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Woldaregay, A.Z., Årsand, E., Walderhaug, S., Albers, D., Mamykina, L., Botsis, T., et al. (2019) Data-Driven Modeling and Prediction of Blood Glucose Dynamics: Machine Learning Applications in Type 1 Diabetes. Artificial Intelligence in Medicine, 98, 109-134.
https://doi.org/10.1016/j.artmed.2019.07.007

has been cited by the following article:

• TITLE: Prediction Modeling: Basic Metabolic Panel

  AUTHORS: Philip de Melo

  KEYWORDS: Artificial Intelligence, Electronic Health Records, Prediction Modeling, Metabolic Panel Prediction

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.16 No.9, September 12, 2025

  ABSTRACT: Blood test informatics is a field that combines data science, medical informatics, and research to improve management, treatment, and understanding of diseases. This field uses health data, wearable technology, artificial intelligence (AI), and electronic health records (EHRs) to optimize healthcare. EHR informatics focuses on the following: 1) Using AI and data analytics to tailor EHR data management for individuals, 2) Identifying early signs of complications or predicting blood sugar fluctuations, 3) Using continuous glucose monitors (CGMs) and insulin pumps to collect real-time data, 4) Assisting doctors and patients with real-time recommendations. In this paper, we will discuss the basic principles of EHR informatics focusing on assisting doctors and patients with accurate recommendations and data management. We will demonstrate a new prediction method that improves accuracy compared to other forecasting technologies.