# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 5 Applications in Healthcare from Biomedical Information
### Other Non-Electrical Biomedical Data  
In addition to the electrical biomedical signals, a vast amount of medical information is achieved from many other non-electrical sources. In the field of signals, we have mechanical signals (e.g., the mechanomyogram), acoustic signals (e.g., phonocardiogram, breathing), chemical signals (e.g., pH, oxygenation) and optical signals (e.g., photoplethysmogram) [153; 154; 155; 156]. Apart from electrical and non-electrical signals, other biomedical information is collected from a large variety of data sources, such as EHR, medical imaging, genomic sequences or laboratory results [157]. The abundant and growing amount of medical data makes BD and DL become global players in healthcare and medicine by developing new applications to classify accurately diseases and disease subtypes, to recommend patient treatments or to guide the development of new therapies [21]. In this context is also precision medicine, which involves the prevention and treatment strategies that consider individual variability, attempting to ensure that the right treatment is delivered to the right patient at the right time. In [48; 117], several aspects of patients are considered by assessing large sets of data from several data sources. Thus, the integration of disparate data from different data sources [21] is one of the major challenges to be met in this area. Data originated from EHRs are becoming increasingly important in recent years. These data include substantial amounts of unstructured text, such as physical examination, clinical laboratory reports, operative notes and discharge, which remain challenging to approach [21; 48]. Natural language processing based on DL is applied to extract useful information from the text to assist clinical decision-making [51]. For example, an application to extract peripheral arterial disease-related keywords from narrative clinical notes to classify the normal and the patients with peripheral arterial disease is implemented in [158]. A semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval, is measured in [159]. More recently, in [160], the problem of prevention and intervention of Acute Coronary Syndrome (ACS) has been considered from a BD framework. A multitask learning framework was proposed in this representative paper, which is based on adversarial learning strategies [161], to predict major adverse cardiac events from EHR data of ACS patients. It was shown that, when shared information among the three subtypes of ACS is incorporated into prediction, their framework performs better than single-subtype oriented models in terms of average prediction. However, the value distributions of frequent features are similar between false-positive samples and true-positive samples, and between false-negative samples and true-negative samples, regardless of the subtypes. The authors considered that this is due to the small number of available patient samples (2863 ACS patients). This shows once again that the potential benefit of BD techniques lies not only in having a large volume of data that need to be extracted from heterogeneous EHR, but also in developing DL algorithms which are able to: (1) Deal with such heterogeneity (in the form of text, images, radiology reports, etc. ); (2) Avoid over-fitting and improve the generalization ability; In addition, (3) handle the uncertainty given by the missing data (so common in EHR). These problems are not new and they still remain open issues in the ML world. DL also applied to model continuous time data, such as laboratory results, toward the automatic identification of specific phenotypes [48], for example, the prediction of disease onset from laboratory test measures alone [162], or the modelization of longitudinal sequences of serum uric acid measurements to distinguish the uric-acid signatures of gout and acute leukemia [163]. In genomics, DL is being applied widely to improve the detection of the relevant patterns [164].