IT-Architecture Development Approach in Implementing BI-Systems in Medicine

Iliashenko, Oksana; Iliashenko, Victoria; Dubgorn, Alissa

doi:10.1007/978-3-030-34983-7_68

Cited by 13 publications

(2 citation statements)

References 6 publications

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“…Utilizing Python toolkits such as LangChain [30], and con guring distinct prompts, provides tailored interfaces for various hospital management applications. These applications include medical retrieval for patients [31], medical quality management [32], dialogue response generation task [33], as well as BI-systems in medicine [34]. This approach lays the groundwork for future applications to conform to the outlined patterns, offering a promising avenue for enhanced e ciency and functionality.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Prospects of LLMs in Hospital Management: A Perspective on Medical data inquiry

Li,

Zhang,

Zhang

et al. 2024

Preprint

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Background In light of the rapid expansion of hospital operations and the increasing digitization of medical data, there is a pressing need for efficient and intelligent methods to process and analyze large-scale medical data. Methods To tackle these challenges, the study integrates the QLoRA algorithm with ChatGLM2-6b and Llama2-6b models. These models undergo fine-tuning on a local SQL dataset, with a specific emphasis on optimizing performance, especially for simpler queries. Subsequently, we employ Prompt-Engineering with ChatGPT-3.5, enabling us to effectively leverage its capabilities and tailor its outputs to execute SQL queries. Results The comprehensive big data platform illustrates the evolution of inpatient operations, encompassing diverse information such as patient diagnoses, surgeries, medications, and examinations across various healthcare domains. The integration of the QLoRA algorithm with ChatGLM2-6b and Llama2-6b models, combined with fine-tuning on a local SQL dataset, enhances the model's performance on simple and moderately difficult SQL queries. Notably, after minimal training, the ChatGPT3.5 model closely approximates the results of human engineers in terms of SQL query performance, achieving an accuracy of approximately 90%. Conclusion The strategic utilization of Large Language Models (LLMs) and Natural Language to SQL (NL2SQL) generation enhances the efficiency of medical data analysis. This approach provides a robust foundation for decision-making in hospital management amid the evolving landscape of healthcare operations and data queries.

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Section: Discussionmentioning

confidence: 99%

Exploring the Prospects of LLMs in Hospital Management: A Perspective on Medical data inquiry

Li,

Zhang,

Zhang

et al. 2024

Preprint

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“…Iliashenko, Yu.O. Yukhnovska [6] propose to add the article: "Costs for repayment of tax liabilities", "Imported materials" for the nomenclature of articles on production costs in construction. As results, the adoption effectiveness of managerial decisions and profit of the industry will be increased.…”

Section: Introductionmentioning

confidence: 99%

Construction Work Cost Determination

Koba¹,

Myronova²,

Byba³

2018

IJET

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The main Ukraine construction industry tendencies during 2010-2017 are described in the article. The order of construction and installation work formation is determined. The practical application peculiarities of cost calculations traditional and alternative methods in construction industry are considered. Based on the principles, methods and tools created a new conceptual model construction work cost determination.

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