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 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
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
To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.
Author(s): Rangan, Ekanath Srihari, Pathinarupothi, Rahul Krishnan, Anand, Kanwaljeet J S, Snyder, Michael P
DOI: 10.1093/jamiaopen/ooac080
Author(s): Atwoli, Lukoye, Erhabor, Gregory E, Gbakima, Aiah A, Haileamlak, Abraham, Kayembe Ntumba, Jean-Marie, Kigera, James, Laybourn-Langton, Laurie, Mash, Bob, Muhia, Joy, Mulaudzi, Fhumulani Mavis, Ofori-Adjei, David, Okonofua, Friday, Rashidian, Arash, El-Adawy, Maha, Sidibé, Siaka, Snouber, Abdelmadjid, Tumwine, James, Yassien, Mohammad Sahar, Yonga, Paul, Zakhama, Lilia, Zielinski, Chris
DOI: 10.1093/jamiaopen/ooac084
A connected system with smart devices could transform patient care and empower patients control of their asthma.
Author(s): Hui, Chi Yan, McKinstry, Brian, Mclean, Susannah, Buchner, Mark, Pinnock, Hilary
DOI: 10.1093/jamiaopen/ooac110
To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions.
Author(s): Chapman, Alec B, Peterson, Kelly S, Rutter, Elizabeth, Nevers, Mckenna, Zhang, Mingyuan, Ying, Jian, Jones, Makoto, Classen, David, Jones, Barbara
DOI: 10.1093/jamiaopen/ooac114
In case of sudden-onset disasters (SODs), the World Health Organization deploys specialized emergency medical teams (EMTs); yet, the coordination and operation of such teams pose significant challenges. One issue is the lack of digital information systems and standards. We developed a highly customizable and scalable electronic medical record (EMR) system, tailored to EMT requirements, called the "Emergency Medical Team Operating System" (EOS). EOS was successfully tested through 9 realistic clinical [...]
Author(s): Schreiber, Erik, Gaebel, Jan, de Hoop, Tom, Neumuth, Thomas
DOI: 10.1093/jamiaopen/ooac106
Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory [...]
Author(s): Datta, Suparno, Morassi Sasso, Ariane, Kiwit, Nina, Bose, Subhronil, Nadkarni, Girish, Miotto, Riccardo, Böttinger, Erwin P
DOI: 10.1093/jamiaopen/ooac097