1. Machine Learning with Clinical Data
Leila Mureebe

2. Machine Learning
Use of statistical techniques to give computers the ability to progressively improve performance on a specific task with data, without being explicitly programmed

3. Machine Learning Techniques

4. Clinical Problem Patient in CT ICU after complicated cardiac procedure
Both feet are cool
Is a consult needed?

5. Clinical Problem
Two samples
How would you classify them?

6. Clinical Problem Survey administered to providers Accuracy
Various roles
Various years in position
Accuracy
57.7%
+/-
9.9%
Clinical Problem

7. Classification Problem
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known
Classification Problem

8. In machine learning and statistics, classification is the problem of identifying to which category an observation belongs to
This is based on the basis of a training set of data where the category is known
This is a form of supervised learning
Classification

9. Goal: Create an algorithm to accurately classify continuous wave Doppler signals
Initially - 2 classifiers
Future - 3 classifiers
Our Project

10. Requirements Curated data set
Data curation is a broad term used to indicate processes and activities related to the organization and integration of data collected from various sources, annotation of the data, and publication and presentation of the data such that the value of the data is maintained over time, and the data remains available for reuse and preservation
Curated data set
Requirements

11. Our Data Set 100 patients undergoing ABIs 5 samples per patient ABI
Phasicity
Plethysmograph
Official report
Our Data Set

12. Classification Algorithms
Linear Classifiers: Logistic Regression, Naive Bayes Classifier
Support Vector Machines
Decision Trees
Boosted Trees
Random Forest
Neural Networks
Nearest Neighbor
Classification Algorithms

13. Features Examined Temporal Frequency Rise time Bandwidth (99%)
Decay time
Mean frequency
Mean time
Peak frequency (high)
Diastolic level
Peak frequency (low)
Systolic level
Total harmonic distortion
Total peak height
Power

14. A receiver operating characteristic curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied
ROC Curve

15. support vector machine constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection[3].
Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class, since in general the larger the margin the lower the generalization error of the classifier
Best Performer

16. H1 does not separate the classes. H2 does, but only with a small margin. H3 separates them with the maximum margin
SVM Theory

17. Survey to assess human accuracy
Additional Studies
Survey to assess human accuracy
Comparison of extracted audio to plethysmogram
Extrapolation of CTA results to audio

18. Other Example
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan, PhD1; Lily Peng, MD, PhD1; Marc Coram, PhD1; et al
JAMA. 2016;316(22): doi: /jama