AIMC Topic: Crowdsourcing

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On the Influence of Apologies on the Likelihood of Lawsuits in Cases of Perceived Medical Negligence: Analysis of Archival and Experimental Data.

Journal of medical Internet research
BACKGROUND: Disappointing medical care (DMC) encompasses cases of medical failures, malpractice, or errors. Literature suggests that individuals' perceptions of harm resulting from medical procedures influence their intention to seek legal recourse a...

Holistic Influence of Multimodal Medical Crowdfunding Affordances on Charitable Crowdfunding Outcome: Systematic Multimodel Analysis Study.

JMIR medical informatics
BACKGROUND: Medical crowdfunding has emerged as a critical tool to alleviate the financial burden of health care costs, particularly in regions where economic disparities limit access to medical treatment. Despite its potential, the success rates of ...

Crowdsourcing a Training Dataset of Question-and-Answer Pairs for AI-Enabled Health Information Tools on Sexually Transmitted Infections: Protocol for a Cross-Sectional Exploratory Survey Study.

JMIR research protocols
BACKGROUND: Sexually transmitted infections are a significant public health concern, particularly in sub-Saharan Africa, where their prevalence remains high. Promoting awareness and reducing stigma are essential strategies for addressing this challen...

Data quality in crowdsourcing and spamming behavior detection.

Behavior research methods
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data to improve analysis performance and reduce biases in subsequent machin...

Aggregating soft labels from crowd annotations improves uncertainty estimation under distribution shift.

PloS one
Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. Recent work has demonstrated that learning from a distribution over labels acquired f...

AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments.

PloS one
Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during cr...

Impact of Image Content on Medical Crowdfunding Success: A Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience.

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

EBioMedicine
BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term...

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Water research
Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, c...

Boosting wisdom of the crowd for medical image annotation using training performance and task features.

Cognitive research: principles and implications
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 individuals recr...