# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 5 Applications in Healthcare from Biomedical Information  
The past decades have witnessed a massive growth in biomedical information, such as genomic sequences, electronic health records (EHRs), and biomedical signals and images [19], which have led healthcare to a new era [48; 51]. Given their demonstrated performance in different domains, BD and DL introduce exciting new opportunities for managing this large amount of medical data [48] (Figure 1). Although physicians will not be replaced by any algorithm in the foreseeable future, these technologies can definitely assist them to make better decisions in certain areas of healthcare [51]. BD and DL have been gaining importance in recent years in the healthcare domain due to their capability to determine associations and relationships between risk factors and diseases. Although a large number of EHRs are generated worldwide each day, not all of them can be considered due to many methodological issues, such as data quality, data inconsistency and instability, multiple scales, incongruences and incompleteness, and legal issues. Moreover, it is a very complex and multi-disciplinary field involving physics, signals and systems, math, instrumentation, computer science, biology, physiology, and medicine.  
The application of BD and DL in healthcare includes areas such as the individual disease diagnoses, disease prognoses, disease prevention and prediction, and also designing tailored health treatments based on lifestyle. One of the most famous example applications in medical care is the \"Precision Medicine Initiative\" project, which was promoted by the US President [PERSON] in 2015 [108]. It aimed to map the human genome of 1 million US citizens, find precise defects in the primary cause of a disease at the genetic level for a group of people, and ultimately guide the development of a new generation of drugs able to address precisely a subset of molecular problems shared by patient groups with a given disease [109]. Another example is the IBM Watson for healthcare [110; 111; 112], which is able to incorporate clinical, pathological and genetic characteristics and then to propose standardized clinical pathways and individualized treatment recommendations from those characteristics. The Enlitic company (San Francisco, CA, USA) [113] was able to improve diagnostic accuracy in less time and at a  
Figure 1: Illustration of how biomedical information (biomedical signals and images, genomic sequences, or EHRs) can be managed by BD and DL with the aim of assist physicians in particular domains of healthcare.  
reduced cost (when compared to traditional diagnostic methods), by means of DL networks to analyze medical images (such as X-rays and MRIs). Another exemplary use is Google Flu Trends [114], which was able to predict more than double the proportion of doctor visits for influenza-like illness than the Centers for Disease Control and Prevention by using surveillance reports from laboratories across the United States.  
The important role of these technologies in medical and clinical research has enabled major research institute centers and funding agencies to invest in this area. In addition to clinical information system, BD and DL technologies can also be applied to hospital management information systems to achieve: lower cost, fewer hospital stays and its length, control of insurance fraud, change detection in disease patterns, high quality healthcare and a better efficiency of medical resource allocation. In the following paragraphs, several application examples are presented according to the different nature of biomedical information: biomedical images, biomedical time signals and other biomedical data like those from laboratory results, genomics and wearable devices.