Andrew Ng
Overview of AI and why it is affecting healthcare
AI is the new electricity
Electrification of country transformed every major industry
Reached surprisingly clear path for AI to transform industries just like electricity 100 years ago
Almost all the value that is created through AI today (economic, etc.) comes from one type - supervised learning
Supervised learning
Learn the following mapping: Input A -> Output B
Ex: facial recognition to unlock cell phones
A = image, B = is it you?
Ex: targeted advertising
A = (ad, user), B = click
Ex: speech recognition
A = audio, B = text transcript
Lots of users, such as when searching -- but only recently has this been enabled
Ex: automated diagnosis
A = radiology image, B = diagnosis
Turns out a lot of the value creation the best opportunities is finding the creative ways to implement supervised learning
Supervised learning has been around for a long time; why is it only now taking off?
We have much more data now
X-axis: amount of data, y-axis: performance
With traditional learning algorithms, performance takes a logarithmic curve
Hence, these algorithms don't take into too much account the amount of data we have
With neural networks, and especially deeper ones, we can take greater advantage of the data
However, you need a lot of data to train these neural networks, and we only recently have this in healthcare
The rise of EHR records being publicly available partially helps fuel this
Over the last 5 years we went from having a very little data to a lot of data
Finally, the other breakthrough was the rise of very large neural networks
The rise of larger neural networks has led to higher performance with greater utilization of data
Pranav Rajpurkar
Arrhythmia Detection
Arrhythmia = irregular beating of the heart
Costs a lot to treat
Affects 100s of millions of people
Diagnosed with ECG test, which shows heart's electrical activity over time
The problem is many times if we see a small sample of the ECG, some issues may not be noticed
Ideally we want to monitor for longer periods of time
We now have the devices to do this
If we collect data for a 14-day period, this is way too much data for doctors to analyze
Would want to automate analysis of this data
Even doctors have a hard time diagnose this
There are many components that are part of a ECG waveform, and the doctor looks at these features
There are many rules but programming into a computer is futile since there's so much variation
If we look at the performance of doctors on Atrial Fibrillation, we see about 70% accuracy
Various components of a ECG waveform
In the past, machine learning relied on shallow models and little data
In this work, we use data from 30,000 unique patients
We use a Convolutional Neural Network (CNN): go from A (ECG data) to B (annotated Arrhythmia)
In a span of 5 months, we hit human performance
And in the 6th month, we beat cardiologists and reached 81% accuracy
Automation has a lot of impact
Saves patients cost of misdiagnosis
Saves doctors time
Low cost
Allows for diagnosis of arrhythmia in low-resource settings, where people may not have access to cardiologists
Could outperform board-certified physicians at Stanford
Anand Avati
Palliative Care
Providing relief from pain, rather than on curative treatments
Ex: hospice care
EHR (electronic health record) + electronic version of patient's health history
Billing, insurance claims, demographic data, clinical order data
Noisy, prone to human error (as humans input the data), missing data
Project: Deep Learning to predict whether patient will die in next 3-12 months using EHR data
Why?
80% of Americans prefer to spend final days at home if possible but only 20% do
60% of deaths occur in hospital while receiving curative treatment
Why the mismatch?
Doctors tend to be over-optimistic, work under time pressure
Palliative care teams are understaffed; expensive to proactively chart review all admitted patients for palliative care needs
A big reason is the economic incentives in the healthcare system
Goal:
Improve access to palliative care for patients
Automatically screen all patients admitted to hospital
Use deep learning model to make automated prediction and share with palliative care team
Trained a model with 18 years of data and 30,000 patients
Impact
Offline validation: AUC = 0.94
Validation in real-world:
Suggested 50 patients to palliative care; all 50 were reviewed by palliative care team and verified as legitimate
Currently undergoing pilots in Stanford hospital
Hospital board of directors are excited
Increases the quality care of patients
Morally right thing to do
Andrew Ng
Many industries are undergoing the IT revolution
Over the last 10 years, what was once stored in a piece of paper are now stored digitally
Ex: Coursera: keeps a record of educational lectures
Ex: garment industry: all purchases are stored in a database rather than filing cabinet
Results in rise of data
IT revolution -> Data -> AI revolution
The IT revolution is still going on generally
Healthcare is still undergoing IT revolution
Alison Darcy: Woebot
Epidemic of mental illness: $200B total cost to US, 58% do not receive care
Student population is acutely underserved
Huge demand for mental health support
50% of college students experience debilitating anxiety and depression
Problems are growing in prevalence and severity
Ex: Stanford CAPS: has ~2 week wait time, which is too long
Up to 75% of students who need help do not receive it
Cost, stigma, time
Created a Facebook Messenger bot, woebot
Randomized controlled trial results: control vs. woebot
High engagement: users interacted with wombat 12.12 days out of 14 day period
High satisfaction: 4.3/5
Very high acceptability: users report emotional stability
Those who talked to the chatbot relieved significant symptoms of depression
Chatbots make good therapists
Talk to bot, you may open up more than if you talk to human; slower to open up to a human; bots don't judge you
Non-human, integrated in everyday life, customized therapy
You're not bothering anyone in the middle of the night (i.e. feel less guilty to talk to a bot at 2am rather than call a clinician)
Therapy is super social important and unmet need.
Application of healthcare that impacts so many people
AI is at the stage -- specifically NLP (natural language processing) -- where bots are able to have rudimentary understanding
Healthcare is influencing the way we build software
Silicon Valley: have standard ways to develop a mobile app
PM has a wireframe, which the engineer builds
Woebot: not as important what the UI/UX looks like per se; more important what the actual content of the chatbot is
In conclusion, seen IT and AI transform several different industries
I think this is a good time to get into AI as well
A lot of this stuff is new; just implemented in the last 2 years
Q&A:
How do we train a supervised learning model when gold standard is subjective (e.g. two clinicians may not agree on a given diagnosis)
Definitely the case, so often get multiple labels for data
Pranav's project: Computer agreed with a human more often than human with human, helping indicate the model is doing something substantial
Averaging the intuition of humans
Healthcare is notorious for being difficult for clinical/regulatory approval and sustainable business model. Is this a challenge for adoptions in AI? What are ways to mitigate this?
Many healthcare entrepreneurs spend 50% innovating/50% worrying about regulation
Among entrepreneurial communities debate:
1) pursue consumerization route (Fitbit)
2) pursue regulatory approval
AI is blackbox
We are comfortable taking drugs whose biological function we don't really understand, but clinical trial shows it works
AI can be treated the same
Ethical issues in healthcare. People may not want to know with certainty particular facts (e.g. date of death with high probability)
Can do a lot of good and a lot of harm just like electricity
Challenge of labeling datasets when the process if costly (e.g. requires clinicians)
Limits availability of labeled data
We will evolve better and better processes to labeling data efficiently; crowdsourcing