An Archetype Query Language interpreter into MongoDB: Managing NoSQL standardized Electronic Health Record extracts systems.

The fast development of today's healthcare and the need to extract new medical knowledge from exponentially-growing volumes of standardized Electronic Health Records data, as required by studies in Precision Medicine, brings up a challenge that may probably only be addressed using NoSQL DBMSs, due to the non-optimal performance of traditional relational DBMSs on standardized data; and these database systems operated by semantic archetype-based query languages, because of the expected generalized extension of standardized EHR systems. An AQL into MongoDB interpreter has been developed to its first version. It translates system-independent AQL queries posed on ISO/EN 13606 standardized EHR extracts into the NoSQL MongoDB query language. The new interpreter has the advantages of both the archetype-based system-independent AQL queries and the dual-model-based standardized EHR extracts stored on document-centric NoSQL DBMSs, such as MongoDB. AQL queries are independent of applications, programming languages and system environments due to the use of the dual model, but EHR extracts featuring this model are best persisted on document-based NoSQL databases. Consequently, the interpreter allows us to query standardized EHR extracts semantically, and also affording optimal performance.

Co-morbidity analysis and decision support on transplanted patients using machine learning techniques.

The A Coruña University Hospital Complex is developing an expert system to improve the decision support for transplanted patients. The system will access the data collected during the monitoring of patients and generate a database of statistics that will aid health professionals in several stages of the transplant process. All historical data will be revised to give an estimation of the patient's parameters evolution depending on his medical record and his actual treatment. We will use two different machine learning techniques to do both clustering and classification.

From documents on paper to electronic medical records.

This paper describes the creation process of an electronic medical records (EMR) application in the Juan Canalejo University Hospital Complex (CHUJC). From the knowledge acquired through the observation of the traditional processes of managing the Patients medical records on paper a tool was developed which in principle was thought of to classify electronic documents associated to a patient and to which different functions of medical work have been subsequently added: visualizing clinical documents of patients, creation of new documents and following the development of patients.