Using AI to Predict Heart Attacks

One of the areas of AI that has always fascinated me for going on a decade now is the use of artificial intelligence in medical advancement. I had in 2010 started considering how the use of data analysis on a large and complex scale could lead to early diagnosis of many of the diseases that still to this day are so rampant.

It is with interest then that I came across a new article discussion a presentation at the 2023 Resuscitation Science Symposium of the American Heart Association which suggests that artificial intelligence (AI) will be key to predicting sudden cardiac death as well as evaluating an individual's risk, with the potential to prevent future fatalities.

For example, I firmly believe that AI with enough given data and diagnose and predict serious diseases far enough in advance that they can be prevented. This includes fatal illnesses such as pancreatic cancer which in most cases by the time it is diagnosed has a 95% fatality rate. What if AI could detect symptoms and patterns that could detect the onset of this deadly disease years in advance so it could be treated before it became a fatal diagnosis.

This is why when I read about the medical conference scheduled for this weekend in Philadelphia, I was ecstatic to see that AI predictive methods was a key area of discussion. The event's main function is to discuss the latest developments in the management of cardiopulmonary arrest and severe traumatic injuries including sudden cardiac death ( SCD ) which is responsible for up to 20% of all deaths in modern society.

Predicting SCD it is a formidable challenge, and traditional methods often struggle to pinpoint high-risk individuals, particularly at the individual patient level.

Utilizing AI, the scientists created around 25,000 personalized health equations to evaluate the data and identify individuals at high risk of sudden cardiac death. Furthermore, they crafted customized risk profiles for each study participant.

Researchers though have begun using AI with considerable success to move past conventional risk factors and using artificial intelligence to incorporate all available medical records on a text group of patients to see how accurately heart failure could be predicted.

Dr. Xavier Jouven, the lead author of the study and a professor of Cardiology and Epidemiology at the Paris Cardiovascular Research Center, led a scientific team that harnessed artificial intelligence to analyze medical data collected from registries and databases in Paris, France, and Seattle. They analyzed records from 25,000 individuals who had experienced sudden cardiac arrest and compared them to data from a sample of 70,000 individuals from the general population.

This matching process considered factors such as age, sex, and residential area. The dataset comprised over 1 million hospital diagnoses and 10 million medication prescriptions, drawn from medical records spanning up to a decade prior to each recorded death.

Initial results suggest that AI-driven research possesses the capability to identify individuals with a risk of sudden cardiac death exceeding 90% and was able to predict approximately 25% of all cases of sudden cardiac death in the control group study.

Utilizing AI, the scientists created around 25,000 personalized health equations to evaluate the data and identify individuals at high risk of sudden cardiac death. Furthermore, they crafted customized risk profiles for each study participant.

These personalized risk equations integrated diverse individual medical factors, such as the treatment of high blood pressure, a history of heart disease, and mental and behavioral conditions like alcohol abuse. The research aimed to identify and collaborate factors that could either elevate or mitigate the risk of sudden cardiac death, quantifying the risk within a specific time frame.

Initial results suggest that AI-driven research possesses the capability to identify individuals with a risk of sudden cardiac death exceeding 90% and was able to predict approximately 25% of all cases of sudden cardiac death in the control group study.

The study does have certain limitations, including the potential applicability of the prediction models beyond the scope of this research. Additionally, electronic health records may contain proxy data rather than raw information, and variations in data collection practices among different countries may necessitate adjustments to the prediction models.

I will be bringing you further news from the medical science world and it's use of AI over the coming months and years. If you enjoy my AI articles and posts, please follow me on LinkedIn so you don't miss any future AI news.