Prediction of emergency department hospital admission based on natural language processing and neural networks

X Zhang, J Kim, RE Patzer, SR Pitts… - … of information in …, 2017 - thieme-connect.com
Objective: To describe and compare logistic regression and neural network modeling
strategies to predict hospital admission or transfer following initial presentation to …

Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department

D Su, Q Li, T Zhang, P Veliz, Y Chen, K He… - BMC medical research …, 2022 - Springer
Background Early screening and accurately identifying Acute Appendicitis (AA) among
patients with undifferentiated symptoms associated with appendicitis during their emergency …

ML models for severity classification and length-of-stay forecasting in emergency units

J Moya-Carvajal, F Pérez-Galarce, C Taramasco… - Expert Systems with …, 2023 - Elsevier
Abstract Length-of-stay (LoS) prediction and severity classification for patients in emergency
units in a clinic or hospital are crucial problems for public and private health networks. An …

Applied bibliometrics and information visualization for decision-making processes in higher education institutions

C Vílchez-Román, S Sanguinetti, M Mauricio-Salas - Library Hi Tech, 2021 - emerald.com
Purpose The purpose of this paper is to analyse how using bibliometrics and information
visualization can provide a “picture at glance” from which decision-makers can structure …

Advanced diagnostic imaging utilization during emergency department visits in the United States: A predictive modeling study for emergency department triage

X Zhang, J Kim, RE Patzer, SR Pitts, FH Chokshi… - PloS one, 2019 - journals.plos.org
Background Emergency department (ED) crowding is associated with negative health
outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced …

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department

X Zhang, MF Bellolio, P Medrano-Gracia… - BMC medical informatics …, 2019 - Springer
Objective To examine the association between the medical imaging utilization and
information related to patients' socioeconomic, demographic and clinical factors during the …

Automatic text categorization and summarization using rule reduction

CL Devasena, M Hemalatha - IEEE-International Conference …, 2012 - ieeexplore.ieee.org
Text mining is a new field that attempts to bring together meaningful information from natural
language text. Automatic Text categorization and summarization is the process of assigning …

Text mining in radiology reports (Methodologies and algorithms), and how it affects on workflow and supports decision making in clinical practice (Systematic review)

A Al-Aiad, T El-shqeirat - 2020 11th International Conference on …, 2020 - ieeexplore.ieee.org
The purpose of this review was to summarize the algorithms and methodologies of text-
mining and demonstrate the main objective of text-mining on radiology reports in health care …

Clustering of 27,525,663 death records from the United States based on health conditions associated with death: An example of big health data exploration

DJA Janssen, S Rechberger, EFM Wouters… - Journal of Clinical …, 2019 - mdpi.com
Background: Insight into health conditions associated with death can inform healthcare
policy. We aimed to cluster 27,525,663 deceased people based on the health conditions …

[PDF][PDF] A parallel implementation of the self organising map using OpenCL

G Davidson - University of Glasgow, 2015 - dcs.gla.ac.uk
The self organising map is a machine learning algorithm used to produce low dimensional
representations of high dimensional data. While the process is becoming more and more …