A new approach to predicting mortality in dialysis patients using sociodemographic features based on artificial intelligence

Diez-Sanmartín, Covadonga; Cabezuelo, Antonio Sarasa; Belmonte, Amado Andrés

doi:10.1016/j.artmed.2022.102478

Cited by 8 publications

(2 citation statements)

References 62 publications

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“…One such venture is DeepMind, a subsidiary of Alphabet, that has an AI system that can predict the onset of acute kidney injury up to 48 h in advance using patient data (Using AI to give doctors a 48-hour head start on life-threatening illness, 2023 ). In order to estimate the death of dialysis patients while they are waiting for a kidney transplant, a method for survival prediction based on ML technique was recently proposed using sociodemographic data (Díez-Sanmartín et al, 2023 ). Studies have demonstrated the efficacy of ML models in predicting symptoms and mortality associated with COVID-19 using evidence that is readily available to patients as well as professionals (Jamshidi et al, 2021 ).…”

Section: Applications Of Ai In Clinical Medicinementioning

confidence: 99%

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

Krishnan,

Singh,

Pathania

et al. 2023

Front. Artif. Intell.

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As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.

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Section: Applications Of Ai In Clinical Medicinementioning

confidence: 99%

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

Krishnan,

Singh,

Pathania

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Based on descriptive models, other works have used a classifier to describe cluster groups based on specific variable values [9]. On the other hand, more recent works have tried to describe clusters using Treemaps [10] or by computing the mode of clusters variables [11]. Another work proposes the combination of subgroup discovery and hierarchical clustering to obtain groups of frequent patterns and select the most relevant patterns for describing each cluster [12].…”

Section: Introductionmentioning

confidence: 99%