A5026674093- Artificial Intelligence in Healthcare and Education
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare
- Sepsis Diagnosis and Treatment
Background Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict risk events. Most developed and evaluated with retrospective data, very few workflow, even fewer report performances different hospitals. In this study, we provide detailed evaluations prediction live workflows for three use cases Objective The main objective study was evaluate compare their performance these setting when using data. We also aimed at generalizing...
An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap existing healthcare data. The fairness decision-support tools is at risk when developed based on biased This paper aims to quantitatively assess gender bias prediction models. We aim generalize our findings by performing investigation multiple use cases different hospitals. First, we conduct a thorough analysis source data find gender-based...
Background In recent years, machine learning (ML)–based models have been widely used in clinical domains to predict risk events. However, production, the performances of such heavily rely on changes system and data. The dynamic nature environment, characterized by continuous changes, has significant implications for prediction models, leading performance degradation reduced efficacy. Thus, monitoring model shifts evaluating their impact are utmost importance. Objective This study aimed...
<sec> <title>BACKGROUND</title> In recent years, machine learning (ML)–based models have been widely used in clinical domains to predict risk events. However, production, the performances of such heavily rely on changes system and data. The dynamic nature environment, characterized by continuous changes, has significant implications for prediction models, leading performance degradation reduced efficacy. Thus, monitoring model shifts evaluating their impact are utmost importance. </sec>...
<sec> <title>BACKGROUND</title> Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict risk events. Most developed and evaluated with retrospective data, very few workflow, even fewer report performances different hospitals. In this study, we provide detailed evaluations prediction live workflows for three use cases </sec> <title>OBJECTIVE</title> The main objective study was evaluate compare their performance these setting when...