**2.1 Data source**

The CIS grouping software for Georges Pompidou European Hospital was used to extract the base of hospital summary report (HSR) from the digestive, oncologic, and orthopedic surgery units. This base of HSR contains a year of hospital stays coded and validated by the department of medical information. Each HSR has medical benefit entities and temporal and administrative patients' data. The entity of medical benefits represented by the medical act and diagnostics appears in three different types of diagnoses: the associated significant diagnosis (ASD), principal diagnosis (PD), and related diagnosis (RD). Moreover, there are various types of acts such as a surgery act and medical technical act.

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**Figure 1.**

*Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding*

entities in which the related act and diagnosis are sub-entities.

**2.3 Correspondence rules with the conceptual/logical level**

(the tree) and the content (the text in the sheets).

is no preservation of hierarchical organization.

**2.4 Document-based model**

*Hospital stay conceptual data model.*

The goal of a document-oriented database is the representation of more or less complex information that satisfies the needs of flexibility, richness of structure, etc. Modeling of a big data-coding warehouse is a function of the hospital stay's structuring elements. The hospital stay is a document represented by a pair (key, value) and has a tree-shaped structure. The stay entity is the root of the tree. The entities (key) and values for coming up with a conceptual model (**Figure 1**) were designed from the base of HSR. The main entities were defined as an object class that consisted of stay, patient, movement of the patient between the different clinical unit entities, and terminology. The medical benefit entity is a sub-document of stay

The entities have a heterogeneous structure and multiple values. The relationship between them is of cardinality "n,m." To ensure that they are unified, it is important to have a flexible open data schema whose structure can be extensible and is able to adapt to more or less important variations. The rank of medical acts, as well as the rank of diagnoses, can easily complete the corresponding type of diagnosis and acts. The rule used to convert the conceptual model to a document-based

The coding data are arranged in rows and columns through the big data warehouse model. It is structured into the nested documents in document-oriented NoSQL. The entity stay is a set of facts (patient, movement, medical benefit, etc.) where an instance turns into a nested document. Every sub-entity (movement, medical benefit, etc.) is changed into a nested document. Every sub-sub-entity (Diag, Act, etc.) is changed into a nested document. Every entity also changes to a nested document held in the same document as the fact instance. A stay that has only one main diagnosis is converted into a document that is turned into only one sub-entity Diag. The attributes (key, values) of the other sub-entities are null. There

Under the prism of rules published in Section 2.3, the document-oriented model considers each hospital stay as a key associated with a value. The values can be either

model is based on the tree model so that the code associates the structure

*DOI: http://dx.doi.org/10.5772/intechopen.85749*

**2.2 Conceptual data model**

*Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding DOI: http://dx.doi.org/10.5772/intechopen.85749*
