Machine learning in image analysis in ophthalmology

Martins, Thiago Gonçalves dos Santos; Schor, Paulo

doi:10.31744/einstein_journal/2021ed6860

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…In some healthcare systems, the clinical and sociodemographic data of all patients belonging to a healthcare center are now stored in an electronic medical record system, which makes it possible to easily collect specific data on a population limited to a hospital or health area. Healthcare facilities can now choose to use these datasets to perform complex analyses using high-performance computing technologies such as machine learning (ML) methods, which are nowadays applied to an increasing number of research areas, including the healthcare field [11][12][13]. ML is defined as a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to emulate the way in which humans learn while gradually improving their accuracy [14].…”

Section: Introductionmentioning

confidence: 99%

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

Queipo,

Barbado,

Torres

et al. 2024

Biomedicines

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The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

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