Research and prediction of opioid crisis based on BP neural network and Markov chain

Fan, Wenyuan; Jiang, Yan; Huang, Songyang; Liu, Weiguo

doi:10.3934/math.2019.5.1357

Cited by 8 publications

(2 citation statements)

References 1 publication

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“…The performance of the RF method was similar to the traditional linear model, but RF significantly outperformed deep learning. ( Fan et al, 2019 ) 2019 To study the social and economic data and the relationship between opioid drug abuse situations. Prediction Survey United States 8344 Opioid Grey relation analysis The deviation of prediction is reduced from (−10.99%, 22.33%) to (−8.29%, 2.81%), making the modified value closer to the measured value.…”

Section: Methodsmentioning

confidence: 99%

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Kino

Hsu

Shiba

et al. 2021

SSM - Population Health

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Background Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

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Section: Methodsmentioning

confidence: 99%

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Kino

Hsu

Shiba

et al. 2021

SSM - Population Health

View full text Add to dashboard Cite

show abstract

“…The conceptual models commonly used by scholars to study internal evolution laws include: PSR model [11], DPSIR model [12], DPSIRM model [13], TQR model [14], EES model [15], PLE model [16], etc. Secondly, the models commonly used to study the prediction between the two include: BP neural network model [17,18,21], ARIMA model [18,19], grey model [20], Markov chain model [21], etc. Finally, methods for measuring key factors include: system dynamics [22], CRITIC method [23], analytic hierarchy process [24,25], resistance model [26], geographic detector [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Research on the Coupling of Regional Low-Carbon Economy and Energy Structure Based on Multi Algorithm Optimization: A Case Study of Anhui Province

Zhao,

He,

Zheng

et al. 2024

Pol. J. Environ. Stud.

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In order to accelerate the high-quality development of green and low-carbon economy, it is necessary to study the coordination between the regional low-carbon economy and the energy structure. Taking Anhui Province, a resource-based province, as an example, a comprehensive evaluation index system for the coupling of low-carbon economy and energy structure is constructed based on the SDI conceptual framework. Using the entropy weight method for weighting and calculating the development index from 2014 to 2021 to measure the level of regional development, and then using the coupling coordination model to calculate the coupling degree to measure the level of regional coupling. According to the coupling grade, it is divided into different coupling regions (disharmonic, adaptive, and benign). Furthermore, three types of GM (1,1) (traditional, new information, and metabolic) were used to predict the regional coupling level from 2022 to 2026. Finally, a resistance model was used to diagnose resistance in the region. The results show that: (1) The overall development level of energy structure shows a downward trend, while the overall development level of low-carbon economy shows an upward trend. (2) The coupling development of low-carbon economy and energy structure in Anhui Province is on the rise steadily. There is a trend towards primary coordination at the coupling grade, which is developing into a benign coupling region.(3) The overall resistance in the dimension of energy structure shows a steady growth trend, while the overall resistance in the dimension of low-carbon economy shows a steady downward trend.

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Exact prediction intervals for future exponential and Pareto lifetimes based on ordered ranked set sampling of non-random and random size

Barakat

Newer

2022

Stat Papers

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In the present paper, two pivotal statistics are suggested to construct prediction intervals of future observations from the exponential and Pareto distributions in the context of ordered ranked set sample. Our study encompasses two cases. The first case, when the sample size is assumed to be fixed and the second case when the sample size is assumed to be a positive integer-valued random variable. In addition to deriving explicit forms for the distribution functions of the two pivotal statistics, we consider some special cases for the random size of the sample. Moreover, a simulation study is carried out to assess the efficiency of the suggested methods. Finally, an example representing lifetime data is analyzed.

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Research and prediction of opioid crisis based on BP neural network and Markov chain

Cited by 8 publications

References 1 publication

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Research on the Coupling of Regional Low-Carbon Economy and Energy Structure Based on Multi Algorithm Optimization: A Case Study of Anhui Province

Exact prediction intervals for future exponential and Pareto lifetimes based on ordered ranked set sampling of non-random and random size

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