The medical sector is one of the sectors that has received the most attention for Artificial Intelligence solutions. We have already spoken to you on several occasions about how it is possible to make more accurate diagnoses thanks to machines that have been trained with thousands of X-rays, but the subject does not stop there, and today it is possible to read more information on the subject on the MIT blog .
In the article they talk about the startup ClosedLoop and the platform of predictive models that they have created, models designed to improve patient care.
It is a system that acts with preventive measures, to prevent patients with diabetes and heart disease problems from having problems outside the hospital and having to return in the future with new problems. Artificial intelligence (AI) can analyze large data sets to identify the patients who will benefit the most from these preventive measures.
Until now, many hospitals have done such work by hiring their own data scientists to create solutions, or by hiring one that doesn’t suit their patients. The startup ClosedLoop.ai is aiming to help with a flexible analytics solution that enables hospitals to quickly connect their data to machine learning models and obtain actionable results.
With the program, they are able to identify patients who are most likely to miss appointments, acquire infections such as sepsis, benefit from regular check-ups and more. That way each hospital can take action before it is too late. Insurers can also use ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.
During this Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for waves of patients. It is an open source tool called the C-19 Index, and it has been used to analyze millions of customers.
It is easy to predict diabetes or to predict readmissions with a generic model, but in reality it does not work, since readmissions that occur in a low-income population of a city are very different from what happens in a noble neighborhood.
