How AI based Clinical Decisioning Tools Can Lower Premature Deaths From Chronic Diseases
Chronic diseases are responsible for a significant portion of the time and money that the healthcare industry spends on an ongoing basis. In fact, it is estimated that approximately 60 percent of American adults suffer from at least one chronic disease.
n a new study exploring AI predictions of premature patient deaths that was conducted by the University of Nottingham, a number of machine learning and cox regression models were compared in terms of their prediction performance.
Machine Learning Algorithms Outperform Traditional Approaches
Models developed took into account the demographics, biometrics, and lifestyle factors such as the dietary habits for each individual. After making the prediction, the results were tracked and directly compared against the 2016 death record information provided by other national sources. This allowed them to calculate how accurately the ML algorithms could predict premature deaths caused by chronic diseases. Deep learning model and Random Forest model out performed multivariate cox regression model which was focused on limited number of variables.
How AI Identifies the Risk of Chronic Diseases More Effectively
When compared against standard human-developed prediction models, which in this case was a Cox regression and a multivariate Cox model, the random forest and deep learning AI models were shown to be significantly more accurate. In fact, they were approximately 10 percent more accurate than the Cox regression and over 3 percent more accurate than the multivariate Cox model.
Even this slight increase in predictive accuracy can have significant impacts in care delivery throughout the United States and with active feedback loops, these models can be improved to reduce the 23 to 25 million premature deaths that the U.S. suffers every year.
By replacing the traditional approaches for model development with Deep Learning architectures and algorithms the next generation of clinical decisioning tools can be prospectively deployed into clinical settings. These more promising AI models can potentially detect and save millions of lives that are prematurely dying from chronic diseases. This mechanism can provide the clinicians an observation window wherein an adverse event is foreseen, and risk level assessed with an opportunity to administer appropriate preventative care and potentially avert the complications and premature death.
Through further testing, this team of healthcare data scientists will be able to determine just how successful this AI predictive model would be with other population groups. With enough progress, these models could soon be successfully used by doctors and other medical professionals in the treatment of chronic diseases as part of routine healthcare.Of course, the prediction of premature deaths in chronic disease patients is only one of many white space opportunities where AI can be used to improve clinical care. To get more industry news regarding the use of artificial intelligence in clinical care, make sure to subscribe to our blog at RediMinds.
In the midst of this robotic revolution, now and then some real game-changing technologies emerge that accelerate the pace of innovation.
The introduction of Generative Adversarial Networks (GANs) into the wild world of AI is the perfect example of one of those game-changers. GANs function by approximating generative models. At a high level, GANs is an approach to machine learning involving two algorithms – a “generator” and a “discriminator” – that are pitted against each other to improve the abilities of the overall system.
Let’s dive into exactly how this works:
Venturing into a new era of GANs, researchers from UC Berkeley published an amazing work on motion retargeting that can make fake videos showing amateurs dancing like professional dancers.
They used custom GANs long with other video generation and smoothening techniques to transpose dance moves to amateurs with no advanced skills from a source video.
Video source: Caroline Chan
This is just the beginning of what’s possible with Generative Adversarial Networks (GANs).
How Does This AI Dancing Process Work?
In the research paper, the authors state:
“With our framework, we create a variety of videos, enabling untrained amateurs to spin and twirl like ballerinas, perform martial arts kicks or dance as vibrantly as pop stars.” AI dancing is the process of taking a video of a professional dancer (Dancer A). Deep learning video generation detects the pose of dancer A and transposes this onto a source video of an amateur dancer (Dancer B).
Essentially, making anyone a pro dancer on video through machine learning and deep learning applications.
Differentiating Between AI, Deep Learning & Machine Learning
Artificial Intelligence (AI)
Artificial intelligence is the general category that all of these terms belong to. It’s a reference to any intelligence performed by a machine that results in an optimal or suboptimal resolution to a problem.
Deep Learning: The Human Connection
Inspired by the function and structure of the brain (specifically the networks of interconnected neurons) – deep learning features algorithms that mimic the biological structure of the brain.
Machine Learning: Cognition Signs
Machine learning is the use of algorithms to interpret data, draw conclusions from it, then go on to make predictions or determinations about something in the world.
Where Did GANS Originate?
What started as a night out with fellow students in 2014 ended with the development of a whole new avenue that would shape the future of artificial intelligence.
While Ian Goodfellow celebrated the recent graduation of a fellow doctoral graduate, he was asked by a peer to help him with a project that would enable a computer to create photos independently.
At that time, technology that used neural networks did exist – where algorithms were modeled similarly to neurons in the brain – to create “generative” machines able to produce plausible data independently. But the results of this process were still very primitive and prone to errors like blurred faces or missing ears.
To find a solution for the project the other students were working on, massive amounts of number crunching would be required, which would not work.
Goodfellow returned home and pondered on this challenge which led to an idea. He wondered what would happen if you pitted two neural network systems against each other, essentially competing to solve the problem.
So, he gave it a shot and coded his software.
It worked the very first time. And the concept of Generative Adversarial Networks (GANs) was born.
Taking these methods a step further, the “do as I do” motion transfer system performs video generation while preserving intricate details like the subjects’ faces.
Recent advancements in image generation and general image mapping frameworks have laid the groundwork and enabled the authors to create a mapping from pose to target subject.
Many more purposes exist for GANs, especially in image generation because of their remarkable ability to replicate detailed, high-quality images.
Future Applications of GANs
Room for Improvement
Upon observation, you will notice that the subject dancer isn’t perfect. You will catch some waviness and choppy motions.
Nonetheless, “Everyone Can Dance” is a shining example of what modern AI is capable of. Some fine-tuning and a little time behind this development will render methods indistinguishable from the real subjects.
Causes for Concern
Obvious scenarios render the concern of GANs and motion transfer technology being used to implicate people in false scenarios.
In recent years, Reddit cracked down on deepfake pornography and the political sphere is concerned about false information spreading via fake videos. Furthermore, down to a very fundamental level, endless scenarios exist as to how these methods can cause interpersonal conflicts.
Future Developments
Many applications of machine learning have been facilitated by the emergence of deep learning methods. Collectively, these improvements are advancing the AI field.
The technological ability to break down tasks into ways that utilize various machine assists will facilitate future developments, including exciting developments like:
Researchers are even asking questions about how GANS and motion re-targeting can be exploited to solve some healthcare problems. For instance: Can this technology be transferred to perform motion re-targeting from an expert surgeon to a novice surgeon?
We believe the technologies needed to codify an expert surgeon’s proficiency into an AI system are here and we will see them in action soon. Such tech will empower novice surgeons and provide care at the same level as a skilled expert with years of experience.
Needless to say the AI spectrum is evolving at lightning speed and there’s no telling how far this is going to take us and how fast. Remember: this is only the beginning.
“Deep Learning is a superpower. With it, you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that isn’t a superpower, I don’t know what is.” – Andrew Ng, Founder of deeplearning.ai
RediMinds are working on numerous exciting AI projects right now, helping businesses and healthcare provides change the face of their industries. Stay up to date with our latest projects and exciting AI news by subscribing to our monthly newsletter.
Artificial Intelligence will Empower Patients and Physicians
In the past, the course of a patient’s illness (especially those with terminal diseases) were filled with uncertainty. Doctors were left to make the best decisions which were often the same for every patient battling a specific illness, even though their course leading up to such were unique. What if doctors know with 90 percent or more accuracy how effective a particular treatment is on a specific patient cohort? Would decisions such as calling in family members or treatment options change with such foreknowledge? As said by Thomas R. Insel, MD, a psychiatrist and neuroscientist, is a co-founder and President of Mindstrong Health: ‘Deep Medicine summarizes hype and threat, then takes us to a place no one had gone.’ This holy grail of healthcare predictive data is now made possible with artificial intelligence (AI). Healthcare quality and patient outcomes stand for drastic improvement once these methods and algorithms are in place and used by physicians to guide care choices. Take a look at how Rediminds is taking on data in healthcare with our collaborative platform Ground Truth Factory to support the development of AI-enabled surgical safety tools.
AI to Improve and Personalize Healthcare with Big Data
Deep learning, a form of machine learning, is now being applied to electronic healthcare records (EHRs) to personalize medicine and aid in important decisions regarding patient care. A personalized predictive model (PPM) is created for individual patients by using data from similar patients. In stark contrast to global models, which were trained on all types of patients. PPMs are capable of making accurate risk scores by capturing the most pertinent data relating to a patient’s own risk factors. Studies show promise that predictive models that make use of temporal data derived from EHRs have the potential to improve the doctor’s management of chronic diseases. According to one of the authors of the study, Personalized Predictive Modeling and Risk Factor Identification Using Patient Similarity: Cluster analysis of the risk profiles show groups of patients with similar risk factors, differences in the top risk factors for different groups of patients and differences between the individual and global risk factors. Compared to global models trained on all patients, they have the potential to produce more accurate risk scores and capture more relevant risk factors for individual patients. Technical challenges in utilizing this data exist and include:
Irregular sampling of data
Varying lengths of patient history
How Personalized Predictive Models Change the Healthcare Landscape
Hospital systems can now take technology to a new place in which lives can be transformed and care outcomes optimized through the use of artificial intelligence (AI) in the form of predictive patient models. For decades, large amounts of patient data and other healthcare information has been collected using hospital EHR, including:
Diagnostic information
Patient lab results
Past surgical history
Notes captured by caregivers
Microbiology
Chart events (i.e. vital signs, intake and output)
All of this information will be used in building predictive models specific to a certain diagnosis predictive model building. On an individual level, a patient’s risk factors and complications will be analyzed with this hospital charge information to give clinicians the opportunity to accurately predict medical events, which can hold great potential for the treatment and monitoring of patients, especially those with critical decisions involving end-of-life care.
Doctors will be able to phone in family members timely for goodbyes or arrangements.
Fatal outcomes can be thwarted with insightful medical health data driving guidance and decisions.
Organ rejection can be detected earlier and interventions done to prevent fatal outcomes.
