DATA SOURCES (LLM) FOR A CLINICAL DECISION SUPPORT MODEL (SSDC) USING A HEALTHCARE INTEROPERABILITY RESOURCES (HL7-FHIR) PLATFORM FOR IN AN ICU ECOSYSTEM

• DOI: 10.37572/EdArt_09122574115

• Palavras-chave: Data sources; Intensive Care Units; SDCC; Artificial Intelligence; Interoperability; EHR.

• Keywords: Data sources; Intensive Care Units; SDCC; Artificial Intelligence; Interoperability; EHR.

• Abstract:

  The increasing development of digital technologies in recent years has led to a concomitant increase in the availability of data in ICUs. For this vast amount of information to be useful, it must be processed and analyzed to extract meaningful knowledge. However, its size and complexity often exceed the capabilities of traditional tools, motivating ongoing research efforts to develop new analytical techniques better able to address these challenges. This effort has resulted in the maturation of disciplines such as artificial intelligence, machine learning (ML), data mining (LLM), parallel computing, and many others. Despite these advances, countless challenges of modern computing remain unaddressed, strategies to extract knowledge from complex data will undoubtedly persist as an active area of ​​research in the years to come. Real-time risk estimation of isolated pathologies provides interpretable information to understand the different risks of patients with multiple pathologies using electronic health records (EHRs) in an ICU patient. However, in this case, there are fundamental problems when formulating hypotheses such as sample selection bias, imprecise variable definitions, implementation limitations, frequency of variable measurement, subjective treatment assignment and model overfitting. Decision-making and predictive models (SSDC), on the other hand, are not yet widely developed with the currently known health systems. However, their potential based on massive data sources, allows, with the structured data of data lakes, to perform artificial intelligence (AI) to improve training and control for algorithms according to the different requirements and security that we must carry out and that we will build for the different syndromes. Here, we present a SSDC model that captures data from public ICU and we show in our report the potential data mining, for later analysis with different predictive models. We highlight that with the current results of a public ICU, through a Smart ICU there is 0.003% which corresponds to EHR data and only 3.97% is structured data in data lakes that are susceptible to useful algorithms -at the present time- for a SSDC system.