The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center has a robust research team with dedicated scientists, cardiologists and surgeons. Research scientist Cedric Manlhiot studies health data and better ways to collect and store information. This data helps doctors provide better treatment plans for their patients.
Learn more about this groundbreaking heart research at hopkinschildrens.org/heart-research
My name is select Mario. I'm a research scientist at johns Hopkins University and the director of the cardiovascular analytical intelligence initiative or C. V. I Square I study health data every time a patient enters a hospital or see a doctor. It creates data now that data is usually stored in electronic medical record to track the patient over time but not much else is being done with that and there's a lot of untapped potential here. My work is to develop tools that use this data to help doctor better treat patients. First we have to design solution to better collect and store health data. Health data is currently stored in a way that is very difficult to access. Sometimes we can get to that data. Sometimes some data is missing and sometimes it's in format that we can't regularly use. If you take think of health data as a book it's like a situation where some pages are missing. Some chapter are written in invisible ink. Um and some sentences are just written in a different language. Our work, the first part of our work is really to develop these tools that would make the book better organized and easier for us to read. Well the second step is for my team to start looking at the data and search for use pattern in that data. A useful pattern in data is something that we can mathematically express and then convert into a useful tool for clinician for example predict the risk of an outcome or the diagnosis to do this. We use statistical methods and when we are dealing with very complex data, we use a method called machine learning, which is a branch of artificial intelligence. For example, in one of our NIH funded study, we are creating tools to better diagnose treat and manage Children who develop multi system and factory syndrome and Children, which is a rare but severe complication of covid infections with these tools and ends. Doctors will have a better sense of which therapy are more likely to be successful in a given patients or are likely some complications will be. They can use this to tailor therapy for each patient according to that information or to take preventative measure to prevent complications that are very likely in the immediate future. Finally, the last step for us is to create computer program that will allow those tools to be deployed at the bedside so they they can be used in real life clinical situation. We also create programs that monitor our tools are used by physicians and other health care providers and monitor our impact patient outcomes. In the end, we want to make sure that our work is useful and positively impact patient outcome. In high school, I once went back to my parents and complained that I would never use anything that I'm learning in ap mathematics. So it took quite a few twist and turn for me to lead a pro a research program where I interact with numbers every day. But through the years I learned how mathematics can be used to organize data and derive useful insights from it. It is challenging like solving a puzzle without having a picture of the end product, but it's fascinating and in medicine it can also produce something that might be beneficial for patients. Over time we noticed that even though we were creating a lot of very useful products using health data, many of them could not reach the patients or the doctors. And this was for a variety of reason. So when we created this C. V A. I Square, our current research program, we decided to focus on the entire life cycle of health data and data related product. Children with heart disease represents a unique challenge in terms of health data, mostly because every patient is unique and they are very complex pathologies. Many of the methods were typically used to generate insights from health data can't be used in Children with heart disease because they are so uniquely complex and each one is different from the other. That means that we have to constantly redevelop new method and push the technological frontier so that those typical method can be applied to this very special population
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