ArticleInternational journal of environmental research and public health2017
Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation.
Article in International journal of environmental research and public health, 2017. 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, 25 citations in OpenAlex.
- Predicting the number of care beds for older people by a novel grey Verhulst cosine self-memory model: two case studies of Jiangsu and Shanghai, China.BMC geriatrics · 2026Article
- Predicting the demand of elderly care beds by a novel recursive grey Gompertz model: case studies of Jiangsu and Shanghai, China.BMC geriatrics · 2025Article
- Analysing the global and local spatial associations of medical resources across Wuhan city using POI data.BMC health services research · 2023Article
- Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification.BMC medical informatics and decision making · 2021Article
- Working Hours, Job Burnout, and Subjective Well-Being of Hospital Administrators: An Empirical Study Based on China's Tertiary Public Hospitals.International journal of environmental research and public health · 2021Article
- Investigation of the differences between the medical personnel's and general population's view on the doctor-patient relationship in China by a cross-sectional survey.Globalization and health · 2020Article
- Assessing Productivity Development of Public Hospitals: A Case Study of Shanghai, China.International journal of environmental research and public health · 2020Article
- Observational
- A Comparative Study on the Prediction of Occupational Diseases in China with Hybrid Algorithm Combing Models.Computational and mathematical methods in medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.
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.