IU Indianapolis ScholarWorks :: Browsing by Subject "Uncertainty"

Browsing by Subject "Uncertainty"

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Challenges and Opportunities: The 21st Century Public Manager in a VUCA World
(Oxford University Press, 2018) Park, Sanghee
Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation
(IEEE, 2021) Yalcinkaya, Dilek Mirgun; Youssef, Khalid; Heydari, Bobby; Zamudio, Luis; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of Medicine
In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.
Distributed Nonlinear Model Predictive Control for Heterogeneous Vehicle Platoons Under Uncertainty
(IEEE Xplore, 2021-09) Shen, Dan; Yin, Jianhua; Du, Xiaoping; Li, Lingxi; Electrical and Computer Engineering, School of Engineering and Technology
This paper presents a novel distributed nonlinear model predictive control (DNMPC) for minimizing velocity tracking and spacing errors in heterogeneous vehicle platoon under uncertainty. The vehicle longitudinal dynamics and information flow in the platoon are established and analyzed. The algorithm of DNMPC with robustness and reliability considerations at each vehicle (or node) is developed based on the leading vehicle and reference information from nodes in its neighboring set. Together with the physical constraints on the control input, the nonlinear constraints on vehicle longitudinal dynamics, the terminal constraints on states, and the reliability constraints on both input and output, the objective function is defined to optimize the control accuracy and efficiency by penalizing the tracking errors between the predicted outputs and desirable outputs of the same node and neighboring nodes, respectively. Meanwhile, the robust design optimization model also minimizes the expected quality loss which consists of the mean and standard deviation of node inputs and outputs. The simulation results also demonstrate the accuracy and effectiveness of the proposed approach under two different traffic scenarios.
Economic Uncertainty and Fertility
(University of Chicago Press, 2021) Gozgor, Giray; Bilgin, Mehmet Huseyin; Rangazas, Peter; Economics, School of Liberal Arts
The precautionary motive for saving predicts that an increase in income uncertainty increases saving by reducing both consumption and fertility. We examine this prediction using a new measure of economic uncertainty—the World Uncertainty Index—and focus on data from 126 countries for the period 1996–2017. The empirical findings indicate that uncertainty decreases the fertility rate, as suggested by theory. This evidence is robust to different model specifications and econometric techniques as well as to the inclusion of various controls. The evidence also indicates that changes in uncertainty may be a factor explaining why fertility is procyclical.
Enduring Uncertainty Through the Lens of Osteoporosis: A Mixed Methods Study
(2024-06) Vlaeminck, Caitlin Mae; Miller, Wendy Trueblood; Carter, Gregory; Crowder, Sharron J.; Otte, Julie; Longtin, Krista
Osteoporosis is a chronic illness that is underdiagnosed and often poorly managed. Uncertainty is a phenomenon experienced by individuals diagnosed with chronic illness. There are no published American studies describing whether women diagnosed with osteoporosis experience uncertainty. Experiencing uncertainty can lead to decreased quality of life (QOL), delays in decision-making, and negative impacts on relationships. A mixed methods approach was used using descriptive statistics and two scales, The Mishel Uncertainty in Illness Scale-Community Form (MUIS-C) and the Patient Reported Outcomes Measurement Information System (PROMIS) Global Health scale, and Interpretative Phenomenological Analysis (IPA). Significant negative correlations were found between levels of uncertainty and physical health status and between physical and mental health. The qualitative study focused on describing the experience individuals had with the diagnosis of osteoporosis. Data were collected through semi-structured interviews with fourteen Caucasian women who all had a diagnosis of osteopenia or osteoporosis. Thematic analysis revealed five themes that could be chronologically processed: The Sentinel Event, Adapting to Chronic Disease, Navigating Uncertainty, Being Less Than, and What the Future Holds. The findings of this study have implications for how healthcare providers share knowledge and education about the disease of osteoporosis with individuals. Future research should include women and men from diverse racial and ethnic backgrounds. This inclusive approach is crucial for ensuring that preventive measures and management strategies are tailored to the diverse needs of all individuals, fostering equity and efficacy in osteoporosis care.
Giving with a purpose: the cybernetics of philanthropy
(Center for a Voluntary Society, 1974) Von Foerster, Heinz
This paper by Heinz Von Foerster, grounded in the logic of information theory, presents a method for getting at the basic causes of social continuity and discontinuity. It is offered as a contribution to a developing research discipline on the theory and practice of philanthrophy.
Neighborhood Disadvantage Associated With Blunted Amygdala Reactivity to Predictable and Unpredictable Threat in a Community Sample of Youth
(Elsevier, 2022) Huggins, Ashley A.; McTeague, Lisa M.; Davis, Megan M.; Bustos, Nicholas; Crum, Kathleen I.; Polcyn, Rachel; Adams, Zachary W.; Carpenter, Laura A.; Hajcak, Greg; Halliday, Colleen A.; Joseph, Jane E.; Kmett Danielson, Carla; Psychiatry, School of Medicine
Background: Childhood socioeconomic disadvantage is a form of adversity associated with alterations in critical frontolimbic circuits involved in the pathophysiology of psychiatric disorders. Most work has focused on individual-level socioeconomic position, yet individuals living in deprived communities typically encounter additional environmental stressors that have unique effects on the brain and health outcomes. Notably, chronic and unpredictable stressors experienced in the everyday lives of youth living in disadvantaged neighborhoods may impact neural responsivity to uncertain threat. Methods: A community sample of children (N = 254) ages 8 to 15 years (mean = 12.15) completed a picture anticipation task during a functional magnetic resonance imaging scan, during which neutral and negatively valenced photos were presented in a temporally predictable or unpredictable manner. Area Deprivation Index (ADI) scores were derived from participants' home addresses as an index of relative neighborhood disadvantage. Voxelwise analyses examined interactions of ADI, valence, and predictability on neural response to picture presentation. Results: There was a significant ADI × valence interaction in the middle temporal gyrus, anterior cingulate cortex, hippocampus, and amygdala. Higher ADI was associated with less amygdala activation to negatively valenced images. ADI also interacted with predictability. Higher ADI was associated with greater activation of lingual and calcarine gyri for unpredictably presented stimuli. There was no three-way interaction of ADI, valence, and predictability. Conclusions: Neighborhood disadvantage may impact how the brain perceives and responds to potential threats. Future longitudinal work is critical for delineating how such effects may persist across the life span and how health outcomes may be modifiable with community-based interventions and policies.
Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning
(IEEE, 2022) Arjomandi-Nezhad, Ali; Ahmadi, Amirhossein; Taheri, Saman; Fotuhi-Firuzabad, Mahmud; Moeini-Aghtaie, Moein; Lehtonen, Matti; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
Electricity demand forecast is necessary for power systems’ operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people’s lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order to make the model capable of correcting itself in pandemic situations and enhance data quality for the forecasting task. Several ensemble-based machine learning models are examined for the short-term country-level demand prediction model. Besides, the quantile random forest regression is implemented for a probabilistic point of view. For case studies, the models are trained for predicting Germany’s country-level demand. The results indicate that ensemble models, especially boosting and bagging-boosting models, are capable of accurate country-level demand forecast. Besides, the majority of these models are robust against missing the pandemic policy data. However, utilizing the pandemic policy data as features increases the forecasting accuracy during the pandemic situation significantly. Furthermore, the probabilistic quantile regression demonstrated high accuracy for the aforementioned case study.
Patients’ views on variants of uncertain significance across indications
(Springer, 2019-08-20) Clift, Kristin; Macklin, Sarah; Halverson, Colin; McCormick, Jennifer B.; Abu Dabrh, Abd Moain; Hines, Stephanie; Medicine, School of Medicine
As genomic sequencing expands into more areas of patient care, an increasing number of patients learn of the variants of uncertain significance (VUSs) that they carry. Understanding the potential psychosocial consequences of the disclosure of a VUS can help inform pre- and post-test counseling discussions. Medical uncertainty in general elicits a variety of responses from patients, particularly in the growing field of medical genetics and genomics. It is important to consider patients’ responses to the ambiguous nature of VUSs across different indications and situational contexts. Genetic counselors and other providers ordering genetic testing should be prepared for the possibility of their patients’ misinterpretation of such results. Pre-test counseling should include a discussion of the possibility of VUSs and what it would mean for the patient’s care and its potential psychosocial impacts. When a VUS is found, post-test counseling should include additional education and a discussion of the variant’s implications and medical management recommendations based on the results. These discussions may help temper subjective interpretations, unrealistic views, and decisional regret.
A Psychometric Evaluation of Script Concordance Tests for Measuring Clinical Reasoning
(2013-06) Wilson, Adam Benjamin; Pike, Gary R. (Gary Robert), 1952-; Humbert, Aloysius J.; Brokaw, James J.; Seifert, Mark F.
Purpose: Script concordance tests (SCTs) are assessments purported to measure clinical data interpretation. The aims of this research were to (1) test the psychometric properties of SCT items, (2) directly examine the construct validity of SCTs, and (3) explore the concurrent validity of six SCT scoring methods while also considering validity at the item difficulty and item type levels. Methods: SCT scores from a problem solving SCT (SCT-PS; n=522) and emergency medicine SCT (SCT-EM; n=1040) were used to investigate the aims of this research. An item analysis was conducted to optimize the SCT datasets, to categorize items into levels of difficulty and type, and to test for gender biases. A confirmatory factor analysis tested whether SCT scores conformed to a theorized unidimensional factor structure. Exploratory factor analyses examined the effects of six SCT scoring methods on construct validity. The concurrent validity of each scoring method was also tested via a one-way multivariate analysis of variance (MANOVA) and Pearson’s product moment correlations. Repeated measures analysis of variance (ANOVA) and one-way ANOVA tested the discriminatory power of the SCTs according to item difficulty and type. Results: Item analysis identified no gender biases. A combination of moderate model-fit indices and poor factor loadings from the confirmatory factor analysis suggested that the SCTs under investigation did not conform to a unidimensional factor structure. Exploratory factor analyses of six different scoring methods repeatedly revealed weak factor loadings, and extracted factors consistently explained only a small portion of the total variance. Results of the concurrent validity study showed that all six scoring methods discriminated between medical training levels in spite of lower reliability coefficients on 3-point scoring methods. In addition, examinees as MS4s significantly (p<0.001) outperformed their MS2 SCT scores in all difficulty categories. Cross-sectional analysis of SCT-EM data reported significant differences (p<0.001) between experienced EM physicians, EM residents, and MS4s at each level of difficulty. When considering item type, diagnostic and therapeutic items differentiated between all three training levels, while investigational items could not readily distinguish between MS4s and EM residents. Conclusions: The results of this research contest the assertion that SCTs measure a single common construct. These findings raise questions about the latent constructs measured by SCTs and challenge the overall utility of SCT scores. The outcomes of the concurrent validity study provide evidence that multiple scoring methods reasonably differentiate between medical training levels. Concurrent validity was also observed when considering item difficulty and item type.

Browsing by Subject "Uncertainty"

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