Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal-external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts

Traditions of research into interruptions in healthcare: A conceptual review

Background Researchers from diverse theoretical backgrounds have studied workplace interruptions in healthcare, leading to a complex and conflicting body of literature. Understanding pre-existing viewpoints may advance the field more effectively than attempts to remove bias from investigations. Objective To identify research traditions that have motivated and guided interruptions research, and to note research questions posed, gaps in approach, and possible avenues for future research. Methods A critical review was conducted of research on interruptions in healthcare. Two researchers identified core research communities based on the community’s motivations, philosophical outlook, and methods. Among the characteristics used to categorise papers into research communities were the predominant motivation for studying interruptions, the research questions posed, and key contributions to the body of knowledge on interruptions in healthcare. In cases where a paper approached an equal number of characteristics from two traditions, it was placed in a blended research community. Results A total of 141 papers were identified and categorised; all papers identified were published from 1994 onwards. Four principal research communities emerged: epidemiology, quality improvement, cognitive systems engineering (CSE), and applied cognitive psychology. Blends and areas of mutual influence between the research communities were identified that combine the benefits of individual traditions, but there was a notable lack of blends incorporating quality improvement initiatives. The question most commonly posed by researchers across multiple communities was: what is the impact of interruptions? Impact was measured as a function of task time or risk in the epidemiology tradition, situation awareness in the CSE tradition, or resumption lag (time to resume an interrupted task) in the applied cognitive psychology tradition. No single question about interruptions in healthcare was shared by all four of the core communities. Conclusions Much research on workplace interruptions in healthcare can be described in terms of fundamental values of four distinct research traditions and the communities that bring the values and methods: of those research traditions to their investigations. Blends between communities indicate that mutual influence has occurred as interruptions research has progressed. It is clear from this review that there is no single or privileged perspective to study interruptions. Instead, these findings suggest that researchers investigating interruptions in healthcare would benefit from being more aware of different perspectives from their own, especially when they consider workplace interventions to reduce interruptions

An observational feasibility study of a new anaesthesia drug storage tray

Drug errors in the anaesthetic domain remains a serious cause of iatrogenic harm. To help reduce this issue, we aimed to explore the potential impact of a simple colour-coded tray to drug preparation and storage on safe drug administration during anaesthesia. Over a six-month period, a total of 30 cases were observed. The observations were conducted at three NHS Trusts by three different trained researchers. Ten observations involved the standard drug trays in ‘normal’ practice and 20 observations, before and after, were conducted where the new “Rainbow trays” were used. A total of 20 semi-structured interviews were conducted immediately upon completing the second observation with the involved anaesthetists. All discussions and detailed notes taken were transcribed and qualitatively analysed using line-byline coding. These codes were then synthesized into themes. Current practice using unicompartmental trays is quick, cheap, and portable but linked to potential or actual harmful errors such as syringe swaps. The Rainbow trays, seem to aid drug identification, allow for drug separation and act as a prompt to guard against drug errors. Limitations to the feasibility of use were around design and placement. The Rainbow trays were perceived as likely to reduce drug errors and improve patient safety. Additionally, there was an overall preference for this novel system at all three sites, as they were perceived to be easy to use and effective

Detection and classification of ADHD from EEG signals using tunable Q-factor wavelet transform

The automatic identification of Attention Deficit Hyperactivity Disorder (ADHD) is essential for developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has been a lot of interest in ADHD detection from EEG signals because it seemed to be a rapid method for identifying and treating this disorder. This paper proposes a technique for detecting ADHD from EEG signals with the nonlinear features extracted using tunable Q-wavelet transform (TQWT). The 16 channels of EEG signal data are decomposed into the optimal amount of time-frequency sub-bands using the TQWT filter banks. The unique feature vectors are evaluated using Katz and Higuchi nonlinear fractal dimension methods at each decomposed levels. An Artificial Neural Network classifier with a 10-fold cross-validation method is found to be an effective classifier for discriminating ADHD and normal subjects. Different performance metrics reveal that the proposed technique could effectively classify the ADHD and normal subjects with the highest accuracy. The statistical analysis showed that the Katz and Higuchi nonlinear feature estimation methods provide potential features that can be classified with high accuracy, sensitivity, and specificity and is suitable for automatic detection of ADHD. The proposed system is capable of accurately distinguishing between ADHD and non-ADHD subjects with a maximum accuracy of 100%

Phoenix: Federated Learning for Generative Diffusion Model

Thesis (Master's)--University of Washington, 2023Generative AI has made impressive strides in enabling users to create diverse and realistic visual content such as images, videos, and audio. However, training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility. Federated learning is an approach that uses decentralized techniques to collaboratively train a shared deep learning model while retaining the training data on individual edge devices to preserve data privacy. This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources, using federated learning techniques. Diffusion models, a newly emerging generative model, show promising results in achieving superior quality images than Generative Adversarial Networks (GANs). Our proposed method Phoenix is an unconditional diffusion model that leverages strategies to improve the data diversity of generated samples even when trained on data with statistical heterogeneity (Non-IID data). We demonstrate how our approach outperforms the default diffusion model in a federated learning setting. These results are indicative that high-quality samples can be generated by maintaining data diversity, preserving privacy, and reducing communication between data sources, offering exciting new possibilities in the field of generative AI