Machine learning -- the use of computational algorithms to find patterns in data -- is increasingly being deployed in clinical contexts to support diagnosis and treatment decisions. In the context of growing volumes of clinical data available in electronic form, there is an opportunity to realise dramatic changes in the practice of medicine through the application of large-scale health data analytics and predictive modeling. This talk will introduce a vision for the use of data-driven methods in health, while also raising important questions about the implementation of this vision: is it conceivable that one day your doctor might be replaced by a digital system? What are the risks?

1. James Bailey and KarinVerspoor
School of Computing and Information Systems
Health and Biomedical Informatics Centre
The University of Melbourne
@karinv @ConverSci

3. AlgorithmsData

4. •Learning Health Systems
•What is machine learning?
•Example current applications of machine
learning in health
•Challenges and Open Issues

5. LearningHealthSystems
Health systems – at any
level of scale – become
learning systems when
they can, continuously
and routinely, study and
improve themselves
Arnold Milstein (2013) NEJM

10. •Learning Health Systems
•What is machine learning?
•Example current applications of machine
learning in health
•Challenges and Open Issues

11. Big Data
Machine
Learning
Artificial Intelligence
“Intelligent machines and software”

12. Question
Answer
Historical data Machine Learning Algorithm
Machine Learning
Prediction Model

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16. plasma <= 0.2137
entropy = 0.9311
samples = 698
value = [456, 242]
age <= -0.3787
entropy = 0.7128
samples = 445
value = [358, 87]
True
BMI <= -0.2551
entropy = 0.9631
samples = 253
value = [98, 155]
False
BMI <= -0.1279
entropy = 0.4173
samples = 249
value = [228, 21]
BMI <= -0.7128
entropy = 0.9217
samples = 196
value = [130, 66]
entropy = 0.1093
samples = 138
value = [136, 2]
entropy = 0.6604
samples = 111
value = [92, 19]
entropy = 0.2918
samples = 39
value = [37, 2]
entropy = 0.9752
samples = 157
value = [93, 64]
plasma <= 0.7758
entropy = 0.9031
samples = 69
value = [47, 22]
plasma <= 1.4004
entropy = 0.8516
samples = 184
value = [51, 133]
entropy = 0.5813
samples = 36
value = [31, 5]
entropy = 0.9993
samples = 33
value = [16, 17]
entropy = 0.9514
samples = 124
value = [46, 78]
entropy = 0.4138
samples = 60
value = [5, 55]
Deep Neural Network

20. •Learning Health Systems
•What is machine learning?
•Example current applications of machine
learning in health
•Challenges and Open Issues

23. April 14, 2007
CHIEF COMPLAINT: Shortness of breath.
HISTORY OF PRESENT ILLNESS: This 68-year-old female presents to the emergency
department with shortness of breath that has gone on for 4-5 days, progressively
getting worse. It comes on with any kind of activity whatsoever. She has had a
nonproductive cough. She has not had any chest pain. She has had chills but no
fever.
EMERGENCY DEPARTMENT COURSE: The patient was admitted. She has had intermittent
episodes of severe dyspnea. Lungs were clear. These would mildly respond to
breathing treatments and morphine. Her D‐dimer was positive. We cannot scan her
chest; therefore, a nuclear V/Q scan has been ordered. However, after consultation
with Dr. C, it is felt that she is potentially too unstable to go for this. Given
the positive D‐dimer and her severe dyspnea, we have weighed the risks and benefits
of anticoagulation with her heme-positive stools. She states that she has been
constipated lately and doing a lot of straining. Given the possibility of a PE, it
was felt like anticoagulation was very important at this time period; therefore,
she was anticoagulated. The patient will be admitted to the hospital under Dr. C.

24. April 14, 2007
CHIEF COMPLAINT: Shortness of breath
HISTORY OF PRESENT ILLNESS: This 68-year-old female presents to the emergency
department with shortness of breath that has gone on for 4-5 days, progressively
getting worse. It comes on with any kind of activity whatsoever. She has had a
nonproductive cough. She has not had any chest pain. She has had chills but no
fever.
EMERGENCY DEPARTMENT COURSE: The patient was admitted. She has had intermittent
episodes of severe dyspnea. Lungs were clear. These would mildly respond to
breathing treatments and morphine. Her D‐dimer was positive. We cannot scan her
chest; therefore, a nuclear V/Q scan has been ordered. However, after consultation
with Dr. C, it is felt that she is potentially too unstable to go for this. Given
the positive D‐dimer and her severe dyspnea, we have weighed the risks and benefits
of anticoagulation with her heme-positive stools. She states that she has been
constipated lately and doing a lot of straining. Given the possibility of a PE, it
was felt like anticoagulation was very important at this time period; therefore,
she was anticoagulated. The patient will be admitted to the hospital under Dr. C.

25. (symptoms)
shortness of breath
dyspnea
cough
chills
constipated
(test results)
D‐dimer was positive
positive D‐dimer
(demographics and status)
68-year-old
female
admitted
(therapies)
breathing treatments
morphine
anticoagulated
Text analysis supporting structured (coded) representation of clinical texts.

26. April 14, 2007
CHIEF COMPLAINT: Shortness of breath
HISTORY OF PRESENT ILLNESS: This 68-year-old female presents to the emergency
department with shortness of breath that has gone on for 4-5 days, progressively
getting worse. It comes on with any kind of activity whatsoever. She has had a
nonproductive cough. She has not had any chest pain. She has had chills but no
fever.
EMERGENCY DEPARTMENT COURSE: The patient was admitted. She has had intermittent
episodes of severe dyspnea. Lungs were clear. These would mildly respond to
breathing treatments and morphine. Her D‐dimer was positive. We cannot scan her
chest; therefore, a nuclear V/Q scan has been ordered. However, after consultation
with Dr. C, it is felt that she is potentially too unstable to go for this. Given
the positive D‐dimer and her severe dyspnea, we have weighed the risks and benefits
of anticoagulation with her heme-positive stools. She states that she has been
constipated lately and doing a lot of straining. Given the possibility of a PE, it
was felt like anticoagulation was very important at this time period; therefore,
she was anticoagulated. The patient will be admitted to the hospital under Dr. C.

27. Fungal infection surveillance by
classifyingCT scan reports
Extracting key
information from
pathology reports
Work by or with Lawrence Cavedon, David Martinez, and others, starting at NICTA
Retrieval of disease-related
records based on
unstructured data
(pathology reports,
radiology reports,
clinical notes)

29. https://shahlab.stanford.edu/greenbutton
clinical situation
RCT
guideline available?
use guideline
cohort of similar patients
no
query medical record db
machine learning: analysis,
prediction, hypothesis testingdecision!

30. •Learning Health Systems
•What is machine learning?
•Example current applications of machine
learning in health
•Challenges and Open Issues

33. CT-scan
85% chance tumor is cancerous
Recommend radiation treatment
• Do I trust the prediction?
• How was this conclusion reached?
• Are there limitations in the reasoning?
• Can the prediction be re-done, placing more weight on
the patient’s family history?Dr Smart

39. James:
baileyj@unimelb.edu.au
Karin:
karin.verspoor@unimelb.edu.au