Related papers: Extending Machine Learning-Based Early Sepsis Dete…

This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A supervised learning approach was adopted, wherein an XGBoost…

Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance in prognosis and treatment. This work demonstrates the…

Sepsis, a critical condition from the body's response to infection, poses a major global health crisis affecting all age groups. Timely detection and intervention are crucial for reducing healthcare expenses and improving patient outcomes.…

Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision. We show that this technique is an ideal fit for…

Sepsis, characterized by a dysregulated immune response to infection, results in significant mortality, morbidity, and healthcare costs. The timely prediction of sepsis progression is crucial for reducing adverse outcomes through early…

In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization. Extensive research has sought continuous improvement in existing models to enhance…

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer…

Diabetes mellitus is a chronic metabolic disorder that has emerged as one of the major health problems worldwide due to its high prevalence and serious complications, which are pricey to manage. Effective management requires good glycemic…

The timeliness of detection of a sepsis event in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this…

Sepsis is a life threatening condition that requires timely detection in intensive care settings. Traditional machine learning approaches, including Naive Bayes, Support Vector Machine (SVM), Random Forest, and XGBoost, often rely on manual…

Sepsis is a life-threatening condition that requires rapid detection and treatment to prevent progression to severe sepsis, septic shock, or multi-organ failure. Despite advances in medical technology, it remains a major challenge for…

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power…

Cardiovascular disease remains a leading cause of mortality in the contemporary world. Its association with smoking, elevated blood pressure, and cholesterol levels underscores the significance of these risk factors. This study addresses…

Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical…

Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely intervention. By leveraging publicly available datasets, machine…

Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to…

In recent years, machine learning has become an increasingly powerful tool for supporting seizure detection and monitoring in epilepsy care. Traditional approaches focus on identifying seizures only after they begin, which limits the…

Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Previous studies employing machine learning faced limitations in…

This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data…

Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. However, it remains challenging due to data complexity and limited labeled samples. This research paper…