Who are you and why are you a member of EHF?

My name is Eray Ozkural. I am the A.I. Head of eHealth First.

I’ve obtained my PhD in computer science from Bilkent University, Ankara, on the subject of parallel data mining, or big data. Since then, I’ve primarily worked on Artificial General İntelligence (AGI), or AGI. My specialty is transfer learning, that is, I’ve designed a long-term memory system for AGI, I’ve designed scalable, parallel general purpose-machine learning algorithms for human-level AI. I’ve also been working on deep learning and AI theory.

For my theoretical contributions to universal induction, I’ve received the Kurzweil Best AGI Idea award in 2015.

Well, why am I a member of EHF? First of all, it is a great team with prominent professionals from the longevity and bioinformatics research community, therefore they know which are the forthcoming problems and how to attack them. Second, it is the first application oriented AI platform on the blockchain, machine learning has the potential to revolutionize healthcare and AI in general can make a huge impact in healthcare as all sorts of predictions and analyses in medicine can be automated. Combined with the AI analyses of millions of research papers, we can make medicine more effective, personalized and preventive. Third, owing to the multitude of data types in medicine, and the data’s higher than usual complexity, the next-generation machine learning algorithms may be particularly helpful, which is a formidable challenge for AGI research. And fourth, and finally, I believe, well, I know that we can defeat biological mortality one day, careful but sure steps are being taken in longevity research, which may eventually help us naturally revitalize by reprogramming our cells to a more youthful state. Along the way, we will need ever more careful monitoring and treatment of our more vital bodies, and AI based automation of routine but important health care tasks will help greatly.

What is A.I. and M.L.?

AI is making computers perform tasks that require intelligence, such as solving a math problem, or understanding a piece of text. There are many definitions of intelligence in the AI literature.

Thinking or acting like human, or thinking or acting rationally, like Mr. Spock. AI spans the whole gamut of our mental faculties such as learning, perception, reasoning, planning, reflecting, language and behavior. There are many approaches in AI, such as symbolic, logical, Bayesian, neural, and evolutionary. On the other hand, machine learning is making models that fit the data. It helps us solve pattern recognition, perception problems, like in vision and speech recognition; statistical problems like classifying or clustering the data, predicting a variable, such as predicting stock movements.

Neural networks, and in particular an advanced form of it we call deep learning has shown tremendous success, matching human level in image recognition, and provided new capabilities like style transfer. I think that most AI problems can be solved with machine learning. Deep learning has vastly improved speech recognition, synthesis, language understanding. We can use deep learning to reason, analogize, map questions to answers, and imagine.

We have thus already started applying machine learning to solve many kinds of cognitive problems. Either deep learning or AGI methods will likely soon help us achieve human-level across all tasks.

Machine learning can also be used to design autonomous or semi-autonomous robots that can adapt to the environment. A popular kind of machine learning application is automating labor such as trading, and legal search.

What is N.L.P.?

NLP stands for natural language processing, and it is a subfield of AI where we solve linguistic problems with an AI approach. It may be considered as a fusion of linguistics and computer science, and we can imagine it as a computer taking on the tasks of a linguist as he analyzes the order of words, meaning, and uses of linguistic expressions, speech, and discourse.

For instance, there are algorithms in NLP that break down a sentence into its tree-like grammatical structure, find the classes and referents of the words, such as, is it a noun, is it a proper name, is it a verb, what do these words refer to, translate a sentence to another language with statistical methods using a large number of examples, construct a logical representation of a sentence’s meaning out of which questions may be answered or stored in a database, generate a sentence from a meaningful representation, carry out dialogues with humans, recognize and synthesize speech.

So, you can see how NLP is related to tools we use everyday like Google Search, and Siri, but the possibilities in NLP are actually much larger. You’ve already seen Google Translate which uses a kind of machine learning to translate, but these tools can fail in some ways so there is a lot of room for improvement.

Nowadays, we’re using a lot of deep learning, which is a field of machine learning that uses brain-like neural network architectures to deal with text and speech. Fantastic results have been achieved with speech recognition and synthesis, the results are mostly indistinguishable from human. These new methods can also perform a great textual analysis, they can translate among many languages, we can detect intentions, that’s how Alexa works, and chatbots are already commonplace.

These are all NLP applications where we instruct computers to analyze and respond to text and speech with algorithms.

How A.I. can be applied to health care?

AI applications can be highly beneficial for healthcare in general to the extent that it can revolutionise the entire healthcare system.

Now, imagine that there are a lot of mobile sensors on your body while you walk around, data is being collected. Smartphones already have some biomedical sensors on them, but imagine that we can cram a lot more sensors onto these devices, and that you can have a mobile unit at your home where you can have even more sensors on it. Your healthcare records would be on the cloud or the EHF platform.

An AI system can have access to all this data, to make a quick diagnosis of conditions that require a more detailed examination. AI systems can use machine learning to correlate medical histories with various sensor readings, and learn to diagnose patients. With the records of millions of patients, maybe the system will be able to make even better predictions than the medical doctors. That would be one of the primary applications.

AI can also look at medical images, it could spot tumors where the physician might miss it, which has already been shown in AI literature. Not everyone has access to these apps yet, but this is of course what the EHF platform is about.

The automated diagnosis would work like a filter for physicians, and we would use machine learning to apply preventive medicine right at your home without requiring you to visit the hospital. The entire system would be revamped so that instead of you making appointments and all this bureaucracy, much of this would be automated, this system would have access to all your medical history, your medical imagery, all of the sensors, and the extensive data would be used to make predictions about your health.

The system would automatically forward you to a clinic or a lab, and it could also make recommendations, so you can see how this changes everything from the ground up. A lot of mundane tasks would be automated, and the physician would really have to deal with cases where his expertise is really required, enhancing overall healthcare quality. Maybe, some of physicians’ tasks would also be automated, because, after all, what the medical doctor does is a kind of a detective work, right?

It’s problem solving, and computers can be very good at problem solving. Now, we don’t claim they are as good currently, but you’ve heard about the IBM Watson system used in healthcare, and that’s using relatively simpler methods rather than the most advanced AGI or deep learning methods, so you can see there is a lot of room for improvement there, and we’re hoping this can go a long way.

Another application of AI in healthcare is Natural Language Processing to work on medical text. At EHF, we’re focusing on what we call medical meta-analysis, which is the task of analyzing medical texts, digital articles that rank in millions. This is a treasure trove that no physician has the time to keep up with. It would take immense effort just to keep tabs on the latest research in a narrow field.

Now, imagine there is a system, like EHF, that will take the burden from the medical professional, and ease the investigation. Remember, it’s like detective work, so we have some clues that we collect from the patient, from his reports, from the lab work, from the wearable biomedical devices, the medical history, all of this data are clues, and then we look at this stuff, and we try to find the culprit.

I can be very useful here, because there is a lot of information about such cases in the medical literature, it can find the culprit based on clues, forecast the patient’s course, and even suggest an intervention. AI & machine learning can therefore be used for diagnosis, but also for prognosis, and any stage of medical treatment. Combined with the biomedical literature knowledge, these capabilities are greatly expanded.

What about eHealth First’s approach to A.I.?

We’re looking at a few possible applications, so I’m going to try to explain what we intend to accomplish.

First is medical meta-analysis, looking through a good number of medical articles, and solving some medical problems using this data. This will have a conversational interface for ordinary users, and one that is intended for medical professionals where they can specify more detailed queries. This will help people to make an automated diagnosis based on their medical history and lab work and other sensor data, so we try to learn these things from medical articles, and we find the relevant publications and recommended course of action, basically, doing a lot of what a medical doctor does, of course in a more automated manner.

At first, we will make use of existing research, and integrate them into our architecture in a practical manner. And then, we will be adding a lot of things like deep learning, and we will automate the language analysis using deep learning approach, and this will make it a lot more adaptable, and a lot more powerful actually.

Deep Learning has started to take over a lot of things in natural language processing, so we’re trying to follow this technology, and we think this will work really well here. We can actually make a conversational engine like Alexa or Siri but one that’s a medical expert that knows all of those articles at a sufficient level. And we’re going to use the medical standards and conventions for understanding the text, so we can ensure sufficient understanding to answer some challenging queries.

You can ask about how to treat a general condition, or you can input your personalized data, lab work, sensor readings, medical images and it will infer what your condition is, it will diagnose you, and recommend a medical intervention much like what a doctor does. So, that’s the main goal.

Medical image analysis using deep learning will also be achieved by the same system. And, in a later stage, we will also integrate genomics data through deep learning, so it will be able to look at your genome sequence, proteomics, and other *omics data, so these are more advanced stuff, and more expensive stuff, but this is where AI can be really powerful, because personalizing treatment based on your genome is a more complex but potentially much more powerful problem. And then, it will also be able to look at medical images, use deep learning on medical images, to detect conditions like tumors or lesions.

So, these are the three basic planned capabilities of the EHF AI Platform. We think we can do more, for instance, I’m considering a longevity application where we will recommend a longevity combination therapy, personalized, for a specific patient, because every patient will have different biomarkers to improve, and these personalized recommendations can thus be very useful.

These are some of the things we are going to do, and we are very excited about them!

Why use biomedical scientific publications for EHF development?

There are millions of biomedical publications, thankfully collated in some online databases, biomedical researchers are very generous in that regard, a lot of articles are open to the public, although some are still behind a paywall. We can access those archives, basically.

Now, a human, or a bunch of humans, say you gathered one thousand people, how much time does it take to go through and summarize the findings and translate to some machine readable format?

For a single technical paper, maybe for an expert, it will take only a few hours, but there are millions of articles, so even with such an extensive work force, you would have to do this over and over again, because there are always new papers, and maybe the format you use isn’t enough, so you have to do it all over again.

This is obviously something that’s apt for automation, it calls for it, and the best kind of automation we have is AI & machine learning. So we’re going to leverage the power of deep learning to do this task.

We want the machine to extract every relevant bit of information from this treasure trove, and use this for preventive medicine, for recommending alternative therapies, helping medical professionals come up with better cures, there is a lot of value in this without doubt.

www.ehfirst.io