LCHI 478 Exam 2|Questions With Correct Answers|Verified
Volume - Big data involves massive amounts of information, often exceeding the
capacity of traditional data processing and storage systems. It can range from terabytes to
petabytes or even exabytes of data.
Velocity - Big data is generated at a high speed and in real time. This rapid influx of data
demands systems capable of handling continuous streams of information from various sources.
Variety - Big data comes in many forms, including structured (organized in databases),
semi-structured (having some organization, like XML or JSON files), and unstructured data
(lacking a defined structure, like social media posts or images).
Veracity - Big data often has inherent uncertainties and inconsistencies. Ensuring data
accuracy and reliability is crucial for extracting meaningful insights.
Value - The ultimate goal of big data is to derive valuable insights that can lead to
informed decision-making, process optimization, and innovation.
Social media data - Billions of posts, comments, and interactions generated daily.
Sensor data - From IoT devices, industrial equipment, and wearables.
Transactional data - From online and offline purchases, financial transactions, etc.
Scientific data - From research experiments, simulations, and observations.
Healthcare data - Electronic health records, genomic data, imaging data, etc.
,Electronic health records - Patient demographics, medical history, diagnoses, treatments,
medications, etc.
Clinical trials - Data from large-scale research studies on new drugs and treatments
Medical imaging - X-rays, CT scans, MRIs, etc., generating large image files
Wearable devices - Fitness trackers, smartwatches, etc., collecting data on patient
activity, heart rate, sleep patterns, etc.
genomic data - Sequencing of human DNA generates massive amounts of data
Social media - Patient discussions and sentiments about health conditions
Data fragmentation - Healthcare data is often siloed in different systems and formats,
making integration challenging
Data privacy and security - Strict regulations (HIPAA) govern patient data, requiring
robust security measures to protect sensitive information
Data quality and standardization - Inconsistent data formats and terminologies can
hinder data analysis and interpretation
Data storage and processing - Big data requires significant storage capacity and powerful
computing resources
Personalized medicine - Tailoring treatments based on individual patient characteristics
and genetic makeup
, Disease prediction and prevention - Identifying high-risk individuals and enabling early
interventions
Population Health Management - Analyzing large datasets to understand disease
patterns and improve public health
Drug discovery and development - Accelerating the identification and development of
new medications
Clinical decision support - Providing real-time insights to clinicians at the point of care
operational efficiency - Optimizing resource allocation and improving hospital workflows
Mayo clinic - Uses big data to predict patient outcomes and personalize treatments
Flatiron health - Oncology data platform that helps accelerate cancer research
Google deepmind health - AI-powered tools for diagnosing diseases and improving
patient care
Continued growth - The volume and complexity of healthcare data will keep increasing
Advanced analytics - Machine learning and AI will play a crucial role in extracting insights
Patient empowerment - Individuals will have more access and control over their health
data
Volume - Big data involves massive amounts of information, often exceeding the
capacity of traditional data processing and storage systems. It can range from terabytes to
petabytes or even exabytes of data.
Velocity - Big data is generated at a high speed and in real time. This rapid influx of data
demands systems capable of handling continuous streams of information from various sources.
Variety - Big data comes in many forms, including structured (organized in databases),
semi-structured (having some organization, like XML or JSON files), and unstructured data
(lacking a defined structure, like social media posts or images).
Veracity - Big data often has inherent uncertainties and inconsistencies. Ensuring data
accuracy and reliability is crucial for extracting meaningful insights.
Value - The ultimate goal of big data is to derive valuable insights that can lead to
informed decision-making, process optimization, and innovation.
Social media data - Billions of posts, comments, and interactions generated daily.
Sensor data - From IoT devices, industrial equipment, and wearables.
Transactional data - From online and offline purchases, financial transactions, etc.
Scientific data - From research experiments, simulations, and observations.
Healthcare data - Electronic health records, genomic data, imaging data, etc.
,Electronic health records - Patient demographics, medical history, diagnoses, treatments,
medications, etc.
Clinical trials - Data from large-scale research studies on new drugs and treatments
Medical imaging - X-rays, CT scans, MRIs, etc., generating large image files
Wearable devices - Fitness trackers, smartwatches, etc., collecting data on patient
activity, heart rate, sleep patterns, etc.
genomic data - Sequencing of human DNA generates massive amounts of data
Social media - Patient discussions and sentiments about health conditions
Data fragmentation - Healthcare data is often siloed in different systems and formats,
making integration challenging
Data privacy and security - Strict regulations (HIPAA) govern patient data, requiring
robust security measures to protect sensitive information
Data quality and standardization - Inconsistent data formats and terminologies can
hinder data analysis and interpretation
Data storage and processing - Big data requires significant storage capacity and powerful
computing resources
Personalized medicine - Tailoring treatments based on individual patient characteristics
and genetic makeup
, Disease prediction and prevention - Identifying high-risk individuals and enabling early
interventions
Population Health Management - Analyzing large datasets to understand disease
patterns and improve public health
Drug discovery and development - Accelerating the identification and development of
new medications
Clinical decision support - Providing real-time insights to clinicians at the point of care
operational efficiency - Optimizing resource allocation and improving hospital workflows
Mayo clinic - Uses big data to predict patient outcomes and personalize treatments
Flatiron health - Oncology data platform that helps accelerate cancer research
Google deepmind health - AI-powered tools for diagnosing diseases and improving
patient care
Continued growth - The volume and complexity of healthcare data will keep increasing
Advanced analytics - Machine learning and AI will play a crucial role in extracting insights
Patient empowerment - Individuals will have more access and control over their health
data
