ArticleJournal of biomedical informatics2021
Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan.
Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- Exploring SARS-CoV-2 spike protein mutations through genetic algorithm-driven structural modeling.Bioinformatics advances · 2025Article
- Observational
- The Effects of Time Window-Averaged Mobility on Effective Reproduction Number of COVID-19 Viral Variants in Urban Cities.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- COVID-19 forecasting using new viral variants and vaccination effectiveness models.Computers in biology and medicine · 2022Article
- Data analytics during pandemics: a transportation and location planning perspective.Annals of operations research · 2022Article
- Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms.Applied soft computing · 2022Article
- One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan.International journal of environmental research and public health · 2021Article
- Polish Emergency Dispatchers During a COVID-19 Pandemic - Burnout Syndrome, Perceived Stress, and Self-Efficacy. Effects of Multidimensional Path Analysis.Frontiers in psychology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.