This document provides an overview of machine learning applications in healthcare. It discusses how machine learning can be used to improve diagnosis, treatment, and other areas by automating processes and analyzing patient information. Different types of health data that can be used as inputs for machine learning models are described, including medical information, molecular features, and medical images. Common machine learning tasks for images like detection, segmentation, and diagnosis are also outlined. The document then explains the basic machine learning process of gathering and cleaning data, building and evaluating models, and deploying selected models. Common machine learning algorithms like linear regression, regularization techniques, and deep learning approaches like convolutional neural networks are briefly introduced.

1. Seyed Ali Madani Tonekaboni
Farnoosh Khodakarami
Practical Machine Learning in Healthcare
1

2. 2
Health and mathematics
Biology
Chemistry
Physics
Mathematics
Mathematics
Mathematics
Prescription
(Quantitative
+Qualitative)
Patient
checkup
Known rules

3. 3
Health and mathematics
Biology
Chemistry
Physics
Mathematics
Mathematics
Mathematics
Prescription
(Quantitative
+Qualitative)
Patient
checkup
Known rules

4. 4
Healthcare and machine learning
Improve quality
(and quantity) of
life by improving
and automating:
● Diagnosis
● Treatment
● ….
Patient
information

5. Machine learning
5
Healthcare and machine learning
Improve quality
(and quantity) of
life by improving
and automating:
● Diagnosis
● Treatment
● ….
Patient
information
Collected data

6. Machine learning functions and tools need specific
data formats
Features(Attribute/variable)
Datarecords
(samples)
# Features = Dimension of dataset
6
● Some ML models use other inputs like images in
convolutional neural network

7. Health data (sample types)
7
Sample types
Patient samples Animal models
Cell lines and other
model system

8. Health data (variable types)
8
Variable types
Medical
information
Molecular
features
Features in
molecules

9. Organism to DNA
9
We can extract
information in any
of these levels and
use the as different
feature types in our
model
Atom
Molecule
Cell
Tissue
Organ
System
Organism

10. Scale in
different levels
of organization
10

11. Data type
11
Survey
information
No need for measurement or
quantification technologies
Macroscopic
measurement
High level measurements without
molecular level information
Microscopic
measurement
Capturing molecular level information in
cells, blood, etc.

12. Medical information (Survey)
12
Age Gender Medication history

13. Medical information (Macroscopic measurements)
13
Height Weight

14. Medical information (Macroscopic measurements)
14
Blood pressure Heartbeat

15. Medical information (Macroscopic measurements)
15
Tumor existence
and size
Heartbeat

16. Medical information (Microscopic measurements)
16
Blood sugar
Histopathological
images

17. Medical information (Microscopic measurements)
17
Quantifying molecular feature values in
cells, blood serum, etc.

18. Molecular features in
central dogma of
biology
18

19. Molecular features (Protein)
19

20. Protein structure determine its function
20
Hemoglobin beta

21. Protein structure determine its function
21
Hemoglobin beta

22. Machine learning and proteins
22
how a small-molecule
drug will interact with
proteins in the body

23. Molecular features (DNA and RNA)
23

24. Medical Imaging
● The most direct way to see inside the human
(or animal) body
is cut it open (i.e., surgery)
● We can see inside the human body in ways
that are less
invasive or (completely non-invasive)
● We can even see
metabolic/functional/molecular activities
which are not visible to naked eye
24

25. Imaging Techniques
Ultrasound PET
MRICT

26. Clinical Task on Images
● Detection: Detecting abnormality on the images
● Characterization: Characterizing this abnormality
Segmentation: Detecting boundary of abnormality
Diagnosis: Identify this abnormality as benign or malignant
Staging Classify this abnormalities into multiple predefined category
● Monitoring:
Tracing object characteristic during monitoring scans
26

27. Treatment
Machine
learning
Images
Machine learning and Radiomics
Image acquisition
&
Segmentation Prognosis
Feature Extraction
Texture
ShapeIntensity
Wavelet
Survival prediction
staging
Disease Progression
Toxicity
Personal Optimal
Treatment
Reduce Cost
27

28. Step By step to
Machine Learning
Farnoosh Khodakarami
Postdoctoral Fellow
Princess Margaret Research Institute / University of Toronto
28

29. Field of study that gives
computers the ability to learn
without being explicitly
programmed
(Arthur Samuel 1951)
https://www.ibm.com/ibm/history/ibm100/us/en/i
cons/ibm700series/impacts/
The Samuel Checkers-playing Program was among the world's first successful
self-learning programs.
He coined the term "machine learning" in 1959
29

30. TheMachineLearningProcesses
Gathering and
Cleaning data
Model building and
Model evaluation
Deploy selected Model and
present result 30

31. DataSet
Size Location ... #Rooms
DataRecords
(samples)
Features(Variable/Attribute)
A data set (or dataset) is a collection of data
Every column of the table represents a particular variable,
Each row corresponds to a given member of the data set
31

32. 32

33. Supervised vs
Unsupervised Learning
Unsupervised Learning
- No Knowledge of output
- data is unlabeled
- Self guided learning
- Goal: determine data
patterns/grouping
Supervised Learning
- Knowledge of output
- data is labeled with class or
value
- Goal: predict value label or
class label
33

34. 34

35. 35

36. Linear Regression
36

37. Price
?
1M
3M
37

38. Linear Regression
Linear regression is the simplest and most widely used statistical
technique
A linear model expresses the target output value in terms of a sum of
weighted input variables.
38

39. Linear Regression
Size of the house feet2
Priceofthehouse
500 1000 1500 2000 2500
400
300
200
100
500
600
700
fi
= w0
xi
+b0
(x,y)
x
y
39

40. Overfitting
Overfitting: Good
performance on the training
data, poor generalization to
other data.
Underfitting: Poor
performance on the training
data and poor generalization to
other data
Image source wikipedia
40

41. Bias–variance tradeoff
41
High variance High bias
Low bias
Low variance

42. K-Folds Cross Validation
Image source wikipedia
42

43. Regularized learning
43

44. Regularization to overcome overfitting
Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity:
the lasso and generalizations. Chapman and Hall/CRC 44
Objective function:
Constraint for regularization:
Ridge regression

45. Optimization space in regularization
Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity:
the lasso and generalizations. Chapman and Hall/CRC 45
Lasso Ridge
a a
b b
contours of the
residual-sum-of-squares
Constraints

46. Autoencoders
46

47. 47
What are neurons in artificial neural networks?
Feature 1
Feature 2
Feature 3
Supposedly Feature 1
Supposedly Feature 2
Supposedly Feature 3
Supposedly Feature M-2
Supposedly Feature M-2
Supposedly Feature M
Feature M-2
Feature M-1
Feature M
Embeddings

48. 48
What are neurons in artificial neural networks?
Feature 1
Feature 2
Feature 3
Supposedly Feature 1
Supposedly Feature 2
Supposedly Feature 3
Supposedly Feature M-2
Supposedly Feature M-2
Supposedly Feature M
Feature M-2
Feature M-1
Feature M
Embeddings
Encoding Decode

49. 49
What are neurons in artificial neural networks?
Feature 1
Feature 2
Feature 3
Feature M-2
Feature M-1
Feature M

50. 50
What are neurons in artificial neural networks?
Feature 1
Feature 2
Feature 3
Feature M-2
Feature M-1
Feature M
New feature 1
New feature 2
New feature H-1
New feature H
H < M => reducing dimension

51. 51
What are the connections in artificial neural networks?
Feature 1
Feature 2
Feature 3
Feature M-2
Feature M-1
Feature M
New feature (NF) 1

52. 52
What are the connections in artificial neural networks?
Feature 1
Feature 2
Feature 3
Feature M-2
Feature M-1
Feature M
New feature (NF) 1
NF1 = W1*F1+W2*F2+W3*F3+...WM*FM
?

53. 53
Adding nonlinearity via activation functions
Output of a neuron = activation(weighted sum of inputs of the neuron)
NF1 = activation(W1*F1+W2*F2+W3*F3+...WM*FM)

54. Deep learning for image classification
54

55. Convolutional Neural Network (CNN)
55
Convolutional layers
Detect Pattern in Images
- shape, object, corners,
circles
Specify filters in the images
Deeper layers can detect
more complex objects

56. Different Level of
abstraction
56Source: Deep learning: a bird’s-eye view, by R. Pieters, 2015, pp. 58, 62.

57. Convolutional Neural Network (CNN)
57
resource: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6268174/

58. Convolutional Neural Network (CNN)
58

59. Issues in CNN
59
Slow training (for complex tasks)
High computational cost
Lot of data to train them

60. Transfer learning
60

61. 61
Transfer Learning
● Deep convolutional neural network models may take days or even weeks to
train on very large datasets.
● Reusing the already trained model is a way to short-cut this process.

62. 62
https://medium.com/free-code-camp/asl-recognition-using-transfer-learning-918ba054c004

63. Transfer Learning as feature extractor
63
https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applic
ations-in-deep-learning-212bf3b2f27a

64. Where to find pre-trained models for transfer learning
❏ Keras: https://keras.io/applications/
❏ TensorFlow: https://github.com/tensorflow/models
❏ caffe: https://github.com/BVLC/caffe/wiki/Model-Zoo
❏ caffe2: https://github.com/caffe2/caffe2/wiki/Model-Zoo
❏ pytorch: https://github.com/Cadene/pretrained-models.pytorch
❏ Lasagne: https://github.com/Lasagne/Recipes
❏ Many pretrained models for various platforms can also be found at
https://www.gradientzoo.com.
64

65. Some
pre-trained
CNN
Models
65

66. Transfer Learning on Medical imaging
❏ Cheplygina V, de Bruijne M, Pluim JP. Not-so-supervised: a survey of
semi-supervised, multi-instance, and transfer learning in medical image
analysis. Medical image analysis. 2019 May 1;54:280-96.
❏ Deep Learning for Computer Vision with Python.
https://www.pyimagesearch.com/2018/12/03/deep-learning-and-medical-image-analysis-with-keras/
https://towardsdatascience.com/convolutional-neural-network-for-breast-cancer-classification-52f1213dcc9
66
Skin cancer Breast Cancer