Dear Colleagues,

Healthcare information is composed of many types of varying and heterogeneous data. Semantic interoperability in healthcare is especially important as all these different types of data need to be constantly exchanged between information systems. The use of semantic resources (e.g., clinical terminologies, biomedical ontologies, linguistic resources, clinical data models, etc.) and of knowledge engineering (KE) methodologies is essential for achieving semantic interoperability and thus improving communication in healthcare and ultimately benefiting patient care. The representation of complex medical information requires adequate structuring to foster correct sharing and interpretation. More specifically, coding and classification systems are essential instruments for the unambiguous identification of clinical concepts during the process of care and during the delivery of health services. The rapid evolution of health informatics and of eHealth platforms in the world, and in Europe in particular, has improved access to medical information and permits the elaboration of learning healthcare information systems.

Moreover, in recent years, more and more artificial intelligence (AI), natural language processing (NLP), and knowledge engineering applications in the medical domain have been proposed to facilitate clinical decision support, coding support, and electronic medical record (EMR) data extraction and alignment. EMRs record a huge amount of patient data, including episodes of care, clinical history, vaccinations, medications, adverse events, diagnoses, laboratory results, and observations. The analysis and interpretation of these data is of great importance for the community, especially considering the recent need for data sharing during the evolution of the global SARS-CoV-2 pandemic.

In this context, patient empowerment and the increasing demand for continuity of care requires the publication of numerous standards to govern and guide the electronic exchange and storage of medical information. Data captured by patients is a rapidly growing area, with the proposal of huge numbers of specific applications, but interoperability with data captured by clinicians still needs innovative solutions. The application of semantic resources and technologies together with the implementation of standards are challenges for improving the semantic interoperability of clinical information and can be useful at different levels (e.g., for clinical purposes, the exploitation of data for decision support, communication with the patient, information retrieval, research purposes, and public health policy). Finally, part of the semantic interoperability challenge is how to bridge between clinical research and clinical care.

We are pleased to invite you to submit original research work covering innovative methods and meaningful applications that can potentially lead to significant advances in the field.

This Special Issue aims to raise awareness for the need to guarantee semantic interoperability in healthcare and thus to integrate structured and unstructured data and encourage the research community to develop approaches and tools based on standards, AI and KE technologies to improve semantic interoperability, and data analysis and interpretation in the field of healthcare.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

• Knowledge management of health data;
• Terminology and ontology in the semantic interoperability of electronic health records (EHRs);
• Biomedical knowledge representation and reasoning;
• Semantic extraction and annotation of biomedical text and data;
• Semantic and rule-based systems for healthcare data interpretation, coding, and presentation;
• Implementation of standards and services for semantic interoperability;
• AI methodologies for medical data analysis;
• Medical data acquisition, cleaning, and integration using AI methodologies;
• Natural language processing in medical documents;
• Global identification of medicinal products.

We look forward to receiving your contributions.

Dr. Elena Cardillo
Prof. Dr. Robert Vander Stichele
Prof. Dr. Dipak Kalra
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• semantic interoperability
• learning healthcare systems
• clinical models
• terminologies
• knowledge representation
• decision support systems
• patient empowerment and health literacy
• AI application in healthcare
• NLP application in healthcare