Emergency medicine, intensive care, and thoracic surgery were among the various departments in each of the hospitals. It was extracted from the CROSS-TRACKS cohort, a population-based Danish cross-sectorial cohort comprising a mixed rural and urban multicentre population served by four regional hospitals as well as one larger university hospital. This data was made up of information from electronic health records and included biochemistry, medicine, microbiology, and procedure codes. They used the secondary healthcare data of all residents aged 18 years and over in four Danish municipalities collected over the five-year period from 2012 to 2017. The researchers demonstrate how the explainable AI early warning score system, xAI-EWS in short, overcomes the shortfalls observed in other computational models by taking a Deep Learning approach to analysing a diverse multicentre data set. It then presents the user with an assessment of when and how to proceed with the patient’s care. Given the patient’s vital parameters and blood tests, it can identify the early signs of critical illness. This algorithm has been trained to recognise cases from the historical data that are similar to the current case. Using historical health data in the form of electronic health records, the research team’s innovative AI algorithm can predict whether a patient will develop an acute critical illness. Unfortunately, the lack of information regarding the complex decisions made by such systems hindered their clinical translation.Įxplainable AI early warning score system: xAI-EWS Previous research into electronic health records-trained AI systems has demonstrated high levels of predictive performance, providing early, real-time prediction of acute critical illness. ![]() These algorithms involve large complex neural networks, and their performance will continue to increase as they are trained with more and more data.įor clinical medicine to benefit from the higher predictive power of AI, explainable and transparent Deep Learning algorithms Deep Learning algorithms deploy artificial neural networks based on the structure and function of the brain and take advantage of copious cheap computation. Deep Learning is a machine learning method that is particularly suitable for big data sets. metamorworks/ĭr Lauritsen and the team explain that in order for clinical medicine to benefit from the higher predictive power of AI, explainable and transparent Deep Learning algorithms are crucial. Moreover, they have designed this algorithm in such a way that it supports the clinician by providing an explanation of its prediction with reference to the electronic health record data supporting it. Simon Meyer Lauritsen, a Biomedical Engineer and Industrial PhD researcher at Enversion A/S and Aarhus University, together with his co-workers, has developed a robust and accurate AI model capable of predicting if a patient will develop an acute critical illness. This strategy, however, can lead to negative outcomes for patients. With such high-stake applications, the simpler, more transparent systems are often chosen so that clinicians can easily back-trace a prediction. ![]() to achieve better outcomes in terms of quality of care and its cost. This must be reliable and comprehensive in order to inform the choices of patients, providers, payers, etc. Healthcare transparency involves making information on quality, efficiency and patients experience available to the public. This means a trade-off between transparency and the potential power of predictive medicine takes place. While Artificial Intelligence (AI) lends itself to producing earlier predictions with greater accuracy than the traditional EWS systems, these black-box predictions are not easily explained to clinicians. Early clinical predictions tend to be based on manual calculations using these clinical parameters that produce screening metrics, including early warning score (EWS) systems such as the Sequential Organ Failure Assessment score (SOFAs). ![]() Acute critical illness usually follows a deterioration of the patient’s vital signs – the routinely measured heart rate, body temperature, respiration rate and blood pressure. ![]() The time it takes medical staff to diagnose a patient’s acute critical illness has a pivotal influence on the patient outcome. Simon Meyer Lauritsen and his collaborators at Enversion, Aarhus University, and Regional Hospital Horsens in Denmark have developed xAI-EWS – an explainable AI early warning score system for the prediction of acute critical illness using electronic health records. For clinical medicine to benefit from the higher predictive power of Artificial Intelligence, however, explainable and transparent systems are essential. Early clinical predictions of acute critical illness have a vital influence on patient outcomes.
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