Using machine learning to predict laboratory test results

By E. Wilkes. 2016

54 citations recorded by the source corpus.

Subfields: Artificial Intelligence

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “Copmared to logistic regression, the Naive Bayes classifier is capable of learning even in the presence of some missing values and relies less onmissing data imputation [94], [95].”
• An Optimal Policy for Patient Laboratory Tests in Intensive Care Units (2018, unknown): “More recent work looked at predicting the results of ferratin testing for iron deficiency from information in other labs performed concurrently [8].”