BMC medical informatics and decision making
Jun 26, 2022
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...
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
May 5, 2022
OBJECTIVES: Assess a machine learning method of serially updated mortality risk.
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fai...
INTRODUCTION: COVID-19 has had a great impact on the elderly population. All admitted patients underwent cardiac auscultation at the Emergency Department. However, to our knowledge, there is no literature that explains the implications of cardiac aus...
BACKGROUND: Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), make...
BACKGROUND: This study aims to develop a machine learning-based application in a real-world medical domain to assist anesthesiologists in assessing the risk of complications in patients after a hip surgery.
BACKGROUND: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algor...
Revista peruana de medicina experimental y salud publica
Apr 1, 2022
OBJECTIVE.: To identify the clinical and epidemiological characteristics related to lethality in patients hospitalized for COVID-19 at the Simón Bolívar Hospital in Cajamarca, during June-August 2020.
BACKGROUND: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acce...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the intensive care unit. However, many of these models and their learned re...
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