Szczegóły publikacji
Opis bibliograficzny
Machine learning prediction techniques in the optimization of diagnostic laboratories’ network operations / Krzysztof REGULSKI, Andrzej OPALIŃSKI, Jakub Swadźba, Piotr Sitkowski, Paweł Wąsowicz, Agnieszka Kwietniewska-Śmietana // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2024 — vol. 14 iss. 6, s. 1–23. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 22–23, Abstr. — Publikacja dostępna online od: 2024-03-13
Autorzy (6)
- AGHRegulski Krzysztof
- AGHOpaliński Andrzej
- Swadźba Jakub
- Sitkowski Piotr
- Wąsowicz Paweł
- Kwietniewska-Śmietana Agnieszka
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152399 |
|---|---|
| Data dodania do BaDAP | 2024-04-17 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app14062429 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.
