Dr. Carlo Carandang, a psychiatrist and data scientist, talks about how Big Data can be implemented into clinical psychiatric practice to improve patient care and reduce costs. Dr. Carandang introduces Big Data topics, Big Data systems, machine learning algorithms, and AI psychiatry applications. Dr. Carandang presented this talk at the 2019 Presidential Symposium in Washington, DC, sponsored by the Washington Psychiatric Society.

1. Big Data in Psychiatry:
An Introduction and Overview
Carlo Carandang, MD, MSc, FAPA

2. Learning Goals
• Understand Big Data in Psychiatry
• Understand Big Data Systems for Psychiatry
• Understand machine learning algorithms for
Psychiatry AI applications

3. I, Robot (2004)
• Det. Spooner : So, Dr. Calvin, what exactly do you
do around here?
• Dr. Calvin : My general fields are Advanced
Robotics and Psychiatry. Although, I specialize in
hardware-to-wetware interfaces in an effort to
advance U.S.R.'s robotic ahthropomorphization
program.
• Det. Spooner : So, what exactly do you do around
here?
• Dr. Calvin : I make the robots seem more human.
• Det. Spooner : Now wasn't that easier to say?
• Dr. Calvin : Not really. No.
This Photo by Unknown Author is licensed under CC BY

4. World’s 1st
Mental
Health
Chatbot
(2018)
This Photo by Unknown Author is licensed under CC BY

5. Definitions
• AI aims to mimic human cognitive functions
• AI is built from:
• Machine Learning for structured data
• Natural Language Processing (NLP) for unstructured data
• Big Data
• petabytes (1,024 terabytes): millions of patient records
• Hard Coding
• explicit coding instructions to carry out a task
• Machine Learning
• computer use of data, algorithms, and statistical models to
perform a task without being hard-coded
• relies on patterns and inference
• Linear algebra: basis for machine learning
• Scalar: magnitude x
• Vector: magnitude and direction (x,y)
• Matrix: array of elements in row and columns
This Photo by Unknown Author is licensed under CC BY-SA-NC
This Photo by Unknown Author is licensed under CC BY-SA
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6. The road map from clinical data generation to natural language processing data enrichment, to
machine learning data analysis, to clinical decision making. EMR, electronic medical record; EP,
electrophysiological.
Fei Jiang et al. Stroke Vasc Neurol 2017;2:230-243
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All
rights reserved. No commercial use is permitted unless otherwise expressly granted.

7. Publications
of Machine
Learning in
Psychiatry
Reevaluating the Efficacy and Predictability of
Antidepressant Treatments: A Symptom Clustering
Approach. JAMA Psychiatry 2017
Multisite Prediction of 4-Week and 52-Week Treatment
Outcomes in Patients With First-Episode Psychosis: A
Machine Learning Approach. Lancet Psychiatry 2016
Predicting Response to Repetitive Transcranial Magnetic
Stimulation in Patients With Schizophrenia Using Structural
Magnetic Resonance Imaging: A Multisite Machine Learning
Analysis. Schizophrenia Bulletin 2018
Treatment Response Prediction and Individualized
Identification of First-Episode Drug-Naïve Schizophrenia
Using Brain Functional Connectivity. Molecular Psychiatry
2018

8. Use Big Data, Systems, and Algorithms to
Solve Psychiatry Problems
Big Data
Big Data Systems
Algorithms
Psychiatry AI Applications
This Photo by Unknown Author is licensed under CC BY-SA

9. Use Big Data, Systems, and Algorithms to
Solve Psychiatry Problems
Big Data
Big Data Systems
Algorithms
Psychiatry AI Applications
This Photo by Unknown Author is licensed under CC BY-SA

10. This Photo by Unknown Author is licensed under CC BY-SA

11. • $300 billion potential annual value to Healthcare
• $165 billion from clinical decision support
• $108 billion from personalized medicine and
clinical trial design
• $47 billion from fraud detection
• $9 billion from public health surveillance and
response
• $5 billion from digitization, aggregation, and
automation of patient records
• 30% of data produced worldwide is from
healthcare
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12. From Data to Wisdom
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13. Psychiatry generates lots of
different kinds of information
• Clinical history, patient
demographics, diagnosis,
procedures
(psychotherapy/ECT/TMS),
medication, lab results, and
clinical notes.
• On-body sensors and other
devices that patients wear
(activity, behavior), social media
(thoughts, mood, reactions of
social network to patient).
• Real-time data sources such as
blood pressure, temperature,
heart rate (sympathetic nervous
system and connection to
Amydala-driven fear response).
• Drug dispensing levels at acute
psychiatric inpatient units.
This Photo by Unknown Author is licensed under CC BY-SA

14. Use Big Data, Systems, and Algorithms to
Solve Psychiatry Problems
Big Data
Big Data Systems
Algorithms
Psychiatry AI Applications
This Photo by Unknown Author is licensed under CC BY-SA

15. Hadoop is the
platform that
administers
MapReduce to
process and
analyze big
data
Patient 1
Patient 2
Map
Map
MDD, 1
GAD, 1
PTSD, 1
MDD, 1
DATA

16. Hadoop is the
platform that
administers
MapReduce to
process and
analyze big
data
MDD, 1
GAD, 1
PTSD, 1
MDD, 1
MDD: 1, 1
GAD: 1
PTSD: 1
Reduce
MDD: 2
GAD: 1
PTSD: 1
MapReduce in Hadoop

17. Pros/Cons of
MapReduce
in Hadoop
• Pro: Hadoop can work with big data using MapReduce
computations
• Con: Hadoop not good for iterative computations, such as
required for machine learning
• Pro: Hadoop works well for billions of records that you want a
histogram, odds ratio, or prevalence
• Pro and Con: Hadoop uses distributed hard discs to store massive
data, but also makes it slow for iterative computations
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18. Spark is the platform
for machine learning
of Big Data
• Spark uses RAM, so it’s
faster than Hadoop
MapReduce data stored on
magnetic disks
• Spark is fast enough to
perform iterative
computations needed by
machine learning
• Spark uses distributed RAM
to handle massive
computations of Big Data
This Photo by Unknown Author is licensed under CC BY-SA

20. Use Big Data, Systems, and Algorithms to
Solve Psychiatry Problems
Big Data
Big Data Systems
Algorithms
Psychiatry AI Applications
This Photo by Unknown Author is licensed under CC BY-SA

21. Supervised
versus
Unsupervised
Learning

23. Graphical illustration of unsupervised learning, supervised learning and semisupervised
learning.
Fei Jiang et al. Stroke Vasc Neurol 2017;2:230-243
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All
rights reserved. No commercial use is permitted unless otherwise expressly granted.

24. Classification
Classification algorithm takes that input x which is in
matrix.
And for example every row in this matrix represents a
patient and every column of this matrix represent a
feature.
And the classification algorithm will map this input
feature matrix x into a set of discrete outcome y.
For example, these y vectors indicate whether the patient
had Treatment-Resistant Depression or not.
So we want to learn this function f that mapped input x
to the target outcome variable y, this is classification.
y = f(x) + b
feature
x
patient
f
outcome
y

25. Regression
Regression algorithm takes that input x which is in matrix.
And for example every row in this matrix represents a
patient and every column of this matrix represent a
feature.
And the classification algorithm will map this input
feature matrix x into a set of continuous outcome Y.
For example, these Y vectors indicate the GAF score of
the patient.
So we want to learn this function f that mapped input x
to the target outcome variable Y, this is regression.
outcomefeature
yx
patient
y = f(x) + b
f

26. Clustering
Clustering is about taking an input matrix x, again,
every row here is a Patient (P), every column is a
feature, Disease (D).
Want to learn a function, f1, that partitioned this
matrix into multiple clusters, P1, P2, and P3, and f2 to
D1, D2, and D3.
In f1, each cluster consists of set of patients and those
patients are similar to each other, and those patients
within a cluster will be different patients in a different
cluster. f2 into different disease phenotypes.
Patient
(P)
Disease (D)
P2
P3
P1
x
Patient
stratification
D1
D2
D3
Computational
Phenotyping

27. Measuring Performance: Classification
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28. Measuring Performance: Regression
• 1 indicates regression line perfectly fits data
• 0 indicates regression line does not fit data at all
R squared (coefficient of determination)
• Error between prediction and actual value
• Small for low error, large for high error
Mean Absolute Error (MAE), Mean Squared Error (MSE)

29. Measuring
Performance:
Clustering
• Rand Index (RI)
• 0 means bad clustering assignment
• 1 means perfect clustering assignment

30. Use Big Data, Systems, and Algorithms to
Solve Psychiatry Problems
Big Data
Big Data Systems
Algorithms
Psychiatry AI Applications
This Photo by Unknown Author is licensed under CC BY-SA

31. Predictive Modeling
Predictive modeling is about using historical data to build algorithms to
predict future events. For example, we can predict which treatment is
likely to work for a patient with depression.
Example: Let’s look at patients with depression who responded to
treatment.
Group A: responded in first two years of treatment.
Group B: responded between two to five years of treatment.
Group C: continued to suffer even after five years of treatment.
Predictive modeling would help improve matching patients to the right
treatment quickly, so that the late responders in group B will become
early responders in group A.
Also, this will help identify non-responders in group C quickly, so that a
new treatment can be developed for them.
Group
A
Group
B
Group
C
35% 20% 45%
Early
Responders
Predictive
Modeling:
Match to right
Rx quickly
New
Rx
Predictive
Modeling:
Match to new
Rx quickly

32. Computational
Phenotyping
The input to Computational Phenotyping is
the raw patient data.
It consist of many different sources, such as
demographic information, diagnosis,
medication, procedure, lab tests, and
clinical notes.
And phenotyping is the process of turning
the raw patient data into medical concepts
or phenotypes.
Phenotyping
Phenotypes
Demographic
Diagnosis
Medication
Procedure
Psychotherapy
Clinical Notes
Lab tests
Psychological Testing
Raw
Data

33. Patient Similarity
When treating patients, doctors often compare the current patient to past patients
they have seen, also known as case-based reasoning.
Patient similarity is about simulating the doctor's case-based reasoning using
computer algorithms.
Instead of depending on one doctor's memory, wouldn't it be nice if we can leverage
all the patient data in the entire database?
So when the patient comes in, the doctor does an examination of the patient.
Then, based on that information, we can do a similarity search through the database.
Find those potentially similar patients, then the doctor can provide some supervision
on that result to find those truly similar patients through this specific clinical context.
Then, we can group those patients based on what treatment they are taking and look
at what outcome they are getting.
Then, recommend the treatment with the best outcome to the current patient.
Patient Doctor
Patient Database

34. How to Use Patient Similarity in Clinical Practice
Guideline
available?
Similar
patients?
Ready for
RCT?
Use Patient
Similarity
Use clinical
judgment
Develop
hypotheses
Perform RCT
Use guideline
Yes
No
No
Yes
Yes
No

35. ‘Perils’ of Machine Learning
in Psychiatry
• “There have been so many fads, and we’ve discovered the
ultimate answer in psychiatry a thousand times,” Rajiv
Tandon said in an interview. “Machine learning could be
just another one.”
• The biggest challenges are sensitivity and specificity of an
algorithm’s predictive results. Ultimately, the results of
any algorithm must be replicable across different
datasets. “This is a particularly important challenge as a
medical test must apply to all potential patients or, if not,
it must be very clear for whom the medical test is
informative,” John Krystal said.
• (Psychiatric News, Oct 2, 2018)

36. Machine Learning Can Enhance Psychiatric
Practice
Inform
Machine learning
can inform
hypothesis testing
for future clinical
trials
Complement
Machine learning
and RCTs actually
complement one
another
Augment
Machine learning
not meant to
replace RCTs and
clinical guidelines
Helps
Machine learning
helps for patients
not covered by the
RCT groups and
clinical guidelines

37. Neural Networks and Deep Learning
Fei Jiang et al. Stroke Vasc Neurol 2017;2:230-243

38. • N. is a 19 year old single caucasian female, who is attending university and works part-time as a cashier for a pizza restaurant. She has a history of
recurrent depressive episodes, which starts in the late fall/early winter months, and remits during the spring. She uses light therapy every morning
for 30 minutes, as without it, she would be unable to function. Although her family doctor has recommended that she consider antidepressant
medication treatment, N. has adamantly refused, stating that it is unnatural, as she tries to ingest healthy and natural foods. When she becomes
depressed, she has symptoms of low moods, anhedonia, excessive fatigue, excessive sleep, increased appetite, carbohydrate cravings, and weight
gain.
• Her family history is significant for her mother suffering from bipolar disorder, and who dies in a car accident when N. was only 12 years old. It is
highly suspected that the car accident was a suicide. N. has refused to see a therapist, stating she does not need psychological help, and that she
can deal with her mother’s death on her own.
• After the death of her mother, she and her brother were sent to live with their estranged father and step-mother, and their infant daughter, N.’s
half-sister. She is the oldest of 3 siblings. Suffice it to say, N. did not get along well with her father and step-mother, and was jealous of all the
attention her infant sister got from the family. When N. became depressed in the winter months, she locked herself in her room, and would sleep
constantly, not contributing to the chores the family needed. When she was not sleeping, she would leave and spend days with her friends, without
any check-ins with her parents.
• Eventually,she ran away from home after her graduation from high school, and was not found until her father shamed her on Facebook that his
daughter was missing and is now a runaway. Humiliated, N. contacted her father and assured him she was safe.
• She eventually enrolled in university, and only has cursory contact with her father and siblings. Her first year in university was difficult, and she
almost failed her classes due to sleeping too much when her depression would worsen like clockwork in the fall and winter.
• When spring would come around, she would have increased energy and could work for 12 hours a day and go out with her friends at night with
only minimal sleep. She spends her summer doing lots of outdoor activities, as she likes to soak in the sun, and dreads each day after June 21st,
when the sun exposure would decrease little by little with each passing day.
• Diagnosis: Major depressive disorder, recurrent, in partial remission, with seasonal pattern. Rule-out bipolar disorder, given the history of
hypomanic symptoms, and family history of bipolar disorder. The patient had only a partial response to the current dose of light therapy.
• Treatment: 1) Will increase light therapy duration to 60 minutes every morning, from a 10,000 lux light box. Will need to monitor for any signs of
mania, as light therapy may switch someone with an underlying bipolar disorder from depression to mania. 2) If no response in 1 to 2 weeks, then
consider augmenting light therapy with cognitive behavioral therapy (CBT). 3) Recommend other helpful strategies for depression, including
exercise, meditation, yoga, and outdoor time to increase sunlight exposure. 4) Treatment with antidepressant medications is a relative
contraindication, as bipolar disorder has not been ruled out. 5) Return to clinic in 2 weeks for follow-up.

39. Extractive summarization process utilizing supervised (multiclass) classification based on the
ontology

40. Extractive summarization process utilizing supervised (multiclass) classification based on the
ontology

41. Carlo
Carandang-
Data Scientist
and Psychiatrist
Email:
carlo@karvit.ca