Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

By Ashwin Belle, Mark A. Kon, Kayvan Najarian. 2013

63 citations recorded by the source corpus.

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.

Subfields: Health Information Management, Information Systems and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Alfredo Vellido
2. Yuan Luo
3. Joseph D. Futoma

In Information Systems and Management

1. Brett K. Beaulieu-Jones
2. Casey S. Greene
3. Gail L. Rosen

Citations in the corpus

• Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, background): “Recently, there is a growing interest in applying deep learning techniques to computational phenotyping [33] due to the increasing availability of the Electronic Healthcare Records (EHR) and the need for Personalized Healthcare [4, 17].”
• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Success in this area could help to enable personalized healthcare or precision medicine [261,262].”
• Big Data Analytics in Healthcare (2015, background): “Research in signal processing for developing big data based clinical decision support systems (CDSSs) is getting more prevalent [110].”
• Machine learning in critical care: state-of-the-art and a sepsis case study (2018, background): “Data is thus quickly becoming a main concern in medicine and its availability to medical experts is a necessary step towards the development of medical decision support systems (MDSS) [3–6].”
• Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness (2014, unknown): “…assessment by a medical expert is that they are fully automated and therefore unbiased towards human mistakes and can be incorporated into computerized medical decision-support systems, a growing fi eld with especially fast research progress in radiology (Stivaros et al., 2010; Belle et al., 2013).”