Historical and future trends in emergency pituitary referrals: a machine learning analysis.

ABSTRACT:

Purpose

Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use.

Methods

A data-driven analysis was performed using all available electronic neurosurgical referrals (2014-2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Lon

SUBMITTER: Pandit AS 

PROVIDER: S-EPMC9462621 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature