association