Background Sepsis is a life - threatening condition affecting over 48.9 million people globally and causing 11 million deaths annually. Despite medical advancements, predicting sepsis remains a challenge due to non - specific symptoms and complex pathophysiology. The SXI++ LNM model is a machine learning - based scoring system that refines sepsis prediction by leveraging multiple algorithms and deep neural networks. The COMPOSER model, a de ep learning framework utilizing conformal prediction, aims to improve robustness in clinical applications. This study compares the predictive performance of SXI++ LNM and COMPOSER for sepsis prediction. Methods A dataset containing 1,552,210 rows with 43 columns was cleaned and refined to 964,355 rows and 14 key features for sepsis prediction. Data were sourced from ICU patients across three separate hospital systems, including two publicly available datasets fro m Kaggle and the Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019.

It could soon save the lives of thousands of people. Sepsis is one of the most common diseases in the inpatient sector. Starting treatment as early as possible significantly increases the chances of survival. Infectious diseases that get out of control cause Sepsis. In the United States, some 200,000 people die from sepsis each year, with many deaths considered preventable according to studies.

An AI designed to catch potentially fatal sepsis before it is too late has proven effective in a large real-world study. The algorithm, called the Targeted Real-time Early Warning System (TREWS), accurately flagged thousands of cases of sepsis -- a devastating overreaction to an infection -- before they had been identified by hospital staff. "Sepsis spirals extremely fast--like in a matter of hours if you don't get timely treatment," TREWS developer Suchi Saria, the founder and CEO of medical AI company Bayesian Health, told Scientific American's Sophie Bushwick. "I lost my nephew to sepsis. And in his case, for instance, sepsis wasn't suspected or detected until he was already in late stages of what's called septic shock," Saria said.

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

You are free to share this article under the Attribution 4.0 International license. Patients are 20% less likely to die of sepsis because a new AI system catches symptoms hours earlier than traditional methods, new research shows. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," says Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins University, and lead author of the studies, which evaluated more than a half million patients over two years.

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system, created by a Johns Hopkins researcher whose young nephew died from sepsis, scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and npj Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

During your stay in a hospital, computer systems are collecting and analyzing all sorts of data about you. In the background of all the beeping and gadgetry, an electronic medical record contains thousands of bits of information about your medical history, vital signs and laboratory results. Sentara Healthcare is now deploying artificial intelligence to use that data to stop patients from contracting life-threatening sepsis. Earlier this year the system launched a sepsis prediction tool that alerts doctors and nurses when a patient is at risk of developing the deadly infection. The tool --looks at relationships in order to predict what might happen in the future,-- said Dr. David Mohr, Sentara's vice president of clinical informatics and transformation.

During your stay in a hospital, computer systems are collecting and analyzing all sorts of data about you. In the background of all the beeping and gadgetry, an electronic medical record contains thousands of bits of information about your medical history, vital signs and laboratory results. Sentara Healthcare is now deploying artificial intelligence to use that data to stop patients from contracting life-threatening sepsis. Earlier this year the system launched a sepsis prediction tool that alerts doctors and nurses when a patient is at risk of developing the deadly infection. The tool "looks at relationships in order to predict what might happen in the future," said Dr. David Mohr, Sentara's vice president of clinical informatics and transformation.