SDS PODCAST EPISODE 13 WITH DAMIAN R MINGLE...Kirill: This is episode number 13, with Chief Data Scientist at WPC Healthcare, Damian Mingle. (background music plays) Welcome to the

Show Notes: http://www.superdatascience.com/13 1

SDS PODCAST EPISODE 13

WITH DAMIAN R MINGLE


Kirill: This is episode number 13, with Chief Data Scientist at WPC Healthcare, Damian Mingle.

(background music plays)

Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week, we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.


Hello and welcome to the SuperDataScience podcast. Today we have a unique episode because we have a very special guest, Damian Mingle. Damian is the Chief Data Scientist at WPC Healthcare, but not only that, he's also a speaker and author and he has been ranked in the top 1% of data scientists across the whole world by Kaggle. That's right, today we have one of the top 1% of data scientists in the world on our show. How great is that!

But is even more important is that Damian in his day to day role uses data science to save people's lives. In this podcast episode, we will discuss a case study where Damian and his team came up with a model that can predict sepsis even before people see a doctor. And that is a life changing thing that data science can do. Because when people come into the emergency room, if they happen to have sepsis, every hour counts. People can die very quickly from sepsis, and sometimes in emergency rooms, you have to wait for a long time. And therefore Damian's data science model literally saves lives.


And on top of that case study and other case studies which we cover off in this episode, we talk about many other interesting things. We talk about how Damian builds models with 95% accuracy rates, how to use ensemble methods, how to combine quantitative data science investigations and analytics with qualitative research findings of doctors and other medical practitioners. We'll talk about how to combine quantitative data science analytics and qualitative domain knowledge to come up with very, very powerful models. We'll talk about the tools that Damian uses, he'll give us his opinion on Python versus R, and also Damian will share with you his vision for the future of data science which I personally found very eye opening. And it will give you some ideas of where you can focus your career next.

And I can't wait for you to listen to this super high profile, yet not complicated, episode. Damian really went that extra mile to break down the complex and make it simple. And without further ado, I bring to you Damian Mingle, Chief Data Scientist at WPC Healthcare.


Hello everybody, welcome to the SuperDataScience podcast. Today you won't believe who is on the show with me. I have a very special guest, Damian Mingle from WPC Healthcare in Tennessee. Hello Damian, how are you today?

Damian: Hey there, how are you? I'm doing well, thank you.

Kirill: Thank you very much. So for those of you who don't know, Damian is the Chief Data Scientist at WPC Healthcare, which is an organisation which combines data and healthcare. And also, Damian is ranked in the top 1% globally by Kaggle as a data scientist. Damian, very great to


have you on the show today. Could you tell us a little bit about what WPC Healthcare is all about?

Damian: Sure, absolutely. At the end of the day, in healthcare, we are interested mostly in assisting in the clinical side of the house, financial side and the operational side. So it's really more in the comprehensive aspect of that, so not necessarily just one component of those three, but actually try to bring all that together, and we do that using data integration and data science.

Kirill: Very interesting. And so is this just a medical facility that treats patients? So are you more of a B2C company? Or do you work with other clinics as well, and you supply the data to other clinics?

Damian: Right, that's a good question. So we actually work with hospitals, or acute setting, and then the post acute setting. So kind of the sickest of the sick, and specifically skilled nursing facilities, things like that. We also work with groups that bundle services together, like an anaesthesiologist group, maybe. So we do work in those areas. And we've been seeing, mainly on the healthcare provider side, however over the years, it's been increasingly becoming more interesting on the payer side. So the insurance, health insurance, has it become more interested in our approach, and how we're solving all of these healthcare problems.

Kirill: Oh, very interesting. How insurance would be interested to get more data to create their policies more correctly, I would imagine. What about your role? What is your role as the Chief Data Scientist at WPC Healthcare?

Damian: It's kind of interesting, I kind of wear all hats. And that's ok with me. I mean, I'll do everything from creating


visualisations, trying to basically build context for an end user. I might create targets, so it might be that we're trying to blend some data sets together and I kind of get into ok, this is how we would create this as a variable so we can create a model around it. I do the modelling aspect. And also, more and more these days, I'm crafting write ups, doing presentations, or publications, and it probably has a lot to do with the amount of time that I'm spending more and more in front of the client. So we've had to leverage technology quite a bit so that I can be kicking off models in the morning, letting them compute while I'm in a meeting, and then come back and reviewing results, and that sort of thing. That's really my day to day. We have a few people on our team, so PhDs and such, that contribute wildly to our success, and it's always fun to kind of get trapped in a room for about an hour and just have a good think tank kind of conversation. So that's kind of my role here, which is in charge of really trying to extract knowledge out of the data for our clients.

Kirill: That's fantastic. And I was about to say that that sounds like a lot of work for one person, and you must have a team. How big is your team?

Damian: We have probably, hard core data scientists, I would say two others besides myself. There are other aspects of that where we might offload some of the data munching who are more technical side, and there are three people there. We also have some domain experts for various things. So the fortunate thing for me is I have this core team, but I'm able to put my hands on other people in other departments and really grow my team pretty quickly if I need to on a particular problem.


Kirill: That's very nice to have that flexibility. And at the same time, people are not just left doing nothing, they're just ad hoc doing data science when it's needed. Very interesting. And could you give us a couple of examples of what kind of data science do you do? Like I'm assuming mostly it's modelling or some machine learning. Could you give us some examples please?

Damian: We might have a situation where a client says, hey, I'm really interested in scheduling. Make sure I have the right amount of coverage. And primarily because they may have as-needed consultants that fill a gap for a particular healthcare provider. And so it's really important if they call too late in the day, or if they call too late in the process, they actually have to pay a premium. So to kind of know in advance, it really impacts the bottom line.

So scheduling kind of sounds humdrum. But the interesting aspect of it is, when our team was able to look at it, we actually just came back with the client and said, "Listen. What about if we don't just look at scheduling, but what if we started to bring in quality of care as a component as well?" And so being able to say, "Well, we scheduled something. We're not just scheduling for people to cover shifts. We're also doing some matchmaking between let's say, CRNAs and anaesthesiologists, if they are known to be better teammates, we can create a better quality outcome for a person. We want to schedule for that firstly. And so that's kind of a unique application of what we do.

In other aspects, there's financial impacts, through the financial revenue cycle side of the house. We mentioned operational. We have sepsis, or condition awareness for people as they come in to facilities that's a really interesting


application of machine learning. Many of the rural hospitals that we work with, they just don't have the technical capability to be able to pull off anything other than "just let me turn it on". And it's just a resource constraint that they have. So being able to do everything from building the model to actually when someone comes into a facility, have that data exchange with us, real time, and score a patient before a doctor maybe, and then provide that information through like secure text, or secure email, it really takes machine learning from the back office, or some sort of visualisation presentation to where it actually says it's useful. It's very exciting for all of our clients.

Kirill: Yeah. I totally can see how that would be valuable, especially for patients coming in when they are greeted with the right people that will deliver the right value and their treatment is expedited. That's very powerful. And I actually watched one of your videos online. I watched your presentation on the West Nile Virus, and how you created a predictive model to predict in which areas of a city, I forget --

Damian: Chicago.

Kirill: Chicago, that's right. How the mosquitos which had this virus were spreading over the years. That was very interesting. Could you talk us through a little bit on that application of machine learning?

Damian: Yeah, so I think just from a business kind of higher level, city-state government type scenario, the process for them right now is they just didn't know where to spray. I mean, they had mosquito traps set, because they'd had an issue. Like many states' government do. They would literally just collect those, and see if West Nile was present in a mosquito trap. If there was, they would know this area has an issue.


So this would be an area that we would generally spray. Except that mosquitos are kind of one of those crazy things to try to monitor and watch. I mean, they travel really, really well because a lot of times, the bird is a vector for transport. So when a bird's taking a little bit of a bath, a little bit of a dip, a mosquito might actually see that as food, and the bird may transfer this to another area. I mean, in my area, you hear a lot of times the birds fly south for the winter. So we have a lot of New York or Chicago area. They're going down to Florida, those sorts of things. So it's kind of interesting to see how nature kind of works. And it's really, really interesting to try to model things in in a natural environment.

So, for us, that was really a great exploration exercise to see how other data, in addition to what was seen as the obvious data -- obvious data was, "I've collected this many observations from a mosquito trap," and combining it with data from weather, from humidity, from rain, from what are the temperatures, all that sort of stuff and trying to come up with many, many features from those interactions. And then also trying to figure out subspace in the features, figure out which ones blend well together to create kind of a supermodel. In the end if random is 50-50, meaning "I don’t exactly know where I need to spray this mosquito spray, but I’m spraying it", we were in the mid-80s on that, which was pretty powerful.

Kirill: Wow, that’s a very impressive uplift. Also, like you say, trying to model nature, or natural phenomena in data science is always very interesting. And what kind of techniques did you use in this specific case, if you are able to disclose that information?


Damian: Yeah, so a lot of it is just trying to understand what interactions occur on the data. So for us, if you take information gain, just what’s the mutual information, kind of the uncertainty in the field, and you take all those variables, in general, in that case, you know, what are the top 10? I take the top 10 and I start thinking about combinations. That’s kind of a "two to the end" scenario, so it’s a little over 1,000 combinations. So trying to figure out and model each subset of those, subspace of those, it takes some time. But if you use distributed computing, it can actually collapse the time quite a bit. And then trying to figure out how to ensemble those models. I’m a huge fan of diversity, so not only in—everybody starts with the same data, for the most part, unless you get into kind of "in the wild" real world setting. But like in these Kaggle competitions and such, everybody starts with a same dataset. So you have to figure a way to kind of diversify away from that. And a lot of times, it’s subsetting the data. So I want percents of a total column, and I want to create new datasets to create new models. Sometimes it is a subspace with the features. I want to take feature 2, 4 and 9, and I want to see what that does, and I literally try to model in a way that I grow the data to see if it’s a learning model versus just kind of a memorising model. So I might take 16% and 32% and 64%. If the model—validation across validation, or in some cases the holdout is going up continually, it’s a good thing. If it’s going down, it’s scary.

Kirill: So that’s—the first example would be a learning model. Is that correct?

Damian: That’s right.


Kirill: Okay. And a memorising model would be one where adding more data doesn’t really help.

Damian: That’s it. That’s exactly right.

Kirill: That’s a very interesting distinction, yeah. So testing for that, what would you do if it’s not a learning model, if it’s a memorising model, when you say it’s a scary situation?

Damian: Yeah, so in those situations, I would pretty much discard that model and what I mean by "that model," it’s usually a model family. I mean, there’s a number of things. There are kind of the out of the box algorithms, if you will. XGBoost, for example, or Random Forest or Extra-Trees and those sorts of things. I’m a big fan of Scikit Learn. There’s some good stuff in R as well. But basically, when you change the subspace, sometimes, when I mentioned the first feature, or the first three features I talked about, they might work really well with a support vector machine. But the other three features in that top 10 may not. They may do really well with a regularised logistic regression. And so being able to create that diversity is a good thing, especially when we start to ensemble.

Kirill: Okay, so to reiterate, a model that memorises, or a memorising model, is something like a curve fitting model, right? So it fits itself.

Damian: That’s it.

Kirill: Okay. Good, that all makes sense. And you mentioned Scikit Learn. Is that a certain tool? I haven’t heard of it before.

Damian: Scikit Learn is just a Python library. So for us, it has a lot of these models that we’re talking about. It’s pretty easy to navigate, if you like Python. It works really, really well. It’s


got some great pre-processing features, and it’s pretty well-developed. They’ve got some great core contributors on there that do a great job for the rest of us. That’s one that I use quite a bit. In the early days, I used to use an R package called Caret. It’s still a great package, it just depends on what the modelling task is.

Kirill: All right. And we’re slowly getting into tools. First question that I love to ask, I call it the million dollar question, your view on Python versus R?

Damian: I think where I’m at today is I’m a bigger and bigger fan of Python. And the main reason is it’s a little more intuitive for me. Syntactically, R is just different than what I’m used to. I don’t spend enough time in it to just say this is home base. The other thing is that the sort of thing we’re doing nowadays, we’re really interacting with a lot of web interfaces and properties, and we’re developing our own skins and applications, and we’ve got APIs. I know R can do a lot of that stuff. It seems that there is more developed stuff out there for me to kind of just grab and implement with Python. I think because it’s such a general purpose language that it just allows me to be more productive. I’m a very "nuts and bolts" kind of guy, probably not the kind of purist I should be, but in my context, we’re a very small organisation so I can’t—I can do anything I want on my free time, but when I get into the office, I have to be productive.

Kirill: Totally. I totally appreciate that. So what would your advice be for somebody just starting out into the field of data science? Should they learn both? Or should they just focus on Python and not worry about R?

Damian: I think learning both is a great solution. I can’t tell you the number of times I’ve read a paper and it’s written in C++


even and you go "Oh, boy." So just having familiarity with it—I don’t want to have knowledge not available to me just because I know only one language. I want to make sure and use as much of it as I can. And we do, I mean, we use a lot of—I wouldn’t say a lot, but we use R in a lot of what we’re doing. Sometimes it could be as simple as Random Forest and R is different. It’s a different implementation in Python. So even though it’s Random Forest, because it’s implemented slightly differently, it creates another family of models. And so we’ll go ahead and use both.

Kirill: Love it. I love the idea of limitation, consciously limiting the knowledge that’s available to you if you’ve learned one language, and also different models. Random Forest is called random for a reason. Random algorithms are different in different implementations, so I can totally appreciate how you can get different results with that. So speaking of tools, are there any other tools that you use or would recommend to those in data science, or even specifically in the healthcare space?

Damian: That’s a good question. I’m going to talk about a dirty data science secret. I don’t know how many people would agree with this, but one of the tools I use is Microsoft Excel.

Kirill: Wonderful.

Damian: It’s a good standby. It’s good for rapid prototyping type stuff. I mean, I’m not crafting algorithms in there, but sometimes even for me to do Pandas work, it’s easier for me to type a little bit versus having to type three or four lines of code just to come up with the same sort of thing. There’s some limitations, obviously, so I’ll quickly move over to Pandas. But if I’m just trying to get a cursory look. Sometimes I find myself spending probably about 15% of my time in Excel.


I use Tableau. Our team uses Tableau. For us, we look at—we like Shiny, for example. That’s an R type scenario. It’s very nice. For us, we just need to be able to be productive and we use Tableau. We’ve gone through stages on Tableau. We like to prototype in Tableau. We will deploy in Tableau. But more and more, what we’re looking to do is quickly iterate with our team in Tableau. Everybody can do their thing there and then get that to a development team or somebody on the data science team to code up and make it a little more proprietary. That’s really helpful. And we have people on our team, data science team, that are more R specific. Specifically in the biostatistics world, R has got a lot of traction, so we want to make sure and play well with that. We have a large university here in our area, Vanderbilt University – big biostatistics, big R users, so we want to make sure and play nice with others. We try to keep our hands on all those tools.

Kirill: Yeah, totally. I can see how in biostatistics, R is popular in that department. You know, speaking of Johns Hopkins University and all those medical-oriented data scientists that are using R—it’s a huge following. You mentioned "play nice", and that instantly reminded me of one of the articles that you wrote: "Will Healthcare Play Nice with Data Science?" If our listeners haven’t read this article, I highly encourage you to check it out. It’s on LinkedIn. I’m just going to quickly give a background of what it talks about, that oftentimes, as Damian says, oftentimes when you’re learning something for a very long time, or when you’re very experienced or an expert in a field, and then somebody comes along and tells you their opinion about it, and they don’t know anything about your field, you’re going to get


irritated. You’re going to get aggravated, and you’re going to feel this turf issue, like they’re threatening your territory.

And in healthcare, that has happened many times over the years. There are examples with smallpox when the cure was invented and it wasn’t adopted for many years; cholera – we all know now that it’s transferred through the mouth. And before, the scientists and doctors used to think that it was transferred through the air, and that wasn’t changed for many years as well. And even washing hands – washing hands for medical practitioners – I was so shocked at this. Semmelweis asked the medical practitioners to wash their hands in 1846, and that was only adopted 130 years later. That is insane! So what you’re seeing, and what the article is about, is will healthcare now allow data science to come in and actually dictate, or even just advise and recommend some changes and some new things. I would love for you to share some of your thoughts on that with our listeners.

Damian: Oh, yeah. You know, the truth is I lived that in healthcare. But the truth is probably data scientists all over the world are going to be, if they haven’t already, encountering those kinds of things, kind of the established domain expert. And then you start leveraging machines the way we do as data scientists. All of a sudden it’s like, "Well, how can that be?" In healthcare proper, what you have is a very smart group of people. You have a lot of MDs and PhDs. In many cases they run trials themselves on some selection of population. Some individuals actually start mining literature – they’ll do 25-30 years of literature review, and they’re going to boil down everything to four or five rules. This is—like in the case of sepsis, usually in the four or five ways we’re going to identify sepsis. Everybody who is probably listening to this podcast


would agree there’s more nuance than four or five rules. So when you come at it from that point of view, of nuance, and not these kind of static rules that were established 20 years ago, and keeping in mind that population shift occurs, people change, people get older, people die. Same thing for providers of care. They get smarter, some of them are brand new, nurses leave. You know, all this sort of stuff is happening underneath the data, and if you don’t take that into account, people kind of don’t appreciate that for the most part. So in the healthcare setting, we have spent probably the last 14 months in that dance of what is helpful, what is seen as a threat, explaining what machine learning is and what data scientists do, how we use data, why we would ever want to create bigger and bigger context with data outside the four walls of a hospital. All these sorts of things—I mean, the sepsis model that we’ve deployed actually doesn’t use any clinical values. So I never get any white blood count, I don’t have any blood drawn, I never know what the heart rate is or the respiratory rate, but yet we still have a very, very high – in the mid-90s – area under the curve.

Kirill: Wow! That is so impressive.

Damian: So, when you talk to a doctor about that, or a physician, if they don’t understand that we’re better together—we try to have the conversation of – and I could say that on this podcast – we want to have an ensemble. We don’t see these patients. We’re in Brentwood, Tennessee. So we don’t see these patients. They are there, they’re seeing them for real. And so they’re having a modelling aspect, and now what we’re talking about is our machine has a modelling aspect too. So we do well—both of us do really well. What’s


interesting is, because of the diversity of the models, the physician and then our machine learning algorithm. We actually have a setting where we have an opportunity to blend those results. So when we blend those, we get so high. I mean, it’s like 98% accuracy or area under the curve, if you will, between both parties coming together. So it’s kind of like machine learning and providers of healthcare coming together. We’re trying that conversation because it’s still scary. I mean, I recently read an article that said anybody who’s touching machine learning or artificial intelligence should be charged an artificial intelligence tax. It makes you go into a bucket to help all the people that’s going to put them out of a job. And that is such hyperbole, it’s out of this world. But at the same point, I do think it does say to the rest of the world, it’s time to get into some higher order thinking where we can, and let the machines leverage what they’re good at.

Kirill: Yeah, that’s fantastic. And I agree with you on that example of the tax. I can understand how people would think that, but that’s one of those things that just slows down progress and keeps us where we are as a civilisation. And I love the example of qualitative — if I may call it that— qualitative plus quantitative. So you’ve got the machine learning as quantitative modelling, and then you’ve got the doctors who may have their own approaches, but we’ll call it qualitative because it’s less quantitative.

But, you know, when you combine the two and, as you say, you leverage this diversity, you create ensembles, and you don’t just focus on one level, but you leverage these different approaches. 98% accuracy – you know, if some of our previous podcast guests create models for banks and so on –


if a bank could get a 98% accuracy, they would pay a lot of money for a model like that. Through all your experience of speaking with people who have this sense of a turf issue, you know, that you’re invading their space, that data science may be something new to them, through all of your experience of exploring this and getting the results but going the hard way, actually fighting your way through to get data science into these fields, what would your tips be for somebody who is starting out? Because, as you mentioned correctly, every data scientist, not just in healthcare but everywhere we experience this pushback. So what would your tips be on going around that, fighting that, or finding your way to actually kind of fulfil your role?

Damian: You know, I think a lot of it would come down to just try to—as passionate as we are, there’s a certain level of expertise that we have that we feel like maybe we can’t explain because it’s just so intuitive. Some of us may have been doing this for a year or two, three years, five years or longer. And so it’s kind of hard sometimes to sit across the table and have somebody with a very strong opinion who is "analytical", but for whatever reason we’re not connecting on this data science issue. What I’ve had to try and train myself to do—and sometimes I do it better than others some days—is really try to understand, keep asking questions why. Why would they be saying this? Why is it that they’re saying they already do analytics? Maybe what we need to do is negotiate terminology. Let’s talk about what analytics is. When you kind of step into that in a non-threatening way, let’s just say "What do you mean by analytics?" you might soon find out that they’re talking about business intelligence report. And it’s not forecasting really, it’s not predictive, it’s not simulation or anything like that, and so when you do that


we can say, "Oh, I see what you’re saying," "of course," and being able to kind of talk about retrospective and prospective, and the differences, and how you feel like you’ve obviously got to have that kind of support from the retrospective first and that sort of thing.

The other thing is I’m learning more and more to try and bring smaller problems into an organisation where we can get some quick wins fast. Once they get a taste for what can really happen, it is a lot easier to get traction in an organisation. So, for example, if we went after a really, really sizeable problem I might say, "Look, that is great. I’m glad we’re going to do that. Can we talk about another problem as well?" And just flat out say it. "I want a quick win for us. I want you to see how it works and let’s talk about it." Some people need an explainable model, and so that’s important to them. Finding out what is driving them, motivating them to have the response is a big thing. We found in the case of healthcare—you know, think about how long a physician goes to school and then you come in and say, "I’ve got a model I just started up 5 minutes ago and it can predict pretty close to what you can predict." It would crush my spirit. But it’s really not a fair comparison. So trying to get them to help make those distinctions obvious and keep machine learning in certain aspects of the business where they don’t necessarily want to be in. So, for example, a lot of them, a lot of physicians hate putting data entry into an electronic medical record. So, if a machine can help in those areas and give them more time with their patient, that’s a good trade-off. So just kind of coming alongside those kinds of things. But the best thing is to be just be a persistent listener – less talking and really listening to what it is that might be motivating them.


Kirill: That’s some solid advice. And at the end of the day, when we think about it, machines can significantly improve certain areas, but at the end of the day, patients coming into facilities want that human interaction. They want somebody to talk to them. They want somebody to listen and to hear them out. We’re not at that stage yet of our development as humans that we’re completely comfortable talking to a machine. Personally, if I would go into a healthcare facility, I would much rather talk to a doctor who can understand me, empathise with me, and things like that. So there is always going to be space for doctors. And as you say, if we can reduce the amount of time that they spend doing those things that they don’t love, as opposed to actually delivering value and sitting with those patients, then I think that’s an advantage of machine learning.

And just to sum up the three things that you pointed out there as tips for our listeners who are facing these situations: try to be non-threatening, try to walk in the shoes and be very understanding of the person that you’re speaking with, the stakeholder; give them a taste so provide a quick win to show them the value that you can actually deliver; and find out what drives them. When you find out what people’s passions are, what people’s drivers are, it’s much easier to communicate to them. So those are some very good tips from Damian. Another thing I would like to talk about, so moving on a little bit from this topic, is situational fluency. We discussed this a little bit before the podcast. And by that, what we mean is how can you think about a problem in terms of data science? How do you decide how to apply data science to a problem. Because knowing the skills, knowing the techniques, is one thing, but then actually seeing a problem and switching on the right


neurological pathways in your brain to come up with the correct way to apply this, it’s a completely different way. From your experience, Damian, what would you say—how do you go about this situation?

Damian: So, generally, when we talk with a client, we generally ask them what they’re measuring today in reports, in retrospective reports. The reason we start there is we kind of get a sense for what’s important to the organisation and that is what we want to try to convert to either real-time machine learning applications, or something that’s a little bit more future than real time. Also start to understand a little bit where their pain points are, so what is it that’s a problem for them. I’ll give you a healthcare example. Imagine a scenario where you’re in an executive meeting and you hear, "Oh, my goodness. Our radiology, all the X-ray images and all that sort of stuff, is killing us. We’re paying these radiologists half a million dollars a year. They work 6 hours a day. They can only do so much." No kidding, it’s a very complex situation. So what we do is we can’t really grow the volume of our department because we can only afford to bring in one radiologist who wants to live in the middle of nowhere America or Australia or wherever. So it’s like, "Boy, how do we do this?!" A lot of companies, a lot of healthcare companies, outsource this to other individuals who are qualified across the ocean in China or in Asia. They do all this analysis and when they come in the morning, the radiologist reviews that too. That’s not free. It costs money. It’s certainly less, but when you think about all that sort of stuff—and as someone who consumes healthcare, one of the things that drives me nuts is having to get this really, really important test to find out if I’m going to die in five minutes


or whatever the situation is, and really having to wait a week or two weeks.

So how do we speed that up and bring some consumer focus in a patient experience? You could convert that pretty easily to a computer vision problem. So if I had images that were already annotated by radiologists over the last 2, 4, 5, 10 years, let’s take those, let’s train them, maybe we can rotate the images so we can kind of synthetically grow that data and get a huge dataset for us to be able to get pretty darn close to a physician and that sort of stuff. Now, the physician or the radiologist can review that to confer with the machine. And there’s no sending overseas, the cost is negligible, it’s whatever it takes for S3 storage on Amazon and whatever GPU instance you might have. And that’s it. And speed up gets really quick. So, not necessarily going into those technical terms with your executive team but being able to say, "Look, this is how I see this happening. Here is where I think we can cut cost and here is why. And we can speed up the process and we can get decisions out to patients sooner." That sort of stuff in the healthcare world actually catches good attention. And it actually is good for business. So, besides just reducing costs, it actually becomes an economic driver.

Kirill: Okay. That’s interesting. So you kind of see the problem and then you think of what uplifts and potential benefits you can deliver at the end of the day using the skills at hand and then you convey those. In that case, how do you go about—knowing for sure that you can actually deliver this increase in speed or this reduction in cost—you know certain skills and techniques and tools in data science, but how would


you know with confidence that this is something that you will be able to deliver?

Damian: So one of the best things I think I ever did a number of years ago was join Kaggle. And the reason was I remember—I first kind of got started when I picked up some magazine somewhere, it was a predictive magazine. It said, "Listen, you’ve got this Heritage Health Prize. It’s a 2-year competition. People are trying to model it, and we’re trying to win big money. See what you can do." And I thought "That’s really interesting." So I went at it with Microsoft Excel initially and then after about probably seven days trying to get there, I think I ranked in like the Top 2,000 or something like that. It was quite embarrassing, but that was a motivator for me to keep going, to get involved, to kind of understand.

The reason why I bring up Kaggle is because Kaggle throws out so many different data sources that I would never, ever touch in just healthcare. So I’ve done classification for bioacoustics, for sounds of birds in the wild, and everything else. Whales and retail and all sorts of stuff. The reason why I find that valuable is because you’re learning at so many different levels when you’re on a Kaggle competition. One is you’re being exposed to new techniques and diversity. There’s people all over the world trying to solve this problem. What questions are they asking and answering, and how does that impact my learning when I go out into the workforce?

The second thing is, when I started really getting into it, I remember being in some of these board meetings and in the early days, it was just I knew what I could do. And until someone basically said, "Hey, we think this is a data science


thing," it was on me to just sit there and just know what I can do. That was really not so fulfilling. But when I started to understand, I would hear a problem and I’d go "Oh, that’s a lot like that bird competition that I was in," and "That’s interesting, that’s a lot like that retail, I was trying to forecast people coming back to make a purchase." And you start kind of understanding how the world works. I don’t exactly know any more, but I think they have about a hundred different competition datasets out there. I know they’re adding more and more datasets, open data, which is great. But just getting exposure to that, that’s going to build the confidence because you’re going to know it works. You’re going to see somebody get paid $25,000, $50,000 or $150,000. They’re not going to let loose to that money unless they know it works. So being able to drive that back into what you do every day is a huge win, I think, and it’s a great way to learn.

Kirill: Yeah, totally fantastic. And I completely agree with you. Kaggle is a fantastic source of this exposure to hands-on experience. And hands-on experience in itself is a crucial component of becoming a successful data scientist. One thing is to go and learn the tool. Go and learn R or Python or Tableau or something else, learn the techniques as well. But that is a completely different story of taking the knowledge that you have and applying it to real world datasets and case studies to understand how to use that. And moreover, not just once, not just twice, but continuously doing that. Because if you don’t continuously use or find ways to apply your knowledge, you’re not going to be on the cutting edge of data science. Data science is evolving so quickly and moving so rapidly that you’re going to fall behind and you need a way to test your knowledge, a way to find new ways,


techniques and methodologies and see how they’re applied in the real world. So definitely case studies and real world examples are very handy so, for our listeners, Kaggle is a great place to go. There are other places you can find datasets online to practice with those. Also, if you’re part of SuperDataScience, we have case studies there. We focus on that as well, on providing the real world examples because it is so, so important. I’ve got a couple of very interesting questions that I would really love to ask you because you have such broad experience in data science. And the first one is: What’s the biggest challenge you’ve ever had as a data scientist?

Damian: Well, I really have to say I think it probably is communicating what we do with individuals who either have very little experience with analytics or have quite a bit of experience with analytics. I know just this week I got some questions from a client of ours. They have a statistician, and the questions were really good questions. But the issue became—it was a little less about statistics proper and more about what we know as data science, so this programming element, of course here’s the math and the stats, but how does this all relate to healthcare and how do we know we have a good model and those sorts of things. So I constantly think about new ways to try to communicate what it is we do in a specific task and make it relatable. Analogies work really, really well. It’s just kind of a new and emerging thing for a lot of businesses. Some people think that if they take the "business intelligence" doorplate off the department and just put "data science", that they have a data scientist team. It’s not that way. We know that there’s a real approach to data science. So explaining that three-legged stool is really, really complex or as simple as it should be. But if you think


about it, when you look at a beautiful sunset, it should be really easy to describe. But it’s actually quite hard. And in a same way, that’s how I view data science when we talk with other individuals.

Kirill: Fantastic! That totally makes sense why you wrote that article "How to Explain Data Science to Someone Non-Technical". Our listeners, if you haven’t seen that article, definitely go and check it out on LinkedIn. Damian actually has quite a few articles and very interesting reads. So, this one is called "How to Explain Data Science to Someone Non-Technical". If you ever face that same problem, you’ll pick up some great ideas from there. The next question is: What is a recent win you can share with us that you’ve had in your role, something that you’re proud of as a data scientist?

Damian: I think the thing that I’m probably most proud of today is kind of Condition Awareness Module. It’s sort of a platform that we’ve created and what I like about it is it’s taking as kind of a first case a 3,000-year-old problem which has been really, really difficult to solve for, and that is the sepsis situation. We just published an article and it’s basically a review of just the very definition medically of what it means to be septic. There are people that argue about that on a very grand scale. If we can’t identify what the truth is, it’s hard to identify anything at all. So being able to come in and create supersets above that kind of the fray of what the definitions are and that sort of thing. We had to create that ground truth based on a lot of expertise that go beyond my knowledge and our group. And then we had to figure out data proxies, you know, how could we proxy an outcome or proxy a missing field. If you think about inferential statistics, there’s some imputations that had to be done to


kind of smooth it out and get a good model going using a multitude of datasets. I mean, we’re constantly bringing in datasets not only to augment and enrich what we’re doing, but really just trying to know more about any of the problems that we might be encountering has really been useful.

But the thing I think I’m most proud of is taking it from just a model that might produce another report to actually having a real world impact. We’ve deployed this model in two facilities and it’s been very interesting to see. At first it was—we had to meet with a lot of the executive team and kind of "What is this? What are you doing? What do you think you can do?" and all this sort of stuff. To my point earlier, not being able to use any of the clinical values because it’s too late then, we actually try to use this—John Tooke has got a famous quote. The idea is basically it’s better to have an approximate answer to the right question than have an exact answer to the wrong question. To me, the way we translated that was, "Listen, if I have to wait for my grandmother to come through the emergency department, and then they have to wait in the waiting room and then they have to see the doctor whenever you can get there and they have to do clinical values and you have to send off lab results and then I can know specifically that my grandmother has sepsis." In some cases, five hours has passed. There’s a 7.6% increase in mortality for every hour that passes, so that starts to mount up pretty quick.

You can have the best medical facility, the best physician, the best world class protocol for treating sepsis, but if you don’t identify it early, it becomes a huge problem. A huge problem! A person is more than half dead before you can


apply any of that great stuff to them. So being able to do that all without any kind of lift technically from a facility is a huge win for me and our team here. We’ve had some great collaborators. We’ve done a lot of publications around that. It’s been a fun process. It’s been certainly challenging. We’ve learned a ton throughout that process and constantly we’re trying – to go back to the other question you asked – we’re trying to reframe what it means when we have these results. So just coming about it and talking about what we’re able to find in those results and the level of area under the curve has been really tremendous and we’re very excited about the future.

Kirill: Wow! Damian, I can only say wow, and hats off to you. I can’t imagine how many lives you saved with just data science and with that algorithm that you deployed. 7.6% increase in mortality per hour! I’ve been in emergency rooms myself where I had to sit like 4 hours, and I saw people that had much worse conditions where something was wrong with their head, or really serious injuries and they were still sitting for 4 hours. That is so impressive. I know it’s probably a huge study that you’ve done and we won’t have enough time to go into detail about it here, but is there any article or publication that you can recommend to our listeners if they would like to learn more about this specific case study of sepsis and how you use data science?

Damian: On our website at wpchealthcare.com there is quite a bit of literature out there. We’ll be posting more and more over the next 45 days, specifically the results. We just published through the International Clinical Pathology Journal the evolving sepsis definition that was just produced last month, I think, so that’s out there. And then there’s some other


publications that should be coming online that we’re impressed on right now. But most all of that is going to be put on wpchealthcare.com

Kirill: Fantastic! So, guys, check out wpchealthcare.com if you’d like to learn more about that life changing application of data science. Moving towards the end, what is your view of data science right now? Like, from where you stand, from all of your experience, where would you say you think the field of data science is going and what should our listeners look out for to prepare for data science in the future?

Damian: I think more and more of what will come is more of the automation. There’s been a real big emphasis on the fact that there’s not a lot of data scientists out there. I don’t know, maybe that’s true, maybe that’s not true. At least that’s what the media is saying. So there’s this scarcity and there’s a need to create some synthetic or artificial data scientists. Some of what we do can be automated, absolutely. So I think we’ll see more of that come into our wheelhouse, if you will. And that could be a good thing, much like if we kind of take our own medicine, like we were talking about with the doctor, maybe there’s some things that we don’t necessarily want to do in data science and there’s other things that we’re interested in doing.

I think there’ll probably be three major things that will show up in the next maybe five years. One is, as a way to kind of prevent being automated out of data science, I think you’d have to think along the lines of "Can I convert a business problem into a data science problem?" Right now, if you think about IBM Watson and some of these other big cognitive platforms, they’re really great and it’s really exciting times. But there is a couple of hang-ups. One is if


you can’t get the data to them they can’t do any of that stuff. So, figuring out how to get to your data, that’s the new bottleneck. It’s not crunch time on the numbers and that sort of thing. So that’s going to be an interesting thing to kind of overcome.

The other thing is trying to create targets, so we talked about creating data science solutions from business problems. If you don’t know how to create a target, something you want to predict that’s not obvious, I think that you’ll have a difficult time in the future with data science. I really think, even in a meeting on your own, depending on what your role is with the company, thinking through "How will I make this a data science problem?" is good mental experiment that you can do. You never have to share it with anybody, but it’s a way for you to start doing some brain training on how to think machine learning in the world and that’s very, very powerful. If you can’t implement these data science solutions, I think that will be another struggle.

And the third one, which I think is going to be everybody’s future field day – if you have the IBMs of the world and some of these other data science players come in and they starting automating, it’s going to send everybody’s floor – let’s call it 80% area under the curve. The new gold will be trying to move it from 80% to 85, 86, 87%. So you’re going to have to have developed techniques on feature engineering and how to work with data and some of the stuff – we talked about subsetting, subspace, ensemble and all these sorts of things – to be able to get yourself the lift that you need because today people will pay from going from 50% to 85%. That’s huge! That means a lot of money in a lot of cases and a lot of


saved lives. In the future I think you’ll see those margins shrink or those percents shrink but they’re still going to want to pay for them.

Kirill: Yeah, totally. I agree with that completely. And just to summarise the points that you mentioned, so getting the data is always going to be valuable. Machines can’t make their predictions unless you give them the data. Finding your target of your data science exercise – that is also very valuable, especially if it’s unobvious to understand what the end outcome should be. And there’s a lot of mental experiments you mentioned. You just sit in any meeting and any conversation and you just think "How can I turn this into a machine learning problem?" And I’m totally going to do this. Even if I’m just going to a social event, I’m going to sit there and think "How can I turn this into machine learning problem?" And it’s funny how sometimes you get so carried away into data science and then you go out into the real world and people start thinking or even telling you that you’re a machine, or you think like a machine. So that’s going to exaggerate that even more.

And finally, the 80-85%. Right now, the big money is in taking something from 50%, so basically flipping a coin, to 80%. But look out for ways or start preparing for the future, start creating models, start thinking of ways to take it from 80-85%. And luckily for our listeners we already discussed a couple of these methods – subsetting, ensemble – in this podcast. So there you go. You’ve got a head start. Thank you so much for coming on the show. How can our listeners contact you or follow you or follow your career or read your articles if they would like to know more about what you’re up to in the world?


Damian: LinkedIn is a great place to connect with me. I usually post quite a few articles out there. I love to make connections with data scientists or people who appreciate data science all around the world. Twitter is another place, so that’s just DamianMingle, and then wpchealthcare.com is great and then, of course, if people want to reach out by e-mail, they are more than welcome to e-mail me at [email protected]. I’d be happy to start up a conversation.

Kirill: Thank you so much. There you go, guys. We’ll include all of those links in the show notes but right now, if you still want to become a super data scientist and learn more from Damian, go to Twitter right now and find him @DamianMingle and follow everything he has to share and follow his career. And one final question for you, Damian: What is your one favourite book that can help our listeners become better data scientists?

Damian: "The Grammar of Science" by Karl Pearson is an oldie but a goodie. He was a professor of applied mathematics in London. That book actually went on to inspire Albert Einstein, so I always try to see if I can get any kind of Einstein fairy dust. I try to review that once a year. It’s a really old book but the idea is to try and think about science in a more comprehensive way and see the interactions for what they are looking at space and time and all that sort of stuff. Trying to bring that into data science, I think it would be hugely helpful in a very scientific way.

Kirill: Fantastic! Anything that inspired Einstein is definitely going to be a good read. So, guys, pick up "The Grammar of Science" when you have a chance. It might inspire you to change the world. All right, thank you so much, Damian, for


coming on the show. I really appreciate you taking the time and sharing this with our listeners. This has been a tremendous experience and you have delivered so much value to us. Thank you so much.

Damian: Thank you. My pleasure.


Kirill: So there you have it. That was Damian Mingle, chief data scientist at WPC Healthcare. Super exciting episode! I hope you learned a lot because personally I learned so much from Damian. The most impressive by far was the application of data science to actually save lives. This has been an eye-opening experience for me to witness first-hand that there are people out there who not only deliver business value and other types of values in the world using data science but actually save people’s lives. When every hour counts, when every minute counts, data science can come in and be that little difference that will help a person stay alive.

Other things that I’ve picked up from this episode are, of course, the combining of qualitative research and knowledge and quantitative data science in order to come up with the most powerful models in the world. I mean, 95% accuracy – that is unheard of. That is very, very powerful. And, of course, that is super valuable especially in the area of medicine. And I was also intrigued by Damian’s view of the future of data science where creating models with 80% accuracy is going to be normal. People are going to want models with 85% accuracy, with 90% accuracy, with 95% accuracy. So looking into advanced modelling techniques such as ensemble methods and combining qualitative and quantitative data and other methods we discussed in this podcast is definitely a worthwhile exercise. So there you go.


Make sure to look up Damian on Twitter. His Twitter handle is @DamianMingle, spelled @D-A-M-I-A-N-M-I-N-G-L-E. Follow Damian so you can get his latest articles and news and see what he’s up to in the world.

Also, visit www.superdatascience.com/13 to get the links and show notes for this episode. You can also download the transcript and also you will get Damian’s LinkedIn, URL and some of his other articles that we discussed in this podcast episode. I really appreciate you, thank you so much for following the show. I can’t wait to see you on the next episode. Until next time, happy analysing.

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SDS PODCAST EPISODE 13 WITH DAMIAN R MINGLE...Kirill: This is episode number 13, with Chief Data Scientist at WPC Healthcare, Damian Mingle. (background music plays) Welcome to the