Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning.

While machine learning (ML) has shown great promise in medical diagnostics, a major challenge is that ML models do not always perform equally well among ethnic groups. This is alarming for women's health, as there are already existing health disparities that vary by ethnicity. Bacterial Vaginosis (BV) is a common vaginal syndrome among women of reproductive age and has clear diagnostic differences among ethnic groups. Here, we investigate the ability of four ML algorithms to diagnose BV. We determine the fairness in the prediction of asymptomatic BV using 16S rRNA sequencing data from Asian, Black, Hispanic, and white women. General purpose ML model performances vary based on ethnicity. When evaluating the metric of false positive or false negative rate, we find that models perform least effectively for Hispanic and Asian women. Models generally have the highest performance for white women and the lowest for Asian women. These findings demonstrate a need for improved methodologies to increase model fairness for predicting BV.

Exploring differences between self-report and electrophysiological indices of drowsy driving: A usability examination of a personal brain-computer interface device.

INTRODUCTION: Impaired driving has resulted in numerous accidents, fatalities, and costly damage. One particularly concerning type of impairment is driver drowsiness. Despite advancements, modern vehicle safety systems remain ineffective at keeping drowsy drivers alert and aware of their state, even temporarily. Until recently the use of user-centric brain-computer interface (BCI) devices to capture electrophysiological data relating to driver drowsiness has been limited. METHOD: In this study, 25 participants drove on a simulated roadway under drowsy conditions. RESULTS: Neither subjective nor electrophysiological measures differed between individuals who showed overt signs of drowsiness (prolonged eye closure) during the drive. However, the directionality and effect size estimates provided by the BCI device suggested the practicality and feasibility of its future implementation in vehicle safety systems. Practical applications: This research highlights opportunities for future BCI device research for use to assess the state of drowsy drivers in a real-world context.

INVENTION IS NOT AN OPTION.

Inventors help solve all kinds of problems. The AAAS-Lemelson Invention Ambassador program celebrates inventors who have an impact on global challenges, making our communities and the globe better, one invention at a time. In this paper, we introduce two of these invention ambassadors: Dr. Suzie Pun and Dr. Juan Gilbert. Dr. Suzie Pun is the Robert F. Rushmer Professor of Bioengineering, an adjunct professor of chemical engineering, and a member of the Molecular Engineering and Sciences Institute at the University of Washington. Dr. Juan Gilbert is the Andrew Banks Family Preeminence Endowed Professor and chair of the Computer & Information Science & Engineering Department at the University of Florida. Both have a passion for solving problems and are dedicated to teaching their students to change the world.

Designing an over-the-counter consumer decision-making tool for older adults.

PURPOSE: Older adults are at increased risk of adverse drug events due to medication. Older adults tend to take more medication and are at higher risk of chronic illness. Over-the-counter (OTC) medication does not require healthcare provider oversight and understanding OTC information is heavily dependent on a consumer's ability to understand and use the medication appropriately. Coupling health technology with effective communication is one approach to address the challenge of communicating health and improving health related tasks. However, the success of many health technologies also depends on how well the technology is designed and how well it addresses users needs. This is especially true for the older adult population. This paper describes (1) a formative study performed to understand how to design novel health technology to assist older adults with OTC medication information, and (2) how a user-centered design process helped to refine the initial assumptions of user needs and help to conceptualize the technology. METHODS: An iterative design process was used. The process included two brainstorming and review sessions with human-computer interaction researchers and design sessions with older adults in the form of semi-structured interviews. Methods and principles of user-centered research and design were used to inform the research design. Two researchers with expertise in human-computer interaction performed expert reviews of early system prototypes. After initial prototypes were developed, seven older adults were engaged in semi-structured interviews to understand usability concerns and features and functionality older adults may find useful for selecting appropriate OTC medication. RESULTS: Eight usability concerns were discovered and addressed in the two rounds of expert review, and nine additional usability concerns were discovered in design sessions with older adults. Five themes emerged from the interview transcripts as recommendations for design. These recommendations represent opportunities for technology such as the one described in this paper to support older adults in the OTC decision-making process. DISCUSSION: This paper illustrates the use of an iterative user-centered process in the formative stages of design and its usefulness for understanding aspects of the technology design that are useful to older adults when making decisions about OTC medication. The technology support mechanisms included in the initial model were revised based on the results from the iterative design sessions and helped to refine and conceptualize the system being designed.

Evaluating the ergonomics of BCI devices for research and experimentation.

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php ) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation. STATEMENT OF RELEVANCE: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.