Collaborating Authors

Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction Machine Learning

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.

Sepsis death in The Archers all too familiar, says charity

BBC News

The death of a young mother from sepsis in BBC radio drama The Archers on Friday has prompted an outpouring of shock and emotion.

Could artificial intelligence prevent sepsis in hospital patients? Sentara thinks so.


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.

Doctors Use Big Data to Cut Sepsis Down to Size


Sepsis is a serious medical condition caused by an overwhelming response to infection that damages tissues and organs. It's unpredictable, progresses quickly, can strike anyone, and is a leading cause of hospital-related deaths. In the U.S. alone, nearly 270,000 people die each year from sepsis. Those who survive sepsis often end up in the hospital again, and some have long-term health complications. Early treatment is key for many patients to survive sepsis, yet doctors can't easily diagnose it because it's so complex and each patient is different.

AI doctor could boost chance of survival for sepsis patients Imperial News Imperial College London


Dr Faisal added: "The explosion in Artificial Intelligence applications in healthcare is currently focused on mimicking the perceptual ability of human doctors, e.g. However, doctors do more than just diagnose, they treat people. Our AI Clinician system focuses on capturing this cognitive capacity of doctors: Imagine having a doctor watching over you every second of every day, administering a course of treatment, observing how you respond to the treatment, and then adjusting the treatment as your condition evolves.