Julia Lane Discusses Best Practices for Social Data Science

2019 ◽  
Keyword(s):  
Best Practices ◽  
Data Science ◽  
Social Data

scholarly journals Correction to: An ethico-legal framework for social data science

2021 ◽  
Author(s):  
Nikolaus Forgó ◽  
Stefanie Hänold ◽  
Jeroen van den Hoven ◽  
Tina Krügel ◽  
Iryna Lishchuk ◽  
...  
Keyword(s):  
Data Science ◽  
Legal Framework ◽  
Social Data

A view from anthropology: Should anthropologists fear the data machines?

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110436
Author(s):  
Kristoffer Albris ◽  
Eva I Otto ◽  
Sofie L Astrupgaard ◽  
Emilie Munch Gregersen ◽  
Laura Skousgaard Jørgensen ◽  
...  
Keyword(s):  
Data Science ◽  
Ethnographic Methods ◽  
Social Data ◽  
Computational Tools ◽  
Future Role ◽  
Digital Methods

If you are an anthropologist wanting to use digital methods or programming as part of your research, where do you start? In this commentary, we discuss three ways in which anthropologists can use computational tools to enhance, support, and complement ethnographic methods. By presenting our reflections, we hope to contribute to the stirring conversations about the potential future role(s) of (social) data science vis-a-vis anthropology and ethnography, and to inspire other anthropologists to take up the use of digital methods, programming, and computational tools in their own research.

scholarly journals COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248128
Author(s):  
Mark Stewart ◽  
Carla Rodriguez-Watson ◽  
Adem Albayrak ◽  
Julius Asubonteng ◽  
Andrew Belli ◽  
...  
Keyword(s):  
Best Practices ◽  
Adverse Events ◽  
Data Science ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Parallel Analysis ◽  
Science Data ◽  
Systems Research ◽  
Cox Proportional Hazards Models ◽  
Global Threat

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

scholarly journals A Review of AI and Data Science Support for Cancer Management

2020 ◽  
Author(s):  
E. Parimbelli ◽  
S. Wilk ◽  
R. Cornet ◽  
P. Sniatala ◽  
K. Sniatala ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Best Practices ◽  
Cancer Patients ◽  
Predictive Modeling ◽  
Data Science ◽  
Cancer Management ◽  
Research Challenges ◽  
Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

The sharing economy: a hedge against recession setbacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Reza Habibi ◽  
Chiranjeev S. Kohli
Keyword(s):  
Best Practices ◽  
Design Methodology ◽  
Data Science ◽  
Science And Technology ◽  
Sharing Economy ◽  
Economic Sector ◽  
Content Type ◽  
Key Characteristics ◽  
The Future ◽  
Conceptual Paper

Purpose This paper aims to provide lessons from the emergence of the sharing economy after the 2008 recession and helps managers prepare more effectively for recessions in the future. Design/methodology/approach In this conceptual paper, the authors build on research on the sharing economy and study the best practices contributing to the sharing economy’s emergence and growth after the 2008 recession. The authors identify the key characteristics of this new economic sector and share lessons that can be used by other companies. Findings The authors recommend five major takeaways: seeking a more flexible supply; actively watching the trends; leveraging customers like employees; using advanced data science and technology like the sharing economy companies; and proactively avoiding panicked responses. This will help companies succeed during recessionary times – and the boom times that follow. Originality/value This is the first paper that, to the best of the authors’ knowledge, investigates the interplay between the sharing economy and recession and highlights practical lessons.

scholarly journals Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes

2020 ◽  
pp. 108-116
Author(s):  
Jill S. Barnholtz-Sloan ◽  
Dana E. Rollison ◽  
Amrita Basu ◽  
Alexander D. Borowsky ◽  
Alex Bui ◽  
...  
Keyword(s):  
Best Practices ◽  
Patient Outcomes ◽  
National Cancer Institute ◽  
Data Science ◽  
Cancer Centers ◽  
Face To Face ◽  
The World ◽  
Population Science ◽  
History Of ◽  
National Forum

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute–funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders’ titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was “intersections between informatics, data science, and population science.” We conclude with a discussion on “hot topics” on the horizon for cancer informatics.

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