Semantic Interoperability: Are you training your AI by mixing data sources that look the same but aren’t?

Semantic interoperability, in particular, is the ability of different computer information systems to use and share data in a meaningful way..The data format may also not be compatible between the two different AI systems..This means that data has to often first be extracted, collected and then the data has to be converted or transformed in such a way that it can be used in the new system.In healthcare, if the electronic health record (EHR) between different health systems is different it means that the semantics or meaning of the data may be different..Archetypes also need to be evidence-based, standardized and designed by experts with domain knowledge (Read more…).Data from different systems may not have the same meaning (i.e. does not have semantic interoperability)..Transferring from legacy systems to the newer systems that have better semantic interoperability will also be a good strategy to ensure better and more effective AI systems in the future..Using standardized archetypes and normalization of data and using newer systems can thus help achieve increased interoperability between systems..There are many useful and important benefits to having a system that has semantic interoperability in different areas where AI is used.For instance, semantic interoperability would be very beneficial with health information systems since it would enable improved access to records and pertinent health information (Read more…).  ConclusionIn summary, semantic interoperability is necessary in AI systems..John Snow Labs is concerned with semantic interoperability, and provides a healthcare data normalization engine, as well as curated and versioned data sets for all the commonly used code sets and terminologies in this space.Such solutions are a requirement to applying any data analysis, let alone AI techniques, on data that has been collected from diverse sources.. More details

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