1. D A T A S C I E N C E I N H E A L T H C A R E
T H E U N I V E R S I T Y M A L A Y A M E D I C A L
C E N T R E
B R E A S T C A N C E R E C O S Y S T E M
A S S O C . P R O F D R S A R I N D E R K . D H I L L O N
D ATA S C I E N C E & B I O I N F O R M AT I C S L A B
U N I V E R S I T Y O F M A L AYA , K U A L A L U M P U R , M A L AY S I A
S A R I N D E R @ U M . E D U . M Y
H T T P : / / S A R I N D E R K A U R . C O M
H T T P S : / / U M E X P E R T . U M . E D U . M Y / S A R I N D E R

2. Machine
Learning
Knowledge
graph
Deep
Learning
Statistics
Big data
Cloud
computing
Predictive
analytics
Data
mining
Text mining
Artificial
Intelligence
Image
Processing
noSQL
DATA
SCIENCE

3. Data Science in Healthcare
Transforming healthcare Data is used effectively Personalised medicine Predicting factors for
improved decision
making
Historical data use in
analytics
Artificial enabled
systems
Electronic medical
records/Electronic
health records

4. F O C U S O F TA L K
U N I V E R S I T Y M A L AYA M E D I C A L
C E N T R E ( U M M C )
D ATA S C I E N C E H E A LT H C A R E
E C O S Y S T E M

5. UMMC
Data Science Healthcare Ecosystem
Machine Learning for
Prediction
MyBCC ( Malaysian Breast Cancer
Cohort Study)
Deep Learning for
Classification
Text Mining in Radiology
UMMC Breast cancer clinical data
&
Electronic Medical Records

6. DEVELOPMENT OF ELECTRONIC
MEDICAL RECORD FOR
CLINICAL & RESEARCH PURPOSES:
B R E A S T C A N C E R C L I N I C A L D ATA M A N A G E M E N T
U S I N G P O I N T- O F - C A R E A N D M U LT I D I S C I P L I N A R Y
D ATA C O L L E C T I O N
FIRST PROJECT

7. Bernama (2018, November 8). Health Minister: EMR system to be implemented at 145 hospitals within the next three years. The Star.
Brar, K. (2018, January 31). Aiding healthcare through data analytics. The Star.
“The new technology would facilitate medical practitioners,
including doctors and nurses to identify and share patients' medical
consultation information, as well as prescription of medicines
through an integrated system.”
“With Big Data, healthcare organisations have the ability of
information exchange, leading to a 360° view of their
patients, so doctors can give a more complete diagnosis.”
In the News

8. Common Scenario: Limitations with clinical data management

9. O B J E C T I V E
To enhance current i-Pesakit© system and achieve interoperability by consolidating
and automating manual retrieval processes of surgical, oncology, radiology, pathology
and pharmacy data into mineable data for clinical use, secondary data usage in audits
and breast cancer research activities (iResearch)
CLINIC VISITS
CLINICAL AUDIT &
REPORTING
DIAGNOSIS & TREATMENT
CLINICAL RESEARCH

10. Interdisciplinary Research Concept
Bridging the gap between research & clinical practice

11. Architecture System of the UMMC i-Pesakit© Breast Cancer Module

12. I-pesakit© Breast Cancer Module During Weekly MDT Meeting

13. System Content Features and Implementation into Clinical Workflow

14. i-Pesakit Breast Cancer Chemotherapy e-Prescription

15. Clinicians’ Perception towards the System
Usage of i-Pesakit© Breast Cancer Module in Clinical Work Is this system worth the time and effort to be used?
Effect on Clinical Workflow by Using i-Pesakit© Breast Cancer Module Among Clinicians

16. • Enhanced EMR provides point-of-care data in
clinics, wards and MDT meetings
• Ability to produce reports on breast cancer
survival in Malaysia for research, individual
hospital performance for policymakers to track
outcomes and provide direction in cancer
control.
• A time effective approach while producing new
knowledge through data mining and analysis –
Research & Development
Alternate Clinical Data Management Using Point-of-care
and Multidisciplinary Data Collection
Nor, N. A. M., Taib, N. A., Saad, M., Zaini, H. S., Ahmad, Z., Ahmad, Y., & Dhillon, S. K. (2018). Development of electronic medical records for clinical and
research purposes : the breast cancer module using an implementation framework in a middle income country- Malaysia. BMC Bioinformatics, 19(Suppl 13), 1–
16. http://doi.org/10.1186/s12859-018-2406-9) (ISI-Indexed)

17. PUBLICATION IN BMC
BIOINFORMATICS

18. SECOND PROJECT

19. SECOND PROJECT
Application of machine learning to predict the important clinical prognostic
factors affecting survival rate of breast cancer
8066 samples from
University Malaya Medical
Centre (UMMC)
(1993-2018)
23 clinical factors
1 target variable (Life
status)

20. METHODOLOGY – Supervised learning (SL)

21. MACHIN
E
LEARNI
NG
PIPELI
NE
1. Model Evaluation
2. Random Forest Further Modelling
3. Variable Selection
4. Decision Tree
5. Survival Curves Validation

22. RESULTS – SL – Model evaluation
No Algorithm Accuracy
(%)
Sensitivity Specificity AUC Precision Matthews
correlation
coefficient
1 Decision tree 79.80 0.82 0.75 0.72 0.91 0.52
2 Random
forest
82.70 0.83 0.81 0.86 0.93 0.59
3 Neural
networks
82.00 0.83 0.79 0.84 0.93 0.58
4 Extreme
boost
81.70 0.84 0.75 0.87 0.89 0.57
5 Logistic
regression
81.10 0.82 0.78 0.85 0.92 0.55
6 Support
vector
machine
81.80 0.81 0.84 0.85 0.95 0.57

23. CALIBRATION ANALYSIS

24. RESULTS – SL – Variable importance

25. PART 1 RESULTS – SL – Variable importance

26. PART 1 RESULTS – SL – Important variables

27. RESULTS – SL – Decision tree
Decision tree for all data; Shows that patients with curable cancer, ≤ 1 positive lymph nodes (PLN) and ≤ 2 total axillary
lymph nodes removed (TLN) had 50% survival probability, while patients with pre-cancer, ≤ 1 PLN and ≤ 2 TLN had
90% survival probability. Patients with metastatic cancer, > 6 PLN and > 6 TLN had only 25% survival probability

28. PART 1 RESULTS – SL – Survival curves

29. VALIDATION USING AJCC MANUAL 5th edition
Variables AJCC Machine learning
Classificat
ion of
tumor
size
< 2.0cm
1.0 < 4.0cm
>4.0cm
<2.5cm
2.5 < 4.8cm
4.8 < 11.0cm
>11.0cm
Number
of
positive
lymph
nodes
≤ 3
3 < 6
> 6
< 3
3 < 9
> 9

30. SECOND PROJECT
Application of machine learning to predict the important clinical prognostic
factors affecting survival rate of breast cancer
8066 samples from
University Malaya Medical
Centre (UMMC)
(1993-2018)
23 clinical factors
1 target variable (Life
status)

31. METHODOLOGY – Unsupervised learning (UL)
• Using Gap Statistics
method, factoextra
library in R
• Labels of variables
suggest the value
of K (number of
cluster)
• Run K-means and
record distance, V
• V minimizes when
K = n
• Visualize through
scree plot
1. Determine
optimal number
of cluster
• Compare
hierarchical, k-means
and PAM
(Partitioning around
Medoids)) using
clValid R library
• Measured using
connectivity, Dunn
and Silhouette
2. Determine
most suitable
clustering
method
• Variables
subdivided into
groups by cutting at
a desired similarity
level
• Function dist() in
hclust library was
used to calculate a
dissimilarity matrix
as an input
• Dendrogram was
visualise using
fviz_dend() function
in factoextra library
3. Perform
hierarchical
clustering
• Clinicians validate
the patterns of
variables in each
cluster
• Results to be
compared with real-
time survival analysis
4. Validate the
pattern of
clusters

32. RESULTS – UL
Optimal number of cluster = 6
Suitable clustering method=
hierarchical clustering
METHOD CONNECTIV
I-TY
DUNN SILHOUETT
E
hierarchica
l
16.45 0.72 0.38
kmeans 16.34 0.68 0.38
pam 20.70 0.34 0.16

33. RESULTS – UL – Hierarchical clustering
V1: Age
V22: Total lymph nodes
V21: Hormonal therapy
V12: ER status
V13: PR status
V18: Method of axillary lymph node
dissection
V2: Marital status
V7: Classification of breast cancer
V16: Surgery status
V19: Radiotherapy
V20: Chemotherapy
V3: Menopausal status
V8: Laterality
V6: Method of diagnosis
V15: Primary treatment type
V10: Grade of differentiation
V14: cerb2 status
V17: Type of surgery
V5: Race
V4: Presence of family history
V9: Cancer stage
V11: Tumor size
V23: Positive lymph nodes

34. PUBLICATION

35. PART 1 REPOSITORY

36. D E E P L E A R N I N G
A P P R O A C H E S :
L E S I O N
C L A S S I F I C AT I O N
THIRD PROJECT

37. Classification of benign and malignant lesions in breast
ultrasound images:
Benign?
Or
Malignant?

38. DATASET
• Collected from UMMC between 2012-2013
• 83 Patients
• Size of images: 1400 X1050
• 140 Benign and 136 Malignant images(Biopsy
Confirmed)
Pre-Processing
• Augmentation
 Rotation
 Shift
 Zoom
 Flip
 Shear
• Zero-Center Data
• Normalize data

39. TRADITIONALLY
:
Handcrafted Features Machine Learning
Prediction
Benign
Malignant

40. OUR STUDY
Deep Learning
Transfer Learning
Feature
Extraction
VGG16 Fine
Tuning
ResNet50 Fine
Tuning
VGG-FC
VGG-SVM
VGG-DT
VGG-RF
VGG-AB
ResNet-FC
ResNet-SVM
ResNet-DT
ResNet-RF
ResNet-AB
• We use VGG16 and ResNet50 pre-trained on Breast US image dataset
• We used Convolutional layers of VGG16 and ResNet50 for feature extraction
• 12 Deep Learning models that we used in our study

41. ROC of different models

42. References Dataset Deep learning Models Performance
[29]
4254 benign
GoogLeNet
Accuracy: 91.23%
3154 malignant Sensitivity: 84.29%
Specificity: 96.07%
[30]
135 benign
Boltzmann
Accuracy: 93.4%
92 malignant Sensitivity: 88.6%
Specificity: 97.1%
[31]
100 benign
Deep Polynomial
network+SVM
Accuracy: 92.40%
100 malignant Sensitivity: 92.67%
Specificity: 91.36%
[32]
275 benign
Stacked denoising
Autoencoder
Accuracy: 82.4%
245 malignant Sensitivity: 78.7%
Specificity: 85.7%
Current Study
249 benign
Attention VGG16 +
ensembled loss
Accuracy: 93%
190 malignant Sensitivity: 96%
Specificity: 90%
THE STATE OF THE ART OF DEEP LEARNING MODELS IN BREAST ULTRASOUND LESION CLASSIFICATION.

43. FOURTH PROJECT OBJECTIVE
To develop a
fully automated AI-enabled database platform
with interactive visualisations
for researchers and clinicians
using The Malaysian Breast Cancer Survivorship Cohort (MyBCC) Study
Baseline
6 Months
1 Year
3 Years
5 Years
5 Timelines
603
Variables
909
Samples
May 2019

44. FOURTH PROJECT MYBCC
Baseline
6 Months
1 Year
3 Years
5 Years
603
Variables
800
Patients

45. FOURTH PROJECT METHODOLOGY

46. FOURTH PROJECT METHODOLOGY – Automated machine
learning

47. FOURTH PROJECT– Automated machine learning
Algorithm 1. Cartesian product to select lifestyle and clinical factors from different tables
Select 13 lifestyle factors, life status and survival years from table, mybcc and 4 clinical factors from table, clinical
where the mybcc.RN = clinical.RN (select samepatient ID from both tables)
r = 𝝈mybcc.RN = clinical.RN ((πRN,l1,l2,l3…,l13,lifestatus,survivalyears (mybcc)) × (πRN,c1,c2,c3.c4 (clinical)))
Definition:
r = relational database
𝝈 = selection
Π = projection
× = Cartesian product
mybcc = data table, which contains lifestyle factors
clinical = data table, which contains clinical factors
RN = patient ID/ primary key in both tables
lifestatus = life status of the patients (Alive/Dead)
survivalyears = Overall survival years of the patients
l1,l2,l3…l13 = 13 lifestyle factors
c1,c2,c3,c4 = 4 clinical factors

48. FOURTH PROJECT– Automated machine learning
Algorithm 2. Python-HTML integration for automated machine learning
a = query1 + ((pm1,pm2,…pmn)+ ps + ph)
Definition:
a = automated analysis
query1 = 𝝈mybcc.RN = clinical.RN
((πRN,l1,l2,l3…,l14,lifestatus,survivalyears (mybcc)) ×
(πRN,c1,c2,c3.c4 (clinical) (Refer to Algorithm 1)
pm1,pm2,…pmn = Pyhton modules
ps = Python script to run each analysis
ph = Python-HTML connection via cgitb

49. FOURTH PROJECT– Automated machine learning
Algorithm 3. Model of the automated visualisation from database
v = query2 + (c1,c2,…,cn)
Definition:
v = visualisation
query2 = query1 + ((pm1,pm2,…pmn) + ps + ph)
(Refer to Algorithm 2)
c1,c2,…,cn = different types of charts

50. FOURTH PROJECT RESULTS – Digitized questionnaire

51. FOURTH PROJECT– Example of automated machine
learning

52. FOURTH PROJECT– Automated quality of life scoring

53. FOURTH PROJECT– Interactive Visualisations

54. FOURTH PROJECT– Reporting/download

55. Other visualizations in progress

56. FIFTH PROJECT TEXT MINING
.in+0=========== REPORT TEXT ==========.br.br.br.br.br.br.brMRI THORACOLUMBAR +C of
13-Oct-2015:.br.brIndication.brMetastatic breast carcinoma. Currently complaint of weakness of the lower limb
bilaterally. TRO cord involvement/ compression..br.brSequences.brCoronal T1W, T2W.brSagittal T1W,T2W,
CISS3D.brAxial CISS3D, T2W.brPost gad- sag, axial.br.brFindings.brCorrelation made with previous CT dated
8.6.2015..br.brNormal spinal alignment..brMultilevel enhancing high signal intensity on T1/T2W/STIR is seen in
the thoracic and lumbar vertebral bodies, sacrum, both ilium and sternum..brReduced T12 vertebral body
height..brThey are expansion of the vertebral body at T3, T6, T9, T10 and T12 levels causing indentation of the
spinal cord worst at the T12 level with AP diameter of the spinal canal measures 0.8cm. .brThe rest of vertebral
body heights are preserved..brThe intervertebral disc returns normal signal..brThe spinal cord ends at L1. No
intramedullary lesion seen or high signals seen in spinal cord at T2WI. .br.brImpression.brBreast carcinoma
with .br1. Multiple vertebral body metastasis with multilevel spinal cord indentation..br2. Pathological
compression fracture and spinal canal stenosis at T12 level .br.br.brDrs Vithya / Nur Aishah / DR
NAZRI.br.br.br.br.br.br.brReport Written By: Dr MOHAMMAD NAZRI BIN MD. SHAH? 13-OCT-
2015 05:05 PM.brReport Approved By : Dr MOHAMMAD NAZRI BIN MD. SHAH? 13-OCT-2015 05:05
PM.br.br.br========== REPORT TEXT END =========.br.in-0

57. WORKFLOW

58. mmo
Towards an
AI Enabled
Digital
Platform for
Modern
Cancer
Healthcare
A Fully
Automated EMR
for BCM
(iPesakit BCM)
AI Enabled
Breast Cancer
Research EMR
AI Powered
Sentiment
Analysis
Research
Databases
1
2
3 Public
Opinio
n
Mobile Application
for E-
Communication
between doctors,
nurses and patients
4
Monitoring & Predictions

59. Summary
• Data Science Is Reshaping Healthcare
• Techniques Such As Machine Learning, Deep Learning, Text Mining Are The Core of
Data Science
• UMMC Is One of The Primary Hospital In The Country To Embark On Data Science
Projects
• The Data Science Pipeline Produced And Tested In These Projects Will Be Used In
Other Asian Hospitals
THANK YOU FOR LISTENING

60. Professor. Dr. Nur Aishah Binti Mohd Taib, UM,
Dr. Nurul Aqilah Mohd Nor, Bioinformatics, UM,
Dr. Tania Islam, MyBCC Project Manager, UM,
Professor Pietro Lio, Adviser, University of Cambridge, UK,
Tan Wee Ming, Programmer, student, Bioinformatics, UM,
Dr. Elham Yousef Kalafi, Researcher, Bioinformatics, UM
Mogana Darshini Ganggayah, student, Bioinformatics, UM