Forecasting The Consumptions of Coagulation Tests Using A Deep Learning Model
Abstract
Background: Laboratory professionals aim to provide a reliable laboratory service using public resources efficiently while planning a test’s procurement. This intuitive approach is ineffective, as seen in the COVID-19 pandemic, where the dramatic changes in admissions (e.g. decreased patient admissions) and the purpose of testing (e.g. D-dimer) were experienced. A model based on objective data was developed that predicts the future test consumption of coagulation tests whose consumptions were highly variable during the pandemic.
Methods: Between December 2018 and July 2021, monthly consumptions of coagulation tests (PTT, aPTT, D-dimer, fibrinogen), total-, inpatient-, outpatient-, emergency-, non-emergency -admission numbers were collected. The relationship between inputs and outputs was modeled with the external input nonlinear autoregressive artificial neural network (ANN) (NARX) using MATLAB. Monthly test consumptions between January-July 2021 were used to test the models’ prediction power.
Results: According to the cointegration analysis, total-, emergency-, and non-emergency admission numbers plus the number of working days per month were included in the model. When aPTT and fibrinogen consumptions were estimated, it was possible to predict the other tests. Fifty months of data were used to predict the next six months, and the NARX prediction was the more robust approach for both tests.
Conclusions: The deep learning model gives better results than the intuitive approach in forecasting, even in the pandemic era, and it shows that more effective and efficient planning will be possible if ANN-supported decision mechanisms are used in forecasting tests’ consumptions in the procurement process.
Copyright (c) 2023 Banu Isbilen Basok, Ipek Deveci Kocakoc, Veli Iyilikci, Selena Kantarmaci, Mesut Fidan
This work is licensed under a Creative Commons Attribution 4.0 International License.
The published articles will be distributed under the Creative Commons Attribution 4.0 International License (CC BY). It is allowed to copy and redistribute the material in any medium or format, and remix, transform, and build upon it for any purpose, even commercially, as long as appropriate credit is given to the original author(s), a link to the license is provided and it is indicated if changes were made. Users are required to provide full bibliographic description of the original publication (authors, article title, journal title, volume, issue, pages), as well as its DOI code. In electronic publishing, users are also required to link the content with both the original article published in Journal of Medical Biochemistry and the licence used.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.