For example, medical imaging dealt with data complexity, overlapped detection target points and 3- or 4-dimensional medical images. Varmus, 2015 Shickel et al., 2018 Angermueller et al., 2016b Esteva et al., 2019 YinĮt al., 2019 Pastur-Romay et al., 2016 Yablowitz andĮt al., 2012 Shickel et al., 2018 Cosgriffĭeep learning in health informatics has many advantages that it can be trained without a priori, which combats the lack of labelled data and burden on clinicians. Previous studies attempted to have the right treatment, delivered to the right patient at the right time by taking into account several aspects of patient’s data, including variability in molecular traits, medical images, environmental factors, electronic health records (EHRs) and lifestyle (MiottoĮt al., 2017 Lyman and Moses, 2016 Collins and Among the various types of academia and industry, the demand for artificial intelligence in the field of health informatics has increased, and the potential benefits of artificial intelligence applications in healthcare have also been proven. In fact, they have been implemented in various fields. Machine learning and deep learning have been newly become a trend and opened a whole new research era. Highlight ongoing popular approaches' research and identify several challenges With a focus on the last five years in the fields of medical imaging,Įlectronic health records, genomics, sensing, and online communication health,Īs well as challenges and promising directions for future research. This article presents aĬomprehensive review of research applying deep learning in health informatics Irregularity, lack of label) and model (reliability, interpretability,įeasibility, security, scalability) for practical use. Despite its notable advantages, there are some challenges onĭata (high dimensionality, heterogeneity, time dependency, sparsity, Pre-process, and it is expected that it will ultimately change human life a lot Traditional models, its approach does not require domain-specific data Predict infectious disease outbreaks with high accuracy. Relationship between genotypes and phenotypes, explore new phenotypes, and
Identify cancer sites, identify drug effects for each patient, understand the Deep learning can help clinicians diagnose disease, The demand forĪrtificial intelligence in the field of health informatics is also increasingĪnd we can expect to see the potential benefits of artificial intelligenceĪpplications in healthcare. As more data and better computational power becomeĪvailable, they have been implemented in various fields. Machine learning and deep learning have provided us with an exploration of a The Hong Kong University of Science and Technology DeepHealth: Deep Learning for Health Informatics