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The importance of providing Labels and Structure to your Healthcare Data towards AI Success

by Srihari Yamanoor

The worn cliche: Data is the new oil!

Your organization may be big. It might be small. It might be a mature business, with an approved device pipeline, or, it might be an early stage one. Your organization might have any number of adjectives describing its nature, size and characteristics. The commonality, is that, if it is a healthcare organization (or any type of organization, though we are focused on healthcare here), it has data, that can be classified and categorized in several ways, and can indicate to a point in the immediate or distant future, where it could have significant impact and ROI depending on how it is analyzed and applied in alignment with your organizational goals.

Thus, whether the cliche seems tired to you or not, there is a lot of truth to the statement that your data can be useful in several ways in the future – it could represent inherent value for investors, it could help with forensics and root cause analysis, in new designs, in optimizing performance, manufacturing, service, competitiveness and possibly in many different ways you haven’t yet imagined! This then indicates you should have a sound data strategy.

Storage is Tactical, Not Strategic

If you think, “well, our strategy is to acquire the data, store it in the cloud and back up for when we are ready for AI”, you are confusing tact with strategy. Some might argue that the act of storing data is even more banal than being tact. There might be truth to that, but let us make sure that you do have sound plans to acquire and store data with back up plans that are better than what MySpace appears to have been employing (for the Rip Van Winkles out there, I will provide a link of the recent disaster that MySpace was engaged in – essentially losing approximately a decade’s worth of its users’ music)!

So, your strategy for data needs to be a lot more than strategy.

However, do not take storage lightly! In my career, I have seen organizations with weak leadership that engaged in such stupid ways (and this is putting it kindly) of collecting and archiving data, that they lost critical and crucial, historic information relating to verification and validation!!

So, while it might seem simple, make sure your organization has well tested and trusted methods of collecting and archiving data, backed up appropriately and retrieved securely, with reasonable ease. At a minimum, you should consult with Data Scientists, if your size and budget does not permit developing a Data Science team in-house. It might be appropriate for the Quality and/or Regulatory functions within a small organization to bear the responsibility for this. For device and drug companies, regulations already require much of this, and yet you would be surprised how easy it is for organizations to miss the mark on this.

Labeling

There are several definitions for data labeling and definition of structure, as it pertains to data. I will leave this as a basic exercise for you, the reader, but I am sure, you have a commonsense understanding of what the terms allude to, and so let us use that as a yardstick to get to the thrust of this blog, rather than reducing it to pedantic and semantic exercises. Data labeling alludes to the fact that you mark data in terms of body temperature, instrument temperature, body weight, age, sex, heart rate and on and on. For anyone who has done even the simple exercise of bench top testing, it will be obvious that you already have to take a lot of care in labeling data, or the losses can be quite catastrophic.

Labeling tends to be the most expensive and time consuming exercise in preparing your data for Machine Learning, as anyone who has attended a talk by me on this topic has been clearly warned before!

Labeling should be understood in broad terms that go beyond merely the titles of columns and rows on a spreadsheet. For instance, being able to pull several spreadsheets, and readily understand which data came from acute or chronic studies, bench-top or human trials, real vs. simulated data must all be considered. Confuse these, and your machine learning might be trained with the wrong data and the results can be quite undesirable.

Data Labeling should also be very clear at every level and this cannot be stressed enough! If you have data collected by 4 thermocouples, two years from now, when someone pulls up the data for instance, they should be able to identify exactly where the data came!

In effect, you should establish a discipline of rigorous labeling for data, from the macro levels, going into the individual levels, through your SOPs, Work Instructions, Forms and make this a part of organizational culture.

Structuring Data

Data can be classified into Structured Data and Unstructured Data. Structured Data is reflected by the type of data discussed above – data obtained manually, through machines automatically, by programming, through logs, etc. (There is a note about data logs I will make in a moment). Unstructured Data on the other hand, could be data coming from sources such as online customer reviews (if you think this is not for you, just search for reviews on IUDs or Heart Burn Medications on the internet and you are in for a surprise), physician or nurse feedback, complaint information, procedure videos and other sources. Depending on the device, drug, biologic, app or the combinations thereof, your unstructured data can take many forms, and you should develop a very deep understanding of these sources, and how to collect, and structure this data as much as possible.

While, we all assume that some day, in the near or far future, depending on the various predictions, Artificial General Intelligence will allow us to completely skip data labeling (Neural Networks can do this to an extent currently), and analyze data, providing insights. However, there are limitations with current Machine Learning and Deep Learning Approaches, and you are better off providing as much structure to your data as possible.

For instance, if you are capturing procedure videos, whether on models or on humans, you might benefit from using fiducial markers for instance. Such markers are common in imaging systems already. Your device could also have identification markers on it, that show position, location etc., making human and machine understanding fast and easy. This can be very helpful when dealing with explainability and machine training. This could essentially convert the status of your data from ‘unstructured‘ to ‘semi-structured‘ or, if you can further template and refine it, ‘structured‘. Of course, you might hit a point where you get diminishing returns, or, as is the case, the machine can handle semi-structured data well enough.

Identifying the sources of your unstructured data, and striving to provide structure to your data, early, and strategically, will ensure that Data Analysis and Machine Learning Projects will be less expensive and better facilitated.

A note on Machine Logging

I was once involved in a project where the capital equipment logged critical data such as currents, voltages and other data associated with procedures. Various regulations require this, and for Failure Analysis, Adverse Event Investigation, etc. this is generally very essential. The fundamental problem with this (and others I have seen) particular data logging was the lack of foresight and poor implementation. Starting from the very labeling of the log files, to the time stamps, to the data collection frequency and various other features, it was a complete mess. The data had to be pulled from the logs into a spreadsheet program, and a number of routines had to be run on it, to make it passably useful. I used to wonder how a diligent auditor would take to the cumbersome and questionable logging, if he or she were to stumble on it!

Worse, the project leader prided himself in thinking that he had created an innovation in translating the gibberish into a-little-less-gibberish through the scripts! That sort of paucity in planning and respect for data will not help you, if you are to succeed.

The Bigger Picture

I want to make it clear. This is not just exhortation to make machine logs readily readable. And yes, your software team MUST do this. The point here is awareness, education, discipline and diligence towards data. Your organization needs to strive towards all of these aspects – and two more, which follow in the next section. It is not just leadership, but it is your entire organization that needs to focus on this for success.

Patient Privacy and Data Security

Increasingly, ranging from Government Organizations to Data Silo Owners and even other third parties, several organizations are admitting to failures in securing data, exposing data belonging to stakeholders. As you can imagine, exposing your patients’ data, erodes trust and is unforgivable, if the root cause for this is carelessness and sloppy, outdated practices.

Even when patient privacy is not involved, losing data of any other kind, such as test data, designs, manufacturing data etc. can be catastrophic to your competitiveness, and can make or break your organization.

Efforts to secure your data and protect your patients can never cease, and there can never be complacency in any of these areas!

Conclusion

The larger conclusion I want you to draw from my post here is this: you need a Data Strategy. Within that strategy lie elements such as labeling, structuring, security and privacy, none of which can be ignored. Your organizational members need to be educated of the importance of your strategy and they must have a healthy respect to help formulate, improve and maintain the strategy and discipline it will take you to succeed.

References:

  1. Tactic Vs. Strategy: https://en.wikipedia.org/wiki/Tactic_(method)
  2. Image of Tags: https://www.pexels.com/photo/several-assorted-color-tags-697059/
  3. Image of Book Stacks: https://www.pexels.com/photo/pile-of-books-on-gray-metal-rack-1853836/
  4. Image of Escalators: https://www.pexels.com/photo/building-escalator-1769356/
  5. Image of Labeled Beans: https://www.pexels.com/photo/assorted-beans-in-white-sacks-1024005/
  6. Image of Laptop: https://www.pexels.com/photo/low-angle-view-of-human-representation-of-grass-296085/
  7. Data Loss at MySpace (An NY Times Subscription may be required): https://www.nytimes.com/2019/03/19/business/myspace-user-data.html

Personalized Medicine might be the true ticket for AI’s march on Healthcare

by Srihari Yamanoor

I came across this interesting piece on siliconANGLE  focused on how big data is coming to cardiovascular medicine, via a precision medicine initiative. I found the read to be fascinating, and I recommend you read the article, provided in the references section below.

In summary, the American Heart Association (AHA) is collaborating with Amazon Web Services to build a data analytics platform. Details are thin, so I am unsure of how much is hype and how much is reality. I assume, as time progresses, it will indeed turn out to be very useful. The article describes the platform as a “marketplace” (yes, concerning, in more ways than one) for various forms of patient data:

  1. Personal Data
  2. Clinical Trial Data
  3. Pharmaceutical Data
  4. Hospital Data, and apparently, other data.

The goal here is to use this to personalize treatments for patients. Read more in the article. I believe that personalized medicine and AI share a deep connection. I want to share some of my own thoughts of how such efforts could give way to lasting influence by AI on healthcare:

  1. Personalized medicine needs knowledge, which comes through data. Similarly, learning requires data. Data helps you glean patterns, which is what learning is. Thus, AI and Personalized medicine are the two snakes of Caduceus, which in this case is the data itself. Thus, efforts to bring about personalized medicine should be expanded vastly.
  2. Knowledge is supreme. When it comes to personalized medicine and such, you hear all this hype of n=1. Unfortunately, for medicine to be effective, first you need large quantities of data, and with that the ability to glean actionable information from the data, to understand diseases, treatments, and their effectiveness. Whether you see this on a gross scale, or all the way down to a Single Nucleotide Polymorphism (SNP), this type of knowledge needs to be the horse in front of the cart. Without actionability, the patterns recognized through the data might not be useful. This knowledge doesn’t exist, so the hype cycle might cause enough damage and reduce funding levels later on. Realistic expectations ought to be set so that this doesn’t happen.
  3. There are two problems with healthcare data that come to the fore. First, we don’t really know that is being collected is appropriate. There is a lot of day dreaming about genetic data, racial data, physiological parameters (one company claims to be measuring 300 parameters!)  and other measurands and the hope that these will somehow magically transform themselves into diagnoses and treatments. With enough research, they will, but this will take expensive effort and time. Low hanging fruits such as breast cancer diagnoses, etc., are pushing people to imagine that the same level of rapid success will be seen throughout the healthcare continuum. There is no way to know if this will be the case.
  4. The second problem with healthcare data is about ownership and walls around the data. For example, will AHA share the data with the general public? If not, why not? Terms like “marketplace” give me pause. They might very well be just terms used to explain things colloquially. On the other hand, it is important to think about the issue of data sitting behind paywalls, or blocked of with HIPAA as a convenient excuse (more posts to come on all this). Data Democracy is a critical need for the progress of medicine and for Artificial Intelligence, eventually.
  5. What if the data is not free? What about the insights? Do we suddenly now start paying for the insights? Or, would CMS start reimbursing Doctors for personalized medicine? For the use of AI tools? There are several questions here, and only time will answer them.

Conclusion: 

Data will give us clinical insights. These insights will eventually fragment into meaningful personalized healthcare. The same data will lead to even more advances with AI. However, unanswered questions over data ownership and access remain.

 

References:

  1. https://siliconangle.com/blog/2018/01/22/can-precision-medicine-break-chokehold-on-healthcare-big-data-reinvent-womenintech/
  2. Image, Courtesy Pexels: https://www.pexels.com/photo/interior-of-office-building-325229/

 

Machine Learning shows promise in Dementia prediction

by Srihari Yamanoor

Rubik's Cube

I was flipping through some archives, and found this Scope (a Stanford University, School of Medicine Publication) article that delineates a machine learning tool (link below, Scope calls it AI, the authors of the tool, in their paper, also linked below, rightfully categorize it as ML, a subset of AI as we generally describe it). I always love when you have access to the paper linked to a study. It always makes things easy.

So, the folks at McGill, trained an ML system using PET scans from people who demonstrate symptoms of mild cognitive impairment, to see who among them would develop Alzheimer’s, given that not all of them do. They taught the system to focus on the elevated protein expression in specific brain regions to train and make predictions.

Used on an independent set, the tool had an 84% prediction accuracy of dementia progression. Read more in the paper. I want to share a few thoughts below.

  1. I think tools like this will become the norm over time. However, right now, they lack the kind of standardization and maturity required for integration into clinical practice. I don’t mean to state that in a negative sense. Such efforts take time, effort and funding, of course.
  2. An 84% percent efficiency is not enough, not even for a supporting tool, not even when humans are completely in charge. This is also achieved through training with large data sets, the use of better algorithms and other improvement methodologies. This could also use some standardization, that can then be spread to all ML, DL and AI tools, which use imaging for diagnostics in healthcare.
  3. The future should consist of such tools passively (and when necessary, actively) siphoning your imaging and other data off your EHRs, and then parsing them to see if predictions can be made. This however, requires more groups such as the ADNI (Alzheimer’s Disease Neuroimaging Initiative), from whose participants the imaging and other data was used, collaborations from hospitals, insurance companies and governments.
  4. To improve diagnosis across ages, sexes, races and other discriminating factors, global co-operation would be required.
  5. Of course, we need to take various types of data, ranging from imaging to genetics, to epigenetics and other sources to make diagnosis quite efficient. Perhaps, this combination is one way to get around the 84% efficiency in this tool, till a time comes when imaging alone produces better results. At that point, say you make predictions based on imaging, genetics, lifestyle and other factors, and they all chime in. You can probably use whatever interventions are available (this is a key factor, missing in all the hype about machine learning. You learn something, yes, but what do you DO?) to delay, treat and cure patients.

When I find more such interesting studies, I will share similar and other thoughts on Machine Learning, Deep Learning and AI, and their impact on Healthcare.

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References: 

  1. The Stanford SCOPE Article: http://scopeblog.stanford.edu/2017/08/29/artificial-intelligence-can-help-predict-who-will-develop-dementia-a-new-study-finds/?imm_mid=0f5d75&cmp=em-data-na-na-newsltr_ai_20170904
  2. The McGill Paper: http://www.neurobiologyofaging.org/article/S0197-4580(17)30229-4/fulltext
  3. Image courtesy, Pexels: https://www.pexels.com/photo/brain-color-colorful-cube-19677/

Slides – How Artificial Intelligence is Changing Medical Devices

by Srihari Yamanoor

The main goal of this site is to air my thoughts on how new technologies and paradigms impact the practice of medicine. In relation to this, I made a presentation, just yesterday, December 6, 2017, on the topic of Artificial Intelligence, and the growing influence of this discipline on the field of medical devices.

The slides will be available to download this through the site.

I do have to caution you. I do not present a lot of text in the slides. I have written some notes on the slides, but together, they still will not be enough to understand all of what I spoke about.

However, do not worry. I will blog about each slide, concept and use-case presented on this presentation over the next few weeks and months. Subscribe for updates and stay tuned! In addition, if you feel you have a set of burning questions and need them answered as soon as possible, do feel free to contact me and I will answer them as early as possible.

The download link: https://drive.google.com/file/d/12o1DHzJ2f_jNJi8MzZFPqhOwydVoDU52/view?usp=sharing