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ROLE OF BIG DATA IN MEDICAL DIAGNOSTICS
ELQ 301 PRESENTATION
NISHANT AGARWAL
2014EE10464
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
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
BIG DATA IN HEALTHCARE
"Medical diagnostics, at heart, is a data problem"
Potential to improve quality of healthcare
meanwhile reducing costs
Source- MANA
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
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
BIG DATA IN MEDICAL DIAGNOSTICS OF
CARDIOVASCULAR DISEASES
STATUS QUO
Rural 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
INDICATORS OF HEALTH
MOST USEFUL PARAMETERS
Source: Ohio State University
HEART RATE VARIABILITY
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
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
Parameters
DATA ANALYSIS
OUTPUT
Diagnosis
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.
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.
OUTPUT
o 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
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
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
THANK YOU

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Role of Big Data in Medical Diagnostics

  • 1. ROLE OF BIG DATA IN MEDICAL DIAGNOSTICS ELQ 301 PRESENTATION NISHANT AGARWAL 2014EE10464
  • 2. 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
  • 3. 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
  • 4. BIG DATA IN HEALTHCARE "Medical diagnostics, at heart, is a data problem" Potential to improve quality of healthcare meanwhile reducing costs Source- MANA
  • 5. 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
  • 6. 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
  • 7. BIG DATA IN MEDICAL DIAGNOSTICS OF CARDIOVASCULAR DISEASES
  • 8. STATUS QUO Rural 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
  • 10. MOST USEFUL PARAMETERS Source: Ohio State University HEART RATE VARIABILITY
  • 11. 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
  • 12. 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 Parameters DATA ANALYSIS OUTPUT Diagnosis
  • 13. 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.
  • 14. 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.
  • 15. OUTPUT o 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
  • 16. 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
  • 17. 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

Editor's Notes

  1. Everyone heard Impact on healthcare system
  2. laboratory, pharmacy, electronic patient records
  3. Availability of massive quantities of data (known as ‘Big Data’) Petabyte? 10^15
  4. Make sense of huge data
  5. Potential to address Devices employing big data can be used Now I will tell how
  6. Look into application of Big Data in medical diagnosis of heart diseases
  7. Cardiovascular diseases account for around one fourth of all deaths due to non- communicable diseases in India Big Data can be used to address the shortage of doctors by use in medical diagnosis devices To address these challenges Big data is the way forward
  8. Some easy to get, some require special instruments, availability and tests
  9. Most imp parameters to dignose heart disease, one of the most important and readily available is HRV
  10. 60 considered good avg of RR interval of pulses "RR variability" (where R is a point corresponding to the peak of the QRS complex of the ECG wave; and RR is the interval between successive Rs), and "heart period variability More than heart rate HRV gives a better indicator of health of the heart As many commercial devices now provide an automated measurement of HRV, the cardiologist has been provided with a seemingly simple tool for both research and clinical studies. Now that we have seen some useful parameters let us look at how we can diagnose diseases using these
  11. (Database of Parameter such as HRV,BMI etc.) Black Box (M.L. Algorithms) Lets look in detail into each of the steps involved
  12. 1 and 2 nd in remote areas possible, 3rd in urban areas
  13. Support vector machines (Standard Deviation of NN interval)
  14. In medical diag Dealing with noisy data : Medical data typically suffer from uncertainty and errors. Therefore machine learning algorithms appropriate for medical applications have to have effective means for handling noisy data. nosis very often the description of patients in patient 5 records lacks certain data. It is desirable to have a classifier that is able to reliably diagnose with a small amount of data about the patients. In medical practice, the collection of patient data is often expensive, time consuming, and harmful for the patients.