Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation.
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...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 1; pp. 1 - 9 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155312940&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155312940 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2022 vid: 35 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155312940 153481187 155312940 155312940 10.1007/s10278-021-00532-4 155312940 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation. aug: au: Becker, Anton S. Erinjeri, Joseph P. Chaim, Joshua Kastango, Nicholas Elnajjar, Pierre Hricak, Hedvig Vargas, H. Alberto affil: Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA sug: subj: Forecasting Radiology Service Radiology Information Systems Web Search Engines Trends Health Resource Allocation Health and Welfare Planning Human Artificial Intelligence Magnetic Resonance Imaging Algorithms Bioinformatics Tomography, X-Ray Resource Allocation ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|