ArticleEinstein (Sao Paulo, Brazil)2023
Outcome prediction for critical care patients with respiratory neoplasms using a multilayer perceptron neural network.
Article in Einstein (Sao Paulo, Brazil), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it, 1 citations in OpenAlex.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Comparative Analysis of Logistic Regression and Machine Learning Algorithms for Predicting De Novo Stress Urinary Incontinence or Symptomatic Worsening Following Pelvic Floor Reconstruction: A Multicenter Study.International journal of women's health · 2026Article
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Abstract
objectiveThe variation in mortality rates of intensive care unit oncological patients may imply that clinical characteristics and prognoses are very different between specific subsets of patients with cancer. The specific characteristics of patients with cancer have not been included as risk factors in the established severity-of-illness scoring systems and comorbidity scores, showing limitations in predicting mortality risk. This study aimed to devise a predictive tool for in-hospital mortality for adult patients with a respiratory neoplasm admitted to the intensive care unit, using an artificial neural network.
methodsA total of 1,221 stays in the intensive care unit from the Beth Israel Deaconess Medical Center were studied. The primary endpoint was the all-cause in-hospital mortality prediction. An artificial neural network was developed and compared with six severity-of-illness scores and one comorbidity score. Model building was based on important predictors of lung cancer mortality, such as several laboratory parameters, demographic parameters, organ-supporting treatments, and other clinical information. Discrimination and calibration were assessed.
resultsThe AUROC for the multilayer perceptron was 0.885, while it was <0.74 for the conventional systems. The AUPRC for the multilayer perceptron was 0.731, whereas it was ≤0.482 for the conventional systems. The superiority of multilayer perceptron was statistically significant for all pairwise AUROC and AUPRC comparisons. The Brier Score was better for the multilayer perceptron (0.109) than for OASIS (0.148), SAPS III (0.163), and SAPS II (0.154).
conclusionDiscrimination was excellent for multilayer perceptron, which may be a valuable tool for assessing critically ill patients with lung cancer.
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