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Deep Learning on Healthcare (1) - Duration: 14:55.
Hello, everyone.
My name is Jihoon Jeong.
Creator of this youtube channel "Information and Intelligence".
Nowadays, more and more people from outside Korea is visiting my youtube channel, so that
I decided try to start english lecture.
This is the first try … so that I'm a little bit nervous.
For the Korean listeners, I'll post Korean subtitle for this lecture and I want you to
know that I'll lecture in Korean, too.
Don't be too disappointed.
I want to introduce myself first.
I graduated Hanyang University Medical College and I've got Medical Doctor degree there.
And I started my master degree for public health at Seoul National University.
At that time, my master thesis title was "Patient Satisfaction Analysis and Quality Improvement in Hospital Management".
I used Case Based Reasoning and Decision Trees such as C4.5 and CART, association rule called
a priori for analyzing the question & answers.
Yes, it is Good Old Fashioned AI technologies.
But at that time that was working quite well.
After that, I started my PhD degree at USC on biomedical engineering.
I majored in optics and my PhD research theme was "Multi-Mode Optical Imaging".
The key issues of my research was managing N-dimensional big image data to make human perceptible efficiently.
So most of my research was focused on dimensionality reduction using PCA, ICA, etc and unsupervised
learning such as K-means clustering algorithm and smart signature selection processes with embedded database.
After my PhD period, I came back to Korea and worked for the hospitals and universities.
Currently, I'm senior teaching fellow of Kyung Hee Cyber University, Media and Communication major.
I also had a position at KAIST Graduate School of Culture Technology as adjust professor.
At that time, my major interests was VR and AR, smart object and space and Human-Computer Interaction.
I wrote many books in Korea on various themes such as computer programming, trends and future
scenarios and nowadays I'm writing SF novel at CoinDesk Korea.
I've also worked for many Korean companies as an advisor.
Samsung Electronics, NHN, LG Electronics, Hyundai Auto Company are some of them.
As an angel investor, I've invested more than 50 startup companies so far.
Now, I co-founded two angel investing and startup accelerator companies BigBang Angels
and Digital Healthcare Partners and also working for those companies as managing partner and
partner respectively.
But, maybe this is much more popular than who I am in English speaking people.
Have you seen this picture?
When there is a ranking introducing laziest guy page this is almost always introduced.
Actually this is my son.
I uploaded this picture at twitter at that time and that was popular.
So, I am the father of world laziest boy.
OK.
now we need to start lecture.
I've picked up my first lecture theme on "deep learning on healthcare".
Actually, this lecture was given to Google, Sloan School at MIT, Center for Data Science
at NYU and Massachusetts General Hospital.
And also I spoke at Nvidia GTC Korea last year on this themes.
This is more like MBA style lecture based on my experiences especially on my investment
portfolio companies.
I'll try to explain what is the reality and myths when you guys try to do something
using deep learning on healthcare.
That is totally different stories from just research.
This is quite long lecture for just an online lecture, so that I'll divide this lecture
into several episodes time about 10 to 15 min or slightly longer.
Let's start.
Firstly, I want to explain "What Deep Learning can do for Healthcare?".
There are 4 different stake holders, patient, medicine (hospital or doctors), public and
pharmaceutical companies.
Each of them has different needs and restrictions.
For patient and medicine, machine learning can help to understand physiological changes
over time.
We can estimate what was going on based on the health record and lab, medical image data.
And, we can forecast progression or onset of the disease.
This is called prognosis and estimating prognosis after the diagnosis and/or treatment intervention
is very important in medicine and also for the patient.
Finally, we expect personalising treatment strategies in near future based on genetic,
health and life-style data.
Oh, I forgot to thank Danielle Belgrave for adopting this amazing slides and items.
I'll use her material in next slides, too.
For population health or for the public, machine learning can do more complex job.
It can help to elucidate average effects and deviations from average effects.
For the public people, it is very important to know what's the average, since it is
related with cost/efficiency, but it is also important to know when we can find out deviations
from average effects.
Based on the machine learning results, decision makers can do better decision.
So that policy recommendation is another benefit using deep learning.
Health education.
Nowadays, may mobile app or internet service can do health education for the patient.
If the app understand patient situations and data better, their education will be much
more personalized and effective.
Outreach.
As you know, medical service is very expensive.
So that many patients in the world to visit doctor's office.
But, if machine learning agent can help the people to decide proper decision with no cost
or very little cost, it will help to outreach poorer people.
One of the key issues in public health is finding the source of disease and cause.
Prevention is the most important issues.
We expect machine learning also can help these issues.
Finally, machine learning also can decrease the healthcare inequalities.
For pharmaceutical industry, machine learning also can do many things.
Typically, this is the pipeline of developing new drugs.
Start from ideas, basic research including animal studies are performed and there are
typically 3 phases of clinical trials.
After that we need to get approval for this new drug from regulatory agency.
And this is not the end.
we need to know and monitor this drug is working well and what will be the side effects.
We believe that machine learning can be helpful for every pipeline processes.
This is the diagram from CBInsight.
Every year they are publishing quite good report on AI and healthcare industries.
I want to show you this diagram because we need to know really many areas exist in healthcare industry.
This is the field that many companies and startups need to collaborate to make some progresses.
It is totally different from consumer internet business.
this is not covering every details AI healthcare.
But as you can see, many medical imaging & diagnosis startups were created and many of them are
doing very well.
One of them, Lunit here, is my investment portfolio.
And there are several startups dealing with mental health and virtual assistants.
Some of the companies are using wearables to promote health using AI technologies.
In the hospital, already many companies are actively working for patient care and hospital
management solutions based on machine learning.
Some of them are more focused on emergency room and surgery.
Nutrition and lifestyle management, monitoring is also very important for wellness.
And nowadays, more research based deep learning companies and patient data & risk analytics
are growing.
This is the diagram from Lunit's oncology brochure.
I like this diagram very much.
We are experiencing another paradigm shift of the medicine.
Before 1990s, medicine was just like art.
Every doctor has their own opinion and since they have their license and authority, it
is very difficult to challenge their decision.
Education was done just like master-apprentice relationships.
It changed in 1990s, more and more insurance system govern the medicine, they requested
to make some guidelines for payment and right decision.
To deal with this request, evidence-based medicine is now de facto standard.
Every medical decision should be made with evidence and such a guideline needs to be
made based on very strict scientific scrutiny.
It made medicine totally different animal.
Many inexperience doctor was gone and average quality of the medicine was much better than before.
But, there are also trade-offs.
Making evidence is very high cost and time-consuming job.
Even though you've got some evidence when you are practicing, it takes very long time
and cost to change previous guideline.
It is easily take 10 years to 20 years.
Why this process is taking so long time and paying big cost?
Because it depends on many doctors agreements and they are get together once a year and
always conservative position are majority without clear evidence.
This has very big problem.
Let's say one doctor find out "this is wrong treatment and that is better" and
it takes 20 years to agree everybody and it is now guideline for payment from insurance
system.
Is it OK?
Well, through those 20 years … Every patient got deprived of better treatment options.
I think this is not ethically acceptable.
We need to overcome this disadvantage for evidence based medicine.
Fortunately, I think machine learning and deep learning can alleviate this problem.
We can now access to large-scale digital data in hospitals and using deep learning technologies
we can make many evidences with the support by deep neural networks.
This kind of accomplishment need to be supported by medical data.
So we can say it as "Data-Driven Medicine".
I think we are now entering totally new medical paradigm, data-driven medicine.
Under the graph, this is the bar that representing the degree of deep learning software's accomplishment.
We can now detect the abnormalities 10 out of 10 doctors can also do.
It is called 'Easy'.
If 50% of experts can miss and AI can detect, it can be called 'Intermediate' task.
If 1 out of 10 doctors can detect, but AI can do, then it is 'Hard' task.
If no human experts can detect the abnormalities and AI can, it will be very "Challenging"
task.
But, already, some of the project are entering this tasks.
Finally, AI can do more than detection.
It will translate clinical implications and provide unprecedented insight.
Then, we will be at the medicine with totally new paradigm.
Still long way to go, but we can make the goal like this.
OK.
This is the 1st lecture.
I hope many researchers and entrepreneurs to innovate medicine with deep learning to
like this lecture.
I'll be back in next week with a subject for the types of medical data and different
approaches for them.
Thank you very much for listening this lecture.
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freebitco.in Multiply BTC Tricks Stop Loss | How to Remove re Captcha 2019 Urdu/Hindi - Duration: 6:27.
freebitco.in Multiply BTC Tricks Stop Loss | How to Remove re Captcha 2019 Urdu/Hindi
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