Eeshan Hasan
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View article: One-shot intervention reduces online engagement with distorted content
One-shot intervention reduces online engagement with distorted content Open
Depression is one of the leading causes of disability worldwide. Individuals with depression often experience unrealistic and overly negative thoughts, i.e. cognitive distortions, that cause maladaptive behaviors and feelings. Now that a m…
A registered report on presentation factors that influence the attraction effect Open
Context effects occur when the preference between two alternatives is affected by the presence of an extra alternative. These effects are some of the most well studied phenomena in multi-alternative, multi-attribute decision making. Recent…
Boosting wisdom of the crowd for medical image annotation using training performance and task features Open
A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by indiv…
The Role of Salience in Multialternative Multiattribute Choice Open
Attention plays a central role in multi-alternative multiat- tribute decision-making but the cognitive mechanisms for it are elusive (Yang & Krajbich, 2023; Molter, Thomas, Huet- tel, Heekeren, & Mohr, 2022; Trueblood, 2022). In this proje…
View article: One shot intervention reduces online engagement with distorted content.
One shot intervention reduces online engagement with distorted content. Open
Depression is one of the leading causes of disability worldwide. Individuals with depression often experience unrealistic and overly negative thoughts, i.e.~cognitive distortions, that cause maladaptive behaviors and feelings. Now that a m…
A registered report on presentation factors that influence the attraction effect Open
Context effects occur when the preference between two alternatives is affected by the presence of an extra alternative. These effects are some of the most well studied phenomena in multi-alternative, multi-attribute decision making. Recent…
Boosting Wisdom of the Crowd for Medical Image Annotation Using Training Performance and Task Features Open
A crucial bottleneck in medical artificial intelligence is high quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individual…
Improving Medical Image Decision‐Making by Leveraging Metacognitive Processes and Representational Similarity Open
Improving the accuracy of medical image interpretation can improve the diagnosis of numerous diseases. We compared different approaches to aggregating repeated decisions about medical images to improve the accuracy of a single decision mak…
Harnessing the Wisdom of the Confident Crowd in Medical Image Decision-making Open
Improving the accuracy of medical image interpretation is critical to improving the diagnosis of many diseases. Using both novices (undergraduates) and experts (medical professionals), we investigated methods for improving the accuracy of …
Machine learning based prediction of antibiotic sensitivity in patients with critical illness Open
Rising antibiotic resistance inflicts a heavy burden on healthcare, both clinically and economically. Owing to the time required to obtain culture and sensitivity test results, quite often the clinicians rely on their experience and static…