التفاصيل البيبلوغرافية
| العنوان: |
Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation. |
| المؤلفون: |
Becker, Anton S., Erinjeri, Joseph P., Chaim, Joshua, Kastango, Nicholas, Elnajjar, Pierre, Hricak, Hedvig, Vargas, H. Alberto |
| المصدر: |
Journal of Digital Imaging; Feb2022, Vol. 35 Issue 1, p1-8, 8p, 3 Charts, 1 Graph |
| مصطلحات موضوعية: |
MEDICAL radiology, INFORMATION storage & retrieval systems, MEDICAL databases, ARTIFICIAL intelligence, MAGNETIC resonance imaging, BIOINFORMATICS, TOMOGRAPHY, FORECASTING, SEARCH engines, RESOURCE allocation, HOSPITAL radiological services, HEALTH care rationing, HEALTH planning, ALGORITHMS |
| مستخلص: |
The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (p = 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation. [ABSTRACT FROM AUTHOR] |
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