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14. Author Correction: Improved device efficiency and lifetime of perovskite light-emitting diodes by size-controlled polyvinylpyrrolidone-capped gold nanoparticles with dipole formation

Author

Lee, Chang Min, Choi, Dong Hyun, Islam, Amjad, Kim, Dong Hyun, Kim, Tae Wook, Jeong, Geon‑Woo, Cho, Hyun Woo, Park, Min Jae, Shah, Syed Hamad Ullah, Chae, Hyung Ju, Kim, Kyoung-Ho, Sujak, Muhammad, Lee, Jae Woo, Kim, Donghyun, Kim, Chul Hoon, Lee, Hyun Jae, Bae, Tae‑Sung, Yu, Seung Min, Jin, Jong Sung, Kang, Yong‑Cheol, Park, Juyun, Song, Myungkwan, Kim, Chang‑Su, Shin, Sung Tae, and Ryu, Seung Yoon

Published
2022
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15. Improved device efficiency and lifetime of perovskite light-emitting diodes by size-controlled polyvinylpyrrolidone-capped gold nanoparticles with dipole formation

Author

Lee, Chang Min, Choi, Dong Hyun, Islam, Amjad, Kim, Dong Hyun, Kim, Tae Wook, Jeong, Geon-Woo, Cho, Hyun Woo, Park, Min Jae, Shah, Syed Hamad Ullah, Chae, Hyung Ju, Kim, Kyoung-Ho, Sujak, Muhammad, Lee, Jae Woo, Kim, Donghyun, Kim, Chul Hoon, Lee, Hyun Jae, Bae, Tae-Sung, Yu, Seung Min, Jin, Jong Sung, Kang, Yong-Cheol, Park, Juyun, Song, Myungkwan, Kim, Chang-Su, Shin, Sung Tae, and Ryu, Seung Yoon

Published
2022
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16. Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning.

Author

Choi, Yeongho, Kim, Ki Hong, Kim, Yoonjic, Choi, Dong Hyun, Joo, Yoon Ha, Choi, Sae Won, Song, Kyoung Jun, and Shin, Sang Do

Subjects
CLINICAL decision support systems, SYSTOLIC blood pressure, RECEIVER operating characteristic curves, HYPOTENSION, CONVOLUTIONAL neural networks
Abstract

Background: Shock is a life‐threatening condition that is associated with high mortality and morbidity. Therefore, the timely identification and management of this condition are important. We aimed to develop a prediction model for vasopressor use based on concise serial arterial blood pressure data. Methods: We collected continuous arterial blood pressure from patients admitted to the emergency department (ED) resuscitation room. Patients with an initial systolic blood pressure lower than 90 mmHg were included in the study. We developed prediction models using convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks. Discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC). Results: A total of 120 patients were enrolled in the study. The CNN and LSTM models yielded AUROCs ranging from 0.731 to 0.834 for predicting the need for vasopressor infusion within different time frames (30 min, 1 h, and 6 h). LSTM outperformed the CNN in terms of predicting vasopressor infusion within 30 min and 1 h (p value < 0.05). The AUPRC values ranged from 0.200 to 0.252, and sensitivity–specificity analyses indicated that the models have potential for clinical applications. Conclusion: This study used serial arterial blood pressure data to construct a promising prediction model for the need for vasopressors in hypotensive ED patients. The simplicity and accuracy of the model provide valuable insights for developing a clinical decision support tool for resuscitation in emergency settings. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Highly efficient, heat dissipating, stretchable organic light-emitting diodes based on a MoO3/Au/MoO3 electrode with encapsulation

Author

Choi, Dae Keun, Kim, Dong Hyun, Lee, Chang Min, Hafeez, Hassan, Sarker, Subrata, Yang, Jun Su, Chae, Hyung Ju, Jeong, Geon-Woo, Choi, Dong Hyun, Kim, Tae Wook, Yoo, Seunghyup, Song, Jinouk, Ma, Boo Soo, Kim, Taek-Soo, Kim, Chul Hoon, Lee, Hyun Jae, Lee, Jae Woo, Kim, Donghyun, Bae, Tae-Sung, Yu, Seung Min, Kang, Yong-Cheol, Park, Juyun, Kim, Kyoung-Ho, Sujak, Muhammad, Song, Myungkwan, Kim, Chang-Su, and Ryu, Seung Yoon

Published
2021
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25. Author Correction: Highly efficient, heat dissipating, stretchable organic light-emitting diodes based on a MoO3/Au/MoO3 electrode with encapsulation

Author

Choi, Dae Keun, Kim, Dong Hyun, Lee, Chang Min, Hafeez, Hassan, Sarker, Subrata, Yang, Jun Su, Chae, Hyung Ju, Jeong, Geon-Woo, Choi, Dong Hyun, Kim, Tae Wook, Yoo, Seunghyup, Song, Jinouk, Ma, Boo Soo, Kim, Taek-Soo, Kim, Chul Hoon, Lee, Hyun Jae, Lee, Jae Woo, Kim, Donghyun, Bae, Tae-Sung, Yu, Seung Min, Kang, Yong-Cheol, Park, Juyun, Kim, Kyoung-Ho, Sujak, Muhammad, Song, Myungkwan, Kim, Chang-Su, and Ryu, Seung Yoon

Published
2021
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37. Machine Learning Analysis to Identify Data Entry Errors in Prehospital Patient Care Reports: A Case Study of a National Out-of-Hospital Cardiac Arrest Registry.

Author

Choi, Dong Hyun, Park, Jeong Ho, Choi, Young Ho, Song, Kyoung Jun, Kim, Sungwan, and Shin, Sang Do

Subjects
PREVENTION of medical errors, DATA quality, STATISTICS, CONFIDENCE intervals, CROSS-sectional method, MACHINE learning, MANN Whitney U Test, DOCUMENTATION, HUMAN services programs, INTER-observer reliability, CARDIAC arrest, HUMAN error, QUALITY assurance, EMERGENCY medical services, CHI-squared test, DESCRIPTIVE statistics, RESEARCH funding, PREDICTION models, LOGISTIC regression analysis, RECEIVER operating characteristic curves, STATISTICAL sampling, EMERGENCY medicine, ALGORITHMS
Abstract

Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable. Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71–0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Severed Connections: Political Parties and Democratic Responsiveness in Sub-Saharan Africa

Author

Choi, Dong-Hyun

Subjects
Political science, African studies, Public policy, Candidate Selection, Democratic Representation, Political Parties, Responsiveness
Abstract

This dissertation investigates how political parties can undermine the representation of citizen interests in new democracies. Conventional wisdom has emphasized the centrality of parties in mediating the relationship between voters and politicians, and has often attributed the representational deficit observed across the developing world to the lack of stable partisan attachments or the ephemeral nature of political parties. I show that this may not be the case. To the contrary, under a political geography that enables political parties to repeatedly monopolize electoral support from voters in subnational elections, parties and the internal processes that govern their selection of candidates can function sever, rather than strengthen, the connection between voters and their representatives. My theory focuses on how conditions typical of many new democracies in Sub-Saharan Africa---local one party dominance and centralized control over candidate selection---shape the incentives of politicians to serve the interests of their constituents. Despite the institutionalization of competitive multiparty elections at the national level in new democracies, political parties are often able to consistently dominate their competitors in subnational elections. Under conditions of local one party dominance, politicians who contest local office become beholden to the selectorate which decides who the party's nominee will be, without much regard to the electorate. Yet for the party leader, who controls the selection of candidates within their parties, local politicians who amass an independent support base by serving the interests of their constituents pose a significant threat towards maintaining her position in the party hierarchy. I argue that the party leader selects candidates in a way that minimizes the risk of politicians building such an independent support base, encouraging responsiveness to constituents only in select locales where they are electorally vulnerable. As a result, politicians are incentivized to divert their effort and resources away from serving their constituents towards other activities that benefit the party leader. I support these claims using a multi-method research strategy that pieces together qualitative, quantitative, and experimental evidence based on 18 months of fieldwork in Kenya. I first combine insights from more than 70 politician interviews and analyses of nationally representative surveys and constituency-level electoral returns across six African democracies to establish that African parties often hold a monopoly on local power. Moreover, using detailed inquiry into the organization of political parties in Kenya and a series of experiments conducted among Kenyan primary voters, I also show that party leaders possess both the institutional tools and the persuasive influence over partisans that enable them to command control over the candidate selection process. Finally, I use supervised machine-learning methods on a large data set collected through web-crawling to document the existence of a nomination tournament, in which party leaders select candidates that invest significantly in "party-oriented" rather than "constituency-oriented" behavior over their terms in office. Substantively, the findings contribute to the emerging consensus that democratic elections are necessary but insufficient to foster better representation and responsiveness for the people. However, while the dominant narrative in the comparative politics has focused on structural-institutional factors such as ethnicity, clientelism, or electoral systems to understand this deficit, I shift the attention rightfully back to political parties. In fact, the conclusions of the dissertation suggest that ideal of "representative democracy" is likely to remain elusive unless democracy within political parties is realized. When power and authority over party institutions and decision-making processes accumulate to a single individual or a small group of elites, and without systematic checks to constrain their power, party leaders have the potential to effectively become autocrats within their domain; manipulating elected representatives who should primarily be interested in tending to their constituents to serve their political ambitions, thereby derailing democratic process that they should protect.

Published
2018

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