## 3 Health Data Formats in Medical Systems  
The application of machine learning in healthcare very often greatly depends on the format in which medical data are supplied to the system. The success of a machine learning model may be determined by how relevant and how well the data are represented. As a result, different formats are used to tailor to specific purposes. The formats in which medical data are stored still remain very different and somewhat disorganized in the context of usage for machine learning. To combat heterogeneity, such data are normally integrated into existing dataset through different points, all for different types of data. In the context of health systems, the data that get used in machine learning, most health data used can be split into three major types: tables, images and plain text (patient records). With tables being the most friendly format for machine learning out of three, it is no surprise that most systems, both in healthcare and in general, are accustomed to accepting this type of data. However, in healthcare, it is more of a \"middleman\" between machines and humans, and tables have to be filled with data from other (likely non-table) sources before they can be passed to an AI. Images can present all kinds of scans and diagrams received from patient testing (CT, X-rays, ultrasound, etc.). Along with the text files, they are one of the primary sourcesof information for physicians, since a lot of data about the patient's disease is usually discovered through tests. They are also the hardest to automate due to an individual solution having to be built for a specific purpose. They are mostly processed with neural networks due to Convolutional Neural Networks (CNN) excelling at image processing and their ability to learn smallest details. Finally, plain text, aka patient records, is a format where the physician leaves most of their observations. It can be difficult to process for machine learning systems due to the fact that most of the records contain natural language. Transformation of such data from text to organized tables is a relatively new task in the machine learning field. Multiple approaches should be used and/or combined here to ensure that the medical data are processed and transformed accurately. However, each of the formats has its own problems one must be on the lookout for when designing a health system (Figure 1). While the common data types used in healthcare being well-known for some time, there are numerous problems in the case of each when it comes to their usage in machine learning, due to the fact that even the data of the same format can still deal with the problem of heterogeneity. As already mentioned, heterogeneity is the issue of data having high variability of types and formats. Ambiguity and low quality may be present due to missing values, high data redundancy, and untruthfulness. The integration of heterogeneous data remains a challenging task these days. There are the following types of data: heterogeneity [35]:  
* Syntactic heterogeneity happens if data sources are expressed in different languages;
* Conceptual heterogeneity, alternatively known as semantic heterogeneity or logical mismatch, stands for differences in modelling the same domain of interest;
* Terminological heterogeneity represents differences in names and labels for the same entities when referring from different data sources;
* Semiotic heterogeneity, or pragmatic heterogeneity, denotes different interpretation of entities by people. Tables are most likely to manifest at least one of these types of heterogeneity, due to usually being derivative from other data formats. Even similarly structured tables may be rendered unusable together due to frequent cases of terminological heterogeneity. In different sources, features may be named differently or with different units. An even bigger problem can be encountered in the form of semantic heterogeneity, which may arise as a result of different units being used to describe numerical data.