Search Tag: machine learning
2024 19 Mar
New findings were presented at ISICEM 2024 evaluating a machine learning model to predict individualised treatment effects using two temporally and geographically distinct clincial trials of lower vs higher oxygen targets in critically ill patients receiving mechanical ventilation. Mechanical ventilation involves titrating the fraction...Read more
2024 06 Feb
Hospitals utilise unscheduled return visit (URV) rates in emergency departments (EDs) to determine care quality. Higher rates lead to increased costs and longer wait times for patients who need immediate care. Frequent ED visits also contribute to overcrowding, causing treatment delays and higher mortality rates. Developing predictive models...Read more
2023 13 Oct
Ongoing research and challenges regarding the use of Artificial Intelligence and Machine Learning to leverage data and identify complex interactions to address antimicrobial resistance in the ICU. The possibilities of artificial intelligence (AI), and more specifically, machine learning (ML), are being researched across almost all domains...Read more
2023 28 Jun
Delayed recognition of haemodynamic and respiratory deterioration in hospitalised patients is linked to increased morbidity and mortality. Measures have been developed to improve the recognition of acute deterioration and reduce the occurrence of major adverse events like all-cause mortality and unplanned ICU admission. One such measure is the implementation...Read more
2023 06 Feb
Many machine learning (ML) models have been developed for use in the ICU, but their effectiveness in new settings is uncertain due to a lack of external validation. A study showed that less than one-third of Food and Drug Administration (FDA) approved ML models have undergone multisite assessment. In addition, only 11% of ICU prediction models have...Read more
2022 25 Jan
An observational, multicohort, retrospective study was conducted to validate two machine-learning clinical classifier models for assigning acute respiratory distress syndrome (ARDS) subphenotypes. Two ARDS subphenotypes with distinct biological and clinical features and differential treatment responses have been identified in seven individual cohorts....Read more
2020 15 Jan
Critical Care Ultrasound (CCUS) has progressed by leaps and bounds, and will continue to push boundaries, with techniques being modified to suit evolving clinical needs and new applications. Introduction With roots traceable to sonar technology developed for underwater listening and submarine detection, the era of medical ultrasound...Read more
2020 15 Jan
Improving early recognition of sepsis in the Neonatal Intensive Care Unit using machine learning models and electronic health record data. Neonatal Sepsis - Incidence and Outcomes Despite advances in knowledge and medical care, sepsis remains a major cause of morbidity and mortality in infants worldwide, claiming the lives of one...Read more
2020 15 Jan
Subject Index for Volume 19, issues 1-4, 2019. Issue Pages Link Issue 1 1-64 https://iii.hm/10cv Issue 2 65-128 https://iii.hm/10cw Issue 3 129-192 https://iii.hm/10cx Issue 4 193-256 https://iii.hm/10cy Acute Respiratory Distress Syndrome Use of sedation...Read more
2019 21 Oct
Sepsis is a major cause of death among hospitalised patients. It affects nearly 6% of all admissions and results in in-hospital mortality of greater than 15%. Early detection could reduce mortality, as it would result in the timely implementation of evidence-based interventions. Clinical researchers have developed and implemented a machine-learning...Read more
2019 23 Aug
Acute Kidney Injury (AKI) is a common occurrence for critically ill patients in the ICU, and its early diagnosis has proven to be challenging. The accuracy of the online, machine-learning-based prediction model, AKIpredictor , was analysed for its use in a clinical setting. The study, which took place over five ICUs in Belgium, compared the predictions...Read more
2019 20 Mar
At this years 39th International Symposium on Intensive Care and Emergency Medicine , Professor Jerry Nolan , a consultant in anaesthesia and intensive care medicine at the Royal United Hospital, Bath, talked about new developments in CPR during the Max Harry Weil Lecture, one of the most important presentations at #ISICEM19. Dr. Max Harry Weil...Read more
2019 27 Feb
The increasing number of emergency department (ED) visits often correlates with ED crowding and delays in care. This problem highlights the need for ED triage systems that accurately differentiate and prioritise critically ill from stable patients, enabling efficient allocation of finite ED resources. Currently, the Emergent Severity...Read more
2019 30 Jan
Enrolling seriously ill patients in intensive care units for clinical trials is often a big challenge for investigators. This is one reason why there is a dearth of evidence-based guidelines in critical medicine. For instance, ICU doctors often face a dilemma when it comes to ordering lab tests for specific patients. While ICU doctors are...Read more
2018 04 Dec
Cardiac arrest, a leading cause of admission to the intensive care unit (ICU), is associated with high mortality. Current illness severity scores perform poorly in predicting survival for this patient group. New research from Australia shows machine learning (ML) techniques can significantly increase the accuracy of estimating survival for ICU patients...Read more
2018 20 Nov
Machine learning can be used to analyse electronic health records and predict the risk of emergency hospital admissions, according to a new study from The George Institute for Global Health at the University of Oxford has found. You might also like: Hospital readmissions and machine learning The study is published in PLOS Medicine [open...Read more
2018 06 Jun
New research shows that a machine learning approach to predicting ICU readmission was significantly more accurate than previously published algorithms or prediction tools. Implementation of this approach could target patients who may benefit from additional time in the ICU or more frequent monitoring after transfer to the hospital ward, according...Read more
2018 06 Mar
Machine learning has proven to be useful for developing prediction models for outcomes in intensive care unit patients. A new study comparing machine learning techniques for predicting central line-associated bloodstream infection (CLABSI) shows supervised artificial intelligence can accurately predict CLABSI in patients admitted to the ICU. Early...Read more
2017 15 Sep
Critical care units are home to some of the most sophisticated patient technology within hospitals. In parallel, the field of machine learning is advancing rapidly and increasingly touching our lives. To facilitate the adoption of machine learning approaches in critical care, we must become better at sharing and integrating data. Greater emphasis on...Read more
2017 24 May
Using electronic health records (EHRs) to identify patients in hospital at risk for sepsis is now possible using machine learning. Machine learning does not rely on rules, but is able to learn complex patterns in data without being programmed to do so. Researchers from the University of Pennsylvania Health System presented their study of a machine-learning...Read more