Browsing by Subject "Data science"

Now showing 1 - 3 of 3
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    Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets
    (Wiley, 2021-10-14) Bastarache, Lisa; Brown, Jeffrey S.; Cimino, James J.; Dorr, David A.; Embi, Peter J.; Payne, Philip R. O.; Wilcox, Adam B.; Weiner, Mark G.; Medicine, School of Medicine
    Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.
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    The Medical Library Association Data Services Competency: A Framework for Data Science and Open Science Skills Development
    (Medical Library Association, 2020-04) Federer, Lisa; Foster, Erin Diane; Glusker, Ann; Henderson, Margaret; Read, Kevin; Zhao, Shirley; Ruth Lilly Medical Library, School of Medicine
    Increasingly, users of health and biomedical libraries need assistance with challenges they face in working with their own and others' data. Librarians have a unique opportunity to provide valuable support and assistance in data science and open science but may need to add to their expertise and skill set to have the most impact. This article describes the rationale for and development of the Medical Library Association Data Services Competency, which outlines a set of five key skills for data services and provides a course of study for gaining these skills.
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    Predicting Participant Engagement in a Social Media–Delivered Lifestyle Intervention Using Microlevel Conversational Data: Secondary Analysis of Data From a Pilot Randomized Controlled Trial
    (JMIR, 2022-07-28) Xu, Ran; Divito, Joseph; Bannor, Richard; Schroeder, Matthew; Pagoto, Sherry; Medicine, School of Medicine
    Background: Social media-delivered lifestyle interventions have shown promising outcomes, often generating modest but significant weight loss. Participant engagement appears to be an important predictor of weight loss outcomes; however, engagement generally declines over time and is highly variable both within and across studies. Research on factors that influence participant engagement remains scant in the context of social media-delivered lifestyle interventions. Objective: This study aimed to identify predictors of participant engagement from the content generated during a social media-delivered lifestyle intervention, including characteristics of the posts, the conversation that followed the post, and participants' previous engagement patterns. Methods: We performed secondary analyses using data from a pilot randomized trial that delivered 2 lifestyle interventions via Facebook. We analyzed 80 participants' engagement data over a 16-week intervention period and linked them to predictors, including characteristics of the posts, conversations that followed the post, and participants' previous engagement, using a mixed-effects model. We also performed machine learning-based classification to confirm the importance of the significant predictors previously identified and explore how well these measures can predict whether participants will engage with a specific post. Results: The probability of participants' engagement with each post decreased by 0.28% each week (P<.001; 95% CI 0.16%-0.4%). The probability of participants engaging with posts generated by interventionists was 6.3% (P<.001; 95% CI 5.1%-7.5%) higher than posts generated by other participants. Participants also had a 6.5% (P<.001; 95% CI 4.9%-8.1%) and 6.1% (P<.001; 95% CI 4.1%-8.1%) higher probability of engaging with posts that directly mentioned weight and goals, respectively, than other types of posts. Participants were 44.8% (P<.001; 95% CI 42.8%-46.9%) and 46% (P<.001; 95% CI 44.1%-48.0%) more likely to engage with a post when they were replied to by other participants and by interventionists, respectively. A 1 SD decrease in the sentiment of the conversation on a specific post was associated with a 5.4% (P<.001; 95% CI 4.9%-5.9%) increase in the probability of participants' subsequent engagement with the post. Participants' engagement in previous posts was also a predictor of engagement in subsequent posts (P<.001; 95% CI 0.74%-0.79%). Moreover, using a machine learning approach, we confirmed the importance of the predictors previously identified and achieved an accuracy of 90.9% in terms of predicting participants' engagement using a balanced testing sample with 1600 observations. Conclusions: Findings revealed several predictors of engagement derived from the content generated by interventionists and other participants. Results have implications for increasing engagement in asynchronous, remotely delivered lifestyle interventions, which could improve outcomes. Our results also point to the potential of data science and natural language processing to analyze microlevel conversational data and identify factors influencing participant engagement. Future studies should validate these results in larger trials.