Evidence map›Paper›PMID 38561694›Full record

ArticleInternational journal of emergency medicine2024

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study.

Miguel Ortiz-Barrios, Antonella Petrillo, Sebastián Arias-Fonseca, Sally McClean, Fabio de Felice, Chris Nugent, Sheyla-Ariany Uribe-López

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
6.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 5 countries.

Miguel Ortiz-BarriosCentro de Investigación en Gestión e Ingeniería de Producción (CIGIP), Universitat Politecnica de Valencia, Camino de Vera, s/n, Valencia, 46022, Spain. mortiz1@cuc.edu.co.
Antonella PetrilloDepartment of Engineering, University of Naples "Parthenope", Naples, Italy.
Sebastián Arias-FonsecaDepartment of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla, 080002, Colombia.
Sally McCleanSchool of Computing, Ulster University, Belfast, BT15 1ED, UK.
Fabio de FeliceDepartment of Engineering, University of Naples "Parthenope", Naples, Italy.
Chris NugentSchool of Computing, Ulster University, Belfast, BT15 1ED, UK.
Sheyla-Ariany Uribe-LópezAcademic Multidisciplinary Division of Jalpa de Mendez, Juarez Autonomous University of Tabasco, Jalpa de Mendez, Tabasco, Mexico.
Parthenope University of Naples · ITUniversity of Ulster · GBUniversidad Juárez Autónoma de Tabasco · MXUniversitat Politècnica de València · ESUniversity of the Coast · CO

Funding

European Union Next Generation EU Margarita SalasMinisterio de Ciencia, Innovación y Universidades (Program for Requalification of the Spanish University System) Margarita SalasUniversitat Politecnica de Valencia (Recovery, Transformation, and Resilience Plan) Margarita Salas
6 · The paper itself

Abstract

backgroundShortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices.

methodsFirst, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology.

resultsThe number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 - 96.26%]), whilst the specificity was 85.45% [77.45 - 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 - 95.13%) and 87.85% (80.12 - 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 - 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy.

conclusionsCombining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.

Indexed as

Artificial Intelligence (AI)Discrete-Event-Simulation (DES)Emergency Department (ED)HealthcareMechanical ventilationRandom Forest (RF)

Identifiers

PMID38561694
PMCPMC10986051
OpenAlexW4393393144

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.