Correction to: Innovation of health data science curricula.
[This corrects the article DOI: 10.1093/jamiaopen/ooac073.].
Author(s):
DOI: 10.1093/jamiaopen/ooac098
[This corrects the article DOI: 10.1093/jamiaopen/ooac073.].
Author(s):
DOI: 10.1093/jamiaopen/ooac098
To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions.
Author(s): Brink, Laura, Coombs, Laura P, Kattil Veettil, Deepak, Kuchipudi, Kashyap, Marella, Sailaja, Schmidt, Kendall, Nair, Sujith Surendran, Tilkin, Michael, Treml, Christopher, Chang, Ken, Kalpathy-Cramer, Jayashree
DOI: 10.1093/jamiaopen/ooac094
To demonstrate the utility of growthcleanr, an anthropometric data cleaning method designed for electronic health records (EHR).
Author(s): Lin, Pi-I D, Rifas-Shiman, Sheryl L, Aris, Izzuddin M, Daley, Matthew F, Janicke, David M, Heerman, William J, Chudnov, Daniel L, Freedman, David S, Block, Jason P
DOI: 10.1093/jamiaopen/ooac089
To gain insights into how data vendor companies (DVs), an important source of de-identified/anonymized licensed patient-related data (D/ALD) used in clinical informatics research in life sciences and the pharmaceutical industry, characterize, conduct, and communicate data quality assessments to researcher purchasers of D/ALD.
Author(s): Erwin Johnson, C, Colquhoun, Daniel, Ruppar, Daniel A, Vetter, Sascha
DOI: 10.1093/jamiaopen/ooac093
One challenge that arises when analyzing mobile health (mHealth) data is that updates to the proprietary algorithms that process these data can change apparent patterns. Since the timings of these updates are not publicized, an analytic approach is necessary to determine whether changes in mHealth data are due to lifestyle behaviors or algorithmic updates. Existing methods for identifying changepoints do not consider multiple types of changepoints, may require prespecifying the [...]
Author(s): Quinn, Matthew, Chung, Arlene, Glass, Kimberly
DOI: 10.1093/jamiaopen/ooac090
Electronic health records (EHRs) are often used for recruitment into research studies, as they efficiently facilitate targeted outreach. While studies increasingly are reaching out to potential participants through the EHR patient portal, there is little available information about which approaches are most effective. We surveyed all investigators at one academic medical center who had used the Epic MyChart patient portal for recruitment. We found that messages were typically adapted for [...]
Author(s): Sherman, Scott E, Langford, Aisha T, Chodosh, Joshua, Hampp, Carina, Trachtman, Howard
DOI: 10.1093/jamiaopen/ooac092
Establish a baseline of informatics professionals' perspectives on climate change and health.
Author(s): Sarabu, Chethan, Deonarine, Andrew, Leitner, Stefano, Fayanju, Oluseyi, Fisun, Myroslava, Nadeau, Kari
DOI: 10.1093/jamia/ocac199
Meditation with mobile apps has been shown to improve mental and physical health. However, regular, long-term meditation app use is needed to maintain these health benefits, and many people have a difficult time maintaining engagement with meditation apps over time. Our goal was to determine the length of the timeframe over which usage data must be collected before future app abandonment can be predicted accurately in order to better target [...]
Author(s): Fowers, Rylan, Berardi, Vincent, Huberty, Jennifer, Stecher, Chad
DOI: 10.1093/jamia/ocac169
Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through [...]
Author(s): Kuo, Tsung-Ting, Jiang, Xiaoqian, Tang, Haixu, Wang, XiaoFeng, Harmanci, Arif, Kim, Miran, Post, Kai, Bu, Diyue, Bath, Tyler, Kim, Jihoon, Liu, Weijie, Chen, Hongbo, Ohno-Machado, Lucila
DOI: 10.1093/jamia/ocac165
Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis.
Author(s): Golovanevsky, Michal, Eickhoff, Carsten, Singh, Ritambhara
DOI: 10.1093/jamia/ocac168