Methamphetamine Abuse Trends in Psychiatric Emergency Services: A Retrospective Analysis Using Big Data

Miller, Jeremy; Atala, Robby; Sarangarm, Dusadee; Tohen, Mauricio; Sharma, Shiva; Bhatt, Snehal; Khafaja, Mohamad; Kroth, Phillip J.; Cruz, Mario

doi:10.1007/s10597-020-00563-1

Cited by 27 publications

(14 citation statements)

References 16 publications

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“…However, in the process of learning activities, most of them have negative emotions such as anxiety, irritability, anxiety, and fear. They have not really experienced the sense of achievement and happiness brought by active learning, so they hate going to school, are afraid of teachers, and resist completing homework [14]. (3) In a state of passive learning for a long time, there is a cognitive bias in learning activities, and learning is a burden.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Student Weariness Emotion in English Classroom Based on Intelligent Internet of Things and Big Data Technology

Pang

2022

Occupational Therapy International

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In order to improve the recognition effect of student weariness emotion in English classroom, this paper combines intelligent Internet of Things technology and big data technology to construct an improvement model of student weariness emotion in English classroom. In the process of student facial expression recognition, according to the given grayscale threshold, this paper extracts the surface contour information from the three-dimensional volume data, extracts the student’s surface contour information, and uses triangular facets to fit to form a triangular mesh. Moreover, this paper renders a triangular mesh model and shows how to speed up the calculation of PFH. In addition, this paper proposes a Fast Point Feature Histogram, which uses an iterative closest point fine registration algorithm for image registration. Finally, this paper constructs an emotion recognition model of students’ weariness in English classroom. From the test results, it can be seen that the student weariness emotion recognition system in English classroom proposed in this paper can effectively identify students’ weariness emotion.

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Section: Introductionmentioning

confidence: 99%

Improvement of Student Weariness Emotion in English Classroom Based on Intelligent Internet of Things and Big Data Technology

Pang

2022

Occupational Therapy International

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“…is the matrix norm operation; m, n reflect the size of the matrix. Formulas ( 8) and ( 9) are combined; we can get [17]…”

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confidence: 99%

Application of Image Denoising Algorithm and Data Mining in Psychological Teaching Quality Evaluation

2022

Security and Communication Networks

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In order to improve the psychological teaching quality of college students, this paper combines image denoising algorithm and data mining algorithm to construct a psychological teaching quality evaluation system. The denoising algorithm is mainly used to identify the behavior and expressions of the students so as to explore the psychological state of the students. Moreover, this paper combines the data mining algorithm to carry out the quantitative analysis of students’ psychological state and analyzes and introduces the calibration principle of Dirckx method in detail to improve it and then forms a new calibration method. In addition, based on the arctangent function, this paper proposes a new two-frame random phase-shift fringe image phase shift extraction algorithm. The research results show that the image denoising algorithm and data mining proposed in this paper have a good effect in the evaluation method of psychological teaching quality and can play a good role in the improvement of students’ mental health.

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“…We observed that the amphetamine-related hospitalisation rate more than doubled over our study period, which coincides with reports of large population-based studies. 2 16 Winkelman et al 2 found that the number of hospitalisations increased by 245% between 2008 and 2015, and Miller et al 16 found a similar increase in the use of psychiatric emergency services between 2011 and 2015. Our study builds on prior findings by being the first to offer insight into national hospitalisation rates in the USA by age and sex.…”

Section: Discussionmentioning

confidence: 98%

Amphetamine-related care in the USA, 2003–2014: cross-sectional analyses examining inpatient trends and factors associated with hospitalisation outcomes

et al. 2022

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ObjectivesAlthough amphetamine use is a growing health problem in the USA, there are limited data on amphetamine-related hospitalisations. The primary objective of our study was to examine trends in amphetamine-related hospitalisations in the USA between 2003 and 2014, including by age and sex. Our secondary objectives were to examine whether demographic, clinical and care setting characteristics were associated with select outcomes of amphetamine-related hospitalisations, including in-hospital mortality, prolonged length of stay and leaving against medical advice.Design, setting and participantsUsing the 2003–2014 National Inpatient Sample, we estimated the rate of amphetamine-related hospitalisations for each year in the USA among individuals 18+ years of age, stratified by age and sex. Subgroup analyses examined hospitalisations due to amphetamine causes. Unconditional logistic regression modelling was used to estimate the adjusted odds of admission outcomes for sociodemographic, clinical and hospital indicators.Primary and secondary outcomesOur primary outcome was amphetamine-related hospitalisations between 2003 and 2014; secondary outcomes included in-hospital mortality, prolonged length of stay and leaving against medical advice.ResultsAmphetamine-related hospitalisation rates increased from 27 to 69 per 100 000 population between 2003 and 2014. Annual rates were consistently greater among younger (18–44 years) individuals and men. Regional differences were observed, with admission to Western hospitals being associated with increased mortality (adjusted OR, AOR 5.07, 95% CI 1.22 to 21.04) and shorter (0–2 days) lengths of stay (AOR 0.70, 95% CI 0.58 to 0.83) compared with Northeast admissions. Males (AOR 1.26, 95% CI 1.15 to 1.38; compared with females) and self-pay (AOR 2.30, 95% CI 1.90 to 2.79; compared with private insurance) were associated with leaving against medical advice.ConclusionsIncreasing rates of amphetamine-related hospitalisation risk being overshadowed by other public health crises. Regional amphetamine interventions may offer the greatest population health benefits. Future studies should examine long-term outcomes among patients hospitalised for amphetamine-related causes.

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Methamphetamine Abuse Trends in Psychiatric Emergency Services: A Retrospective Analysis Using Big Data

Cited by 27 publications

References 16 publications

Improvement of Student Weariness Emotion in English Classroom Based on Intelligent Internet of Things and Big Data Technology

Improvement of Student Weariness Emotion in English Classroom Based on Intelligent Internet of Things and Big Data Technology

Application of Image Denoising Algorithm and Data Mining in Psychological Teaching Quality Evaluation

Amphetamine-related care in the USA, 2003–2014: cross-sectional analyses examining inpatient trends and factors associated with hospitalisation outcomes

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