HOW DEEP LEARNING IS TREATING HEALTH-BASED ISSUES

Rosie O'Callaghan our consultant managing the role
Posting date: 8/30/2018 8:00 AM
Hospitals are a complicated system of many moving parts both human and machine. In recent years, the role of humans driving the process, entering information, gathering individual records, or arranging medical and billing follow ups, has shifted.

Paper records have become electronic health records and AI is helping streamline bulky processes. AI bots and programs free up time when it comes to arranging follow up medication or helping to make diagnoses and, in some cases, can assist physicians or surgeons making remote calls and decisions.

As Machine Learning and AI enter healthcare, the application of Deep Learning, using data rather than task-based algorithms, is coming into its own. At this year’s KDD event, both Healthcare and Deep Learning were hot topics, with a day of programming dedicated to each.

The Three Ingredients Driving AI Advances:


  • Supply of digital data which can now be created.
  • Development of algorithms to make artificial neural networks.
  • Graphics Processing Unit (GPU) chip architecture pioneered by NVIDIA.

GPUs are used by anyone working in Deep Learning and can be used in any number of ways, such as videos, graphics, and audio recordings to name a few. This type of usage has huge impact on Healthcare’s image, clinical data interpretation, and management. 

For example, Radiology requires consultants to look at medical imagery to determine whether or not there are abnormalities. With the inclusion of Deep Learning, this process could be done in minutes or seconds rather than hours. This is especially important as a diagnosis made is based on findings in the radiological images.

However, Radiology, is not the only instance where health management can utilise Deep Learning and AI. From helping to identify ideal treatments for patients, to helping administrators utilise their resources more effectively and efficiently, there is huge potential for implementation. 

Predictive Analytics in Deep Learning


Healthcare can be hard to predict. But, with the application of Machine Learning, there are some things we can focus on, starting by asking ourselves the following:

  • Is it scalable? This may differ based on different hospital systems and how much data wrangling is involved. But, the more straightforward the answer, the better.
  • Is it accurate?  Using Deep Learning data for electronic health records can greatly improve accuracy and avoid the distraction of false alarms.

Predictive modelling can help Healthcare professionals answer the questions above more accurately, including determining which patient will have a particular outcome versus which patient will not. Though this model does not diagnose the patient, it does use the information from data gathered to identify the conditions in which the patient was being treated and predict outcomes. Like a human might pick up nonverbal signals, AI picks up signals based on the data it receives to and helps inform physician’s decisions.

The Patient Journey 


Whether it’s the customer journey or the patient journey, there is a path that needs to be followed. As Deep Learning helps fuel the use of AI in Healthcare, our patient journey becomes less stressful and more streamlined. 

Below are a few ways Deep Learning is helping to facilitate a more efficient health management system:

  • At Home: You go to a doctor because you don’t know what’s wrong. But, how do you know which doctor you should make an appointment with? AI can help. From your home PC, a few clicks and few questions can direct you to the correct provider for your needs.
  • In the Waiting Room: To avoid long wait times, you can check in via an app, have an AI bot ask a number of questions for you to answer to help better prepare the physician for your visit with the goal of a quicker diagnosis.
  • With the Doctor: Referrals are great. But, having to explain your health issue or record, can be daunting. In addition, the doctor to whom you’re referred may have to call your traditional physician and discuss, or he or she may have papers to read cutting into their time with you. Instead, AI standardises how the doctor reads the notes and can lay it out the way the doctor prefers, increasing your time with them and streamlining their process.
  • Patient Follow Up: An AI bot based on Deep Learning algorithms can become part of a provider’s team, checking in, asking a few questions, and sending a friendly reminder email, text, or phone call to remind patients to take continue their course of treatment. 

The introduction of Deep Learning into Data & Analytics has made an impact across many industries, but especially Healthcare. not the least of which has been healthcare. From speech recognition to Natural Language Processing, the effects have been informative and transformational.

If you’re interested in Deep Learning, predictive analytics, or AI we may have a role for you. We specialise in Junior and Senior roles.  To learn more, check out our vacancies. You can also call us at +44 20 8408 6070 or email us at info@harnham.com.

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Is Computer Vision at the Core of the New Normal?

Computer Vision is one of the fastest growing markets in Data & Analytics. While it was on a trajectory prior to the pandemic, the needs we have now have amped up the role Computer Vision plays in our day-to-day lives and businesses who want to keep up or get ahead are paying attention.  Unexpected Businesses Using Computer Vision Some unusual players leaning on these technologies are grocery stores. While some have pivoted to pickup and delivery, others have remained stagnant with yesterday’s shopping habits changed only to individuals in store wearing masks. For those who made the leap to the "new normal", they’re using things like shelf sensors and Machine Learning to automate ordering and determine best placement of a product. Though retail stores are no stranger to video analytics, the rise of Deep Learning and AI offer a more rapid analysis of video for real-time threat assessment. Teaching the machine to watch for crowding, erratic movement, or potential conflict allows for quick reaction or proactive measures to stop a conflict in play. Yet, behind all this Machine Learning and Computer Vision elements are people. Real live humans. And it’s their new normal which is a strong part of the world’s new normal as most everyone shifts and remains online, working remotely. Behaviours are changing and many businesses have differentiated themselves from others by staying ahead of the game.        Five Ways Businesses Are Moving Forward in the New Normal Remote work is here to stay. A jump of 18% of remote working after the pandemic is expected to remain key to many businesses. And nearly three quarters of executives, plan to increase their remote workers. Key components of this new change will be to bring onboard those with strong digital collaboration skills, ability to manage virtually, and reassess how goals and objectives are to be decided. How will businesses keep remote employees engaged, enthused, and feel part of the team when they could be miles or countries apart?Gig Workers as Cost-Saving Measure. As employees move out of office and online, gig workers are a go-to for businesses hoping to move forward and keep costs low. Performance management systems will need to be re-evaluated. After all, if the idea is to keep costs low (read: overhead), then how does the debate about whether or not to offer benefits fit in to the mix?Definitions are Changing. Whether the definition includes ‘critical skills,’ ‘critical role,’ or something similar. What these meant once are changing. Now, the focus is on how to encourage, mentor, or coach employees in professional development skills which can open up a variety of opportunities versus one set path to one set role.Keeping Track Virtually. Though most businesses tend to follow the model of ‘productivity and performance’ over simply hours worked, some organisations passively track their remote workforce. This keeping track can include timeclock software virtually managed to computer usage to monitoring communications. Several benefits of data tracking in this manner could be a boon to HR Managers as it could help to understand employee engagement. But it’s a fine line to traverse.Organisational Redesign Done with Efficiency in Mind. As everything from products to people move online, it’s more important than ever to ensure things like logistics, supply chains, and workflows are designed with efficiency in mind. Computer Vision AI models can help take these systems to the next level as things like grocery shopping, retail, and legacy businesses find their business must go online or pivot in the new normal to survive. In our recently released 2020 Salary Guide we discuss each specialism. What’s working. What isn’t. And how businesses can hire and retain top talent to keep their projects on track and their businesses running smoothly.If you’re interested in Data & Technology, Risk or Digital Analytics, Life Sciences Analytics, Marketing & Insight, or Data Science, check out our current opportunities. Alternatively, you can contact one of our expert consultants if you’d like to learn more. 

WE HAVE TO TEACH SPECIALISATION, WE CAN’T EXPECT IT: A Q&A WITH VIN VASHISHTA

We recently spoke to Vin Vashishta, a consulting Data Scientist and Strategist who was named one of LinkedIn’s Top Voices in Data Science.  Having started off in the tech world 25 years ago and progressing from web design and hardware installation to Business Intelligence Analytics, Vin found for many years that enterprises were reluctant to adopt AI technologies and embrace the value of Data. In fact, it wasn’t until the beginning of the decade just passed that companies started to think about their Data more strategically and the world of Data Science was born, albeit hesitantly:  “When I first started, it was a lot of experimentation, everyone wanted a proof of concept,” he says. “A lot of work was creating models that could go from whiteboard to production and productise and show their value.” However, it wasn’t until halfway through the decade that he began to see businesses who had adopted Machine Learning move away from experimentation into incorporating it more deeply into their companies, relying more on analytical and optimisation models to make strategic business decisions.  “After that, in about 2017/2018 the maturity changed. It went from being a one off implementation to it being a comprehensive tool within an organisation where we have full lifecycles of model implementation and full models that were full views of the system. The key component of development was allowing users to access a small part of the system to do their job better without having to understand the whole thing. And that’s where we are now. We have this applied Deep Learning and we are seeing, especially this year, attempts to optimise that, make things go faster and make them more repeatable.” But, as we all know, with great power comes great responsibility: “There’s this whole depth we are getting into, the expectations are so much higher, people don’t just expect it to work they expect it to work the way they want it to and in a way they can adopt.” So, with so much expected and required of Data Scientists in 2020, building the right team is more important than ever. However, many businesses, Vin believes, are yet to get their hiring processes right: “A lot of the measures that we use to sort of evaluate employees are fictional – when you say years of experience, it has no correlation to employee outcomes or the quality of employee you get long term. It’s the same thing as college degree, there’s no correlation.” So when Vin is trying to build a highly specialised team, what does he do? “We have to teach specialisation, we can’t expect it. We can’t bring someone in and call them a Data Scientist and hope that they train up. You end up with teams that are exactly the same because they have hired the same people, people who reinforce the bias of what they do, and that is where true leadership needs to come in.” A specialised team made up of individuals who bring their own ideas to the table is more important than ever, particularly as businesses demand more from their Data teams. Gone are the days of one-size-fits-all models. Businesses now want something tailored to them: “Custom models are huge. The “import from…” Machine Learning development from three years ago adds value when it comes to wrangling and doing the Analysis, but when it comes to creating models companies are now expecting it to become a competitive advantage. Companies no longer want the same model that everyone else has, now it has to be differentiating.” These smart, customised models, he adds, will help businesses through the current pandemic. “The best models right now are adapting rather than reacting.”  However, he’s sceptical about the Data Science community becoming too preachy:  “When it comes to COVID-19 one message I want to send to the Machine Learning and Deep Learning community is ‘shut up’. We don’t have the Data! We have so many Data Scientists talking about something that’s very important to get right. If you get it wrong the consequences and the credibility we will lose as a field is enormous.” Indeed, discussions about the lack of quality Data on COVID-19 are widespread at the moment and raise concerns for Vin: “What the last two and a half months has revealed is the danger of bad Data, the danger of assumptions that are hidden in Data that hasn’t been looked over well or wasn’t gathered well and was fed into these models that now aren’t robust. Of course, no model can account for something this drastic, but they should still be performing far better than they are right now.” Despite these concerns, Vin believes any change in the world brings about opportunities for those in the Data and technology space. “What I’ve been trying to do ever since I joined the technology space is figure it out. It’s constantly evolving and it’s constantly changing. That’s really what has driven my journey. I’m always trying to figure out ‘what’s next’ over the next five years, ten years whatever it may be.” If you’re looking for your next Data Science, Machine Learning or Deep Learning role, or want to build out your own highly-specialised team, we may be able to help.  Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.   

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