Projects


Project 1


Project 1: Reference Interval Tool (LabRI Tool) - 2021 to 2024


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The “Laboratory Reference Interval” (LabRI) method employs a multi-criteria automated approach, integrating various algorithms throughout different stages to identify the optimal reference distribution (related to the reference subpopulation) and calculate reference limits (RLs). The pipeline sequentially arranges algorithms and each step’s output is the next step’s input. The method was developed through an analysis of publications on recommendations, limitations, and findings in biostatistics applied to laboratory results and RIs. It emphases data distribution analysis, outlier detection, mixture deconvolution, data transformation, and distribution truncation.


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Project 2


Project 2: Development of the Educational Chatbot “LabRI Chatbot” for Clinical Laboratories - 2024 to Present


Introduction

Accreditation standards harmonized with ISO 15189 establish requirements for defining and verifying Reference Intervals (RIs) used in laboratories. To comply with these standards, laboratories must periodically verify pre-established RIs, confirming their suitability for the served population. To facilitate the implementation of these requirements in Brazil, our Working Group has launched several initiatives:

  • LabRI Package: contains the LabRI tool for estimating and verifying reference intervals, as well as the refineR tool for estimating reference intervals.;
  • Grupo Lab R Website: It offers free access to solutions developed using the R language, training slides, tutorials, links to free R language books, updates on tools and packages using R, and other useful resources for the analytical phase in clinical laboratories;

All these resources are freely accessible. However, we identified the need for greater professional literacy in this area. Therefore, the objective of this project is to develop a chatbot to educate clinical laboratory professionals about estimating, transferring, and verifying reference intervals.


Objective

To develop a Generative Pre-trained Transformer (GPT) called “LabRI Chatbot” to educate clinical laboratory professionals about reference intervals, using six instructional parameters to guide its actions and behavior.

The alpha version of the GPT “LabRI Chatbot” was internally tested for response validation on topics such as direct and indirect methods, estimation, verification, transfer, and sample size. The chatbot answered accurately and educationally to questions with answers contained in the uploaded document. For questions not addressed in the document, he answered generically but without delirium.


Methodology


Development of LabRI Chatbot (Alpha Version)

  • Customization of OpenAI’s ChatGPT.
  • Definition of six instructional parameters:
    • Technical Accuracy
    • Educational Value
    • Focus on Reference Interval
    • Personalized Responses
    • Current Standards and Practices
    • Confidence and Limitations


Configuration and Restrictions

  • Inclusion in the “Research and Analysis” category of the GPT Store.
  • Disabling all “Capabilities” options (Web Browsing, DALL·E Image Generation, Code Interpreter) to ensure responses are based solely on the internally loaded document.


Development of the Review Document

  • Creation of a technical document with information on estimation, transfer, sample size, statistical and clinical criteria for partitioning, and verification.
  • Inclusion of over 150 bibliographic references (articles and books).


Chatbot Validation

  • Conducting questions and validating the alpha version chatbot responses.
  • Review of responses by the specialists responsible for the development of the technical document.


Expected Results

  • Professional Education: Improving the literacy of clinical laboratory professionals regarding the estimation, transfer, and verification of reference intervals.
  • Facilitation of Accreditation Process: Helping comply with ISO 15189 standards, improving verification, and suitability of RIs to the served population.
  • Free and Accessible Resources: Providing easily accessible and usable educational tools.