# A Survey of Deep Learning for Electronic Health Records
## 2 Evolution of EHR  
In recent years, the use of Electronic Health Record (EHR) systems [6] by healthcare organizations worldwide has increased dramatically. Compared to paper-based medical record management, electronic medical records are easier to store and use. Along with the annual increase in the adoption of EHR systems, medical data is also growing significantly. This data often contains a wealth of medical information. Although EHRs were originally designed to archive medical information, researchers have found that this type of information can provide effective support in a variety of clinical informatics applications.  
Initially, the EHR systems were designed to handle the core administrative functionalities of hospitals, allowing for the use of controlled vocabulary and labeling schemes. There are various labeling schemes such as ICD (International Statistical Classification of Diseases) for diagnostic [7, 8], CPTta (Current Procedural Terminology) for procedures [9], and LOINC (Logical Observation Identifiers Names and Codes) for laboratories [10], ATC (Anatomical Therapeutic Chemical) for drug [11], and RxNorm for medication [12]. The different labeling schemes result in standard datasets for various specialties. The number of EHR data is increasing year by year as the EHR system improves, and there have been numerous types of research on the secondary use of these data.  
Early on, traditional machine learning techniques, such as Support Vector Machine (SVM) [13], Random Forest [14], and Logical Regression, were often used to leverage the rich EHR data. However, it is still not enough to provide an effective and reliable analysis structure. Rapidly developing deep learning provides good technical support for better processing and analysis of such big medical data. Especially in the real environment of huge data volume, deep learning techniques based on deep data feature analysis have the ability to provide efficient and stable analysis results. The number of deep learning papers in this area is increasing, and it has been able to outperform traditional algorithms and move closer to the expert level. This survey checked the number of research papers connected to EHR on _Web of Science_ as of 2021, and the number of papers published increased dramatically, as shown in Figure 1. The use of EHR standards when recording medical events and related information. in computer science account for 29.488 percent of the entire application, as illustrated in Table 1. Deep learning, as opposed to typical machine learning approaches, produces better results with less preprocessing time and better prediction results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Record Numbers** & **Percentage (\%)** \\ \hline Health Care Sciences Services & 851 & 73.489 \\ Computer Science & 771 & 66.580 \\ Mathematical Computational Biology & 622 & 53.713 \\ Medical Informatics & 455 & 39.292 \\ Mathematics & 379 & 32.729 \\ \hline \hline \end{tabular} Statistical source: www.webgscience.com (accessed on 28 July 2022).  
\end{table}
Table 1: Statistics of research papers on artificial intelligence in the field of medical information.