AIMC Topic: COVID-19

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Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Pharmacoepidemiology and drug safety
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...

Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Stress and health : journal of the International Society for the Investigation of Stress
Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and infant HCC may be correlated due to shared genetic, physiological, behavioural, and environmental factors, such as stressful life circumstances. The curr...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...

Ceasing sampling at wastewater treatment plants where viral dynamics are most predictable.

Epidemics
Wastewater sampling has been shown to be an effective tool for monitoring the dynamics of an infectious disease. During the COVID-19 pandemic, many sampling sites were opened in order to capture as much information as possible. However, with the pand...

BigLSTM: Recurrent neural network for the treatment of anomalous temporal signals. Application in the prediction of endotracheal obstruction in COVID-19 patients in the intensive care unit.

Computers in biology and medicine
Real-world applications, particularly in the medical field, often handle irregular time signals (ITS) with non-uniform intervals between measurements. These irregularities arise due to missing data, inconsistent sampling frequencies, and multi-sensor...

An informed deep learning model of the Omicron wave and the impact of vaccination.

Computers in biology and medicine
The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicr...

Mental disorder preventing by worry levels detection in social media using deep learning based on psycho-linguistic features: application on the COVID-19 lockdown period.

Computers in biology and medicine
BACKGROUND: The COVID-19 pandemic has had a profound effect on the daily routines of individuals and has influenced various facets of society, including healthcare systems, economy, education, and more. With lockdown and social distancing measures, p...

Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.

Health care management science
With the advancement in computing power and data science techniques, reinforcement learning (RL) has emerged as a powerful tool for decision-making problems in complex systems. In recent years, the research on RL for healthcare operations has grown r...

Detection of COVID-19, lung opacity, and viral pneumonia via X-ray using machine learning and deep learning.

Computers in biology and medicine
The COVID-19 pandemic has significantly strained healthcare systems, highlighting the need for early diagnosis to isolate positive cases and prevent the spread. This study combines machine learning, deep learning, and transfer learning techniques to ...