scholarly journals The Impact of Nondiagnostic Information on Selection Decision Making: A Cautionary Note and Mitigation Strategies

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dev Dalal ◽  
Levi Sassaman ◽  
Xiaoyuan Zhu
Keyword(s):  
Decision Making ◽  
Mitigation Strategies ◽  
Cautionary Note ◽  
Selection Decision ◽  
The Impact ◽  
Nondiagnostic Information

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

2021 ◽  
Vol 12 (04) ◽  
pp. 808-815
Author(s):  
Lin Lawrence Guo ◽  
Stephen R. Pfohl ◽  
Jason Fries ◽  
Jose Posada ◽  
Scott Lanyon Fleming ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Mitigation Strategies ◽  
Learning Performance ◽  
Learning Models ◽  
Dataset Shift ◽  
The Impact ◽  
Machine Learning Models

Abstract Objective The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was measured, the procedures used to preserve model performance, and their effects. Results Of 4,457 potentially relevant publications identified, 15 were included. The impact of temporal dataset shift was primarily quantified using changes, usually deterioration, in calibration or discrimination. Calibration deterioration was more common (n = 11) than discrimination deterioration (n = 3). Mitigation strategies were categorized as model level or feature level. Model-level approaches (n = 15) were more common than feature-level approaches (n = 2), with the most common approaches being model refitting (n = 12), probability calibration (n = 7), model updating (n = 6), and model selection (n = 6). In general, all mitigation strategies were successful at preserving calibration but not uniformly successful in preserving discrimination. Conclusion There was limited research in preserving the performance of machine learning models in the presence of temporal dataset shift in clinical medicine. Future research could focus on the impact of dataset shift on clinical decision making, benchmark the mitigation strategies on a wider range of datasets and tasks, and identify optimal strategies for specific settings.

scholarly journals Leveraging mobile phone surveys during the COVID-19 pandemic in Ecuador and Sri Lanka: Methods, timeline and findings

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250171
Author(s):  
Rachael Phadnis ◽  
Champika Wickramasinghe ◽  
Juan Carlos Zevallos ◽  
Stacy Davlin ◽  
Vindya Kumarapeli ◽  
...  
Keyword(s):  
Decision Making ◽  
Sri Lanka ◽  
Mobile Phone ◽  
Technical Assistance ◽  
Population Based ◽  
Mitigation Strategies ◽  
Time Data ◽  
Attitudes And Practices ◽  
Government Clinic ◽  
The Impact

Effective and rapid decision making during a pandemic requires data not only about infections, but also about human behavior. Mobile phone surveys (MPS) offer the opportunity to collect real-time data on behavior, exposure, knowledge, and perception, as well as care and treatment to inform decision making. The surveys aimed to collect coronavirus disease 2019 (COVID-19) related information in Ecuador and Sri Lanka using mobile phones. In Ecuador, a Knowledge, Attitudes and Practices (KAP) survey was conducted. In Sri Lanka, an evaluation of a novel medicine delivery system was conducted. Using the established mobile network operator channels and technical assistance provided through The Bloomberg Philanthropies Data for Health Initiative (D4H), Ministries of Health fielded a population-based COVID-19-specific MPS using Surveda, the open source data collection tool developed as part of the initiative. A total of 1,185 adults in Ecuador completed the MPS in 14 days. A total of 5,001 adults over the age of 35 in Sri Lanka completed the MPS in 44 days. Both samples were adjusted to the 2019 United Nations Population Estimates to produce population-based estimates by age and sex. The Ecuador COVID-19 MPS found that there was compliance with the mitigation strategies implemented in that country. Overall, 96.5% of Ecuadorians reported wearing a face mask or face covering when leaving home. Overall, 3.8% of Sri Lankans used the service to receive medicines from a government clinic. Among those who used the medicine delivery service in Sri Lanka, 95.8% of those who used a private pharmacy received their medications within one week, and 69.9% of those using a government clinic reported the same. These studies demonstrate that MPS can be conducted quickly and gather essential data. MPS can help monitor the impact of interventions and programs, and rapidly identify what works in mitigating the impact of COVID-19.

Application of DEMATEL and MMDE for Analyzing Key Influencing Factors Relevant to Selection of Supply Chain Coordination Schemes

2018 ◽  
pp. 1688-1710
Author(s):  
Pradeep Kumar Behera ◽  
Kampan Mukherjee
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Influencing Factors ◽  
Structural Model ◽  
Selection Process ◽  
Supply Chain Coordination ◽  
Selection Decision ◽  
The Impact ◽  
Relevant Factors ◽  
Selection Of

Any selection decision of supply chain coordination schemes (SCCS) is essentially affected by the environment where the schemes are to be implemented, the necessary conditions required for their implementation, the risk associated with the implementation, and the impact on the performance of the supply chain. Because of the multi-dimensional characteristics; the selection of appropriate SCCS in a given situation remains a challenging task for supply chain managers. This study explores relevant factors that influence this selection process. A structural model is proposed to capture relationships among these factors for development of Impact Relationship Maps (IRM) by applying Decision Making Trial and Evaluation Laboratory (DEMATEL) and Maximum Mean De-Entropy (MMDE) algorithm. A study has been conducted and the outcome leads to add significant value to the decision making process with knowledge on the roles of the factors and inter-factor relations which helps in taking meaningful decision on SCCS selection and implementation.

Application of DEMATEL and MMDE for Analyzing Key Influencing Factors Relevant to Selection of Supply Chain Coordination Schemes

2015 ◽  
Vol 8 (2) ◽  
pp. 49-69 ◽  
Author(s):  
Pradeep Kumar Behera ◽  
Kampan Mukherjee
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Structural Model ◽  
Necessary Conditions ◽  
Selection Process ◽  
Supply Chain Coordination ◽  
Selection Decision ◽  
The Impact ◽  
Relevant Factors ◽  
Selection Of

Any selection decision of supply chain coordination schemes (SCCS) is essentially affected by the environment where the schemes are to be implemented, the necessary conditions required for their implementation, the risk associated with the implementation, and the impact on the performance of the supply chain. Because of the multi-dimensional characteristics; the selection of appropriate SCCS in a given situation remains a challenging task for supply chain managers. This study explores relevant factors that influence this selection process. A structural model is proposed to capture relationships among these factors for development of Impact Relationship Maps (IRM) by applying Decision Making Trial and Evaluation Laboratory (DEMATEL) and Maximum Mean De-Entropy (MMDE) algorithm. A study has been conducted and the outcome leads to add significant value to the decision making process with knowledge on the roles of the factors and inter-factor relations which helps in taking meaningful decision on SCCS selection and implementation.

Quota Women Are Threatening to Men

2017 ◽  
Vol 76 (3) ◽  
pp. 107-116 ◽  
Author(s):  
Klea Faniko ◽  
Till Burckhardt ◽  
Oriane Sarrasin ◽  
Fabio Lorenzi-Cioldi ◽  
Siri Øyslebø Sørensen ◽  
...  
Keyword(s):  
Decision Making ◽  
Organizational Performance ◽  
Gender Stereotypes ◽  
Perceived Threat ◽  
Preferential Treatment ◽  
Public Sector Employees ◽  
Different Types ◽  
Low Levels ◽  
Women In Decision Making ◽  
The Impact

Abstract. Two studies carried out among Albanian public-sector employees examined the impact of different types of affirmative action policies (AAPs) on (counter)stereotypical perceptions of women in decision-making positions. Study 1 (N = 178) revealed that participants – especially women – perceived women in decision-making positions as more masculine (i.e., agentic) than feminine (i.e., communal). Study 2 (N = 239) showed that different types of AA had different effects on the attribution of gender stereotypes to AAP beneficiaries: Women benefiting from a quota policy were perceived as being more communal than agentic, while those benefiting from weak preferential treatment were perceived as being more agentic than communal. Furthermore, we examined how the belief that AAPs threaten men’s access to decision-making positions influenced the attribution of these traits to AAP beneficiaries. The results showed that men who reported high levels of perceived threat, as compared to men who reported low levels of perceived threat, attributed more communal than agentic traits to the beneficiaries of quotas. These findings suggest that AAPs may have created a backlash against its beneficiaries by emphasizing gender-stereotypical or counterstereotypical traits. Thus, the framing of AAPs, for instance, as a matter of enhancing organizational performance, in the process of policy making and implementation, may be a crucial tool to countering potential backlash.

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