In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in ...
BMC medical informatics and decision making
35761275
BACKGROUND: A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared wit...
Journal of the American Medical Informatics Association : JAMIA
35751440
BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to im...
BACKGROUND: The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addre...
Hong Kong medical journal = Xianggang yi xue za zhi
38545639
INTRODUCTION: This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong ...
International journal of surgery (London, England)
38489557
BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency general surgery (EGS). The American Association for the Surgery of Trauma recommends developing risk assessment tools specifically for EGS-related disease...
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiolo...
BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians i...
BACKGROUND: Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model ...