Recommendation Algorithms

Recommendation Algorithms enable machines to recommend a choice based on its similarity to historical data. These algorithms can assist medical professionals in making more informed decisions by predicting patient outcomes based on historical data, recommending treatment options, and suggesting preventative measures.

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"Confidence-based laboratory test reduction recommendation algorithm"

This research was conducted by Tongtong Huang, Linda T. Li, Elmer V. Bernstam, and Xiaoqian Jiang. It was published in the journal BMC Health Services Research. The study introduces a novel deep learning model with the potential to significantly reduce healthcare costs and improve patient outcomes by identifying unnecessary laboratory tests for hospitalized patients.

"A drug recommender system for the treatment of hypertension"

This research was conducted by Arthur Mai, Karen Voigt, Jeannine Schübel, and Felix Gräßer. It was published in the journal BMC Health Services Research. The study describes a digital recommendation system for the pharmaceutical treatment of hypertension and compares its recommendations with clinical experts.

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