Role of Big Data in Medical Diagnostics
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Transcript of Role of Big Data in Medical Diagnostics
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ROLE OF BIG DATA IN MEDICAL DIAGNOSTICS
ELQ 301 PRESENTATIONNISHANT AGARWAL 2014EE10464
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INDEX What is Big data in healthcare? Need for Big Data Analytics Big Data in Medical Diagnostics of Heart Diseases Process of Medical Diagnostics Applications Challenges
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WHAT IS BIG DATA? Big data in healthcare refers to large and complex electronic health data sets
Huge volume and diversity of data types
Includes data from clinical decision support systems (medical imaging, EPRs etc.)
The totality of data related to patient healthcare make up “Big Data” in healthcare
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BIG DATA IN HEALTHCARE"Medical diagnostics, at heart, is a data problem"
Potential to improve quality of healthcare meanwhile reducing costs
Source- MANA
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Need for Big Data Analytics in HealthCare Data mining at times has proven to predict the diseases better than the physicians
Huge volume and variety of data which can’t be handled by traditional methods
Reduce the clinical and economic burden of healthcare
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Need for Big Data Analytics in HealthCare Address shortage of doctors and assist doctors in decision making
Can be used for self-diagnosis or pre-diagnosis in hospitals
Self-diagnosis: Make clinical decision support system accessible to all even in remote area To make ill-informed patients more informed about their health status
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BIG DATA IN MEDICAL DIAGNOSTICS OF
CARDIOVASCULAR DISEASES
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STATUS QUORural India faces a shortage of more than 60% doctors
30 million heart patients in India according to WHO
500 petabytes of available Healthcare Data
Big-data is the way forward
Source- indiatimes/ TOI
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INDICATORS OF HEALTH
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MOST USEFUL PARAMETERS
Source: Ohio State University
HEART RATE VARIABILITY
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HEART RATE VARIABILITY HRV is the physiological phenomenon of variation in time interval between heartbeats One of the most promising quantitative markers of autonomic activity Widely applied in basic and clinical research studies
Ref- http://www.myithlete.com/what-is-hrv, https://en.wikipedia.org/wiki/Heart_rate_variability
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PROCESS OF MEDICAL DIAGNOSTICS• Input patient’s data related to relevant parameters such as HRV, BMI etc.
• Analyse and compare the data using ML algorithms on Database of Parameters
• Prediction/ Diagnosis of cardiovascular diseases
INPUT Data
of Paramete
rs
DATA
ANALYSI
S
OUTPUT
Diagnosi
s
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INPUT PARAMETERS Basic info about patient such as Age, BMI, Smoking Status
Get HRV data of patient using ECG or some wearable devices
Input blood cholesterol, glucose level, MRI data
Ref: Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction by Ayon Dey et al.
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ANALYTICS OF DATA Time Domain/ Frequency Domain Analysis of HRV Data like SDNN
Apply ML Algorithms like SVM, Naive Bayes, Decision Tree, Principal Component Analysis to the Big Data Sets to find patterns and classify and predict the diseases
PCA can be used to reduce the number of attributes, SVM can further be used to predict heart disease
Ref: Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction by Ayon Dey et al.
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OUTPUTo Diagnose heart as healthy or predict possible diseases
o Classification of disease as chronic, coronary heart disease, inflammatory heart disease
o Recommend further action or tests to confirm the disease
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APPLICATIONS Decision Support System to assist doctors in decision making Cross platform systems can be developed to be adopted to smartphones, kiosks etc.
Image Courtesy appleinsider.com
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CHALLENGES Getting high Diagnostic accuracy on new cases from available data
Dealing with missing and noisy data
Reducing the number of tests required for diagnosis
Minimising Time complexity of the whole process from acquisition to decision making
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THANK YOU