Editorial: Predictive Intelligence in Biomedical and Health Informatics

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 2; pp. 333 - 335
• Main Authors: Adeli, E., Rekik, I., Park, S. H., Shen, D.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Big Data; Bioinformatics; Biomedical measurement; Diseases; Informatics; Intelligence; Medical diagnostic imaging; Pathology; Special issues and sections
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2962852

The papers in this special section examine the use of predictive intelligence for bioinformatics. Big data is fueling diverse research directions in both medical image analysis and computer vision research fields. These can be divided into two main categories: (1) analytical methods, and (2) predictive methods. While analytical methods aim to efficiently analyze, represent, and interpret data, predictive methods leverage the data currently available to predict observations at present (e.g., by completingmissing observations), at previous time-points (e.g., by solving reverse problems), or at later time-points (i.e., forecasting the future). For instance, a method which only focuses on classifying patients with mild cognitive impairment (MCI) and patients with Alzheimer’s disease (AD) is an analytical method, while a method that predicts if a subject diagnosed with MCI will remain stable or convert to AD over time is a predictive method. Similar cases can be established for various neurodegenerative or neuropsychiatric disorders, degenerative arthritis, or cancer studies, in which the disease/disorder develops over time.