Examining the COVID-19 case growth rate due to visitor vs. local mobility in the United States using machine learning.

Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread in the United States between March 1, 2020, and December 31, 2020. Two metrics, namely local and visitor transmission risk, were extracted from mobility data to capture the transmission potential of COVID-19 through mobility. A combination of the three factors: the current number of cases, local transmission risk, and the visitor transmission risk, are used to model the future number of cases using various machine learning models. The factors that contribute to better forecast performance are the ones that impact the number of cases. The statistical significance of the forecasts is also evaluated using the Diebold-Mariano test. Finally, the performance of models is compared for three waves across all 50 states. The results show that visitor mobility significantly impacts the case growth by improving the prediction accuracy by 33.78%. We also observe that the impact of visitor mobility is more pronounced during the first peak, i.e., March-June 2020.

A multimodal sensor dataset for continuous stress detection of nurses in a hospital.

Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.

Exploring the relationship between mobility and COVID- 19 infection rates for the second peak in the United States using phase-wise association.

Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID- 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID- 19 infection growth rates once lockdowns are lifted, primarily due to modulation by other factors such as face masks, social distancing, and the non-linear patterns of both mobility and infection growth. This paper introduces a piece-wise approach to better explore the phase-wise association between state-level COVID- 19 incidence data and anonymized mobile phone data for various states in the United States. Prior literature analyzed the linear correlation between mobility and the number of cases during the early stages of the pandemic. However, it is important to capture the non-linear dynamics of case growth and mobility to be usable for both tracking and forecasting COVID- 19 infections, which is accomplished by the piece-wise approach. The associations between mobility and case growth rate varied widely for various phases of the epidemic curve when the stay-at-home orders were lifted. The mobility growth patterns had a strong positive association of 0.7 with the growth in the number of cases, with a lag of 5 to 7 weeks, for the fast-growth phase of the pandemic, for only 20 states that had a peak between July 1st and September 30, 2020. Overall though, mobility cannot be used to predict the rise in the number of cases after initial lockdowns have been lifted. Our analysis explores the gradual diminishing value of mobility associations in the later stage of the outbreak. Our analysis indicates that the relationship between mobility and the increase in the number of cases, once lockdowns have been lifted, is tenuous at best and there is no strong relationship between these signals. But we identify the remnants of the last associations in specific phases of the growth curve.

Association mining based approach to analyze COVID-19 response and case growth in the United States.

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

MetaMap Lite in Excel: Biomedical Named-Entity Recognition for Non-Technical Users.

We developed an easy-to-use tool for non-technical biomedical researchers to conduct Named-Entity Recognition (NER) on biomedical text, in a familiar spreadsheet environment. The system is a simple, offline, easy to install, end-user front-end to the new MetaMap Lite. Early adopters found it to be a quick starting-point to incorporate NER in their investigations.

"Doing the yellows" -- analysis of medication review processes by different clinicians in long term care.

Medication regimens in long term care are intermittently revised through the participation of multiple clinicians, different institutions, through time. Such revisions have potential to result in inconsistencies and errors. Periodic revisions are undertaken to address these. We examined these review tasks, as performed by clinicians, from a broad professional spectrum that consisted of nursing consultants, a nurse care manager, a pharmacist and a pharmacy technician.

The MERG Suite: Tools for discovering competencies and associated learning resources.

BACKGROUND: As the demands for competency-based education grow, the need for standards-based tools to allow for publishing and discovery of competency-based learning content is more pressing. This project focused on developing federated discovery services for competency-based medical e-learning content. METHODS: We built a tool suite for authoring and discovery of medical e-learning metadata. The end-user usability of the tool suite was evaluated through a web-based survey. RESULTS: The suite, implemented as an open-source system, was evaluated to identify areas for improvement. CONCLUSION: The MERG suite is a starting point for organizations implementing competency-based e-learning resources.

Enhancing access to the Bibliome: the TREC 2004 Genomics Track.

BACKGROUND: The goal of the TREC Genomics Track is to improve information retrieval in the area of genomics by creating test collections that will allow researchers to improve and better understand failures of their systems. The 2004 track included an ad hoc retrieval task, simulating use of a search engine to obtain documents about biomedical topics. This paper describes the Genomics Track of the Text Retrieval Conference (TREC) 2004, a forum for evaluation of IR research systems, where retrieval in the genomics domain has recently begun to be assessed. RESULTS: A total of 27 research groups submitted 47 different runs. The most effective runs, as measured by the primary evaluation measure of mean average precision (MAP), used a combination of domain-specific and general techniques. The best MAP obtained by any run was 0.4075. Techniques that expanded queries with gene name lists as well as words from related articles had the best efficacy. However, many runs performed more poorly than a simple baseline run, indicating that careful selection of system features is essential. CONCLUSION: Various approaches to ad hoc retrieval provide a diversity of efficacy. The TREC Genomics Track and its test collection resources provide tools that allow improvement in information retrieval systems.

Adopting e-learning standards in health care: competency-based learning in the medical informatics domain.

Like many forms of education, health professions education is increasingly competency-based. At the same time, there is growing use of e-learning technologies, which can be linked to competencies via emerging e-learning standards. Health care has been slow to adopt competencies and e-learning standards. We report our efforts to facilitate access to competencies and e-learning content in the medical informatics domain, linked by content-competency associations, based on standards developed by the MedBiquitous Consortium. We demonstrate that such standards can be successfully used and their implementation in other domains is warranted.

A standards-based approach for facilitating discovery of learning objects at the point of care.

Recent concerns about the quality and safety of healthcare practice provide an imperative for discovering and accessing learning resources. The growing ubiquity of the Internet, World Wide Web, and on-line educational content provide opportunity for healthcare practitioners to identify and master learning in a granular and rapid fashion. The e-learning community at large has developed a number of standards to facilitate interoperability of learner competencies, metadata describing on-line content, and packaging and navigation of such content. The overall goal of our project is to enable healthcare professionals to easily and rapidly discover learning content at the point of care. This discovery and access of learning content will be based on healthcare-specific extensions of existing e-learning standards, which are themselves based on other Web standards, such as Web Services.

Enhancing access to the Bibliome: the TREC Genomics Track.

The growing amount of scientific discovery in genomics and related biomedical disciplines has led to a corresponding increase in the amount of on-line data and information. A new challenge for biomedical researchers has been how to access and manage this ever-increasing quantity of information. The Text Retrieval Conference (TREC) has implemented a Genomics Track to create an experimental environment for research in the use of information retrieval systems in the genomics domain. In the first year of the track, an ad hoc document retrieval task and an information extraction task were carried out by 29 research groups. Future work will focus on more complex data sources, searching tasks, and types of experiments.