Advancing the science of visualization of health data for lay audiences.
Author(s): Arcia, Adriana, Benda, Natalie C, Wu, Danny T Y
DOI: 10.1093/jamia/ocad255
Author(s): Arcia, Adriana, Benda, Natalie C, Wu, Danny T Y
DOI: 10.1093/jamia/ocad255
Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics.
Author(s): Graffy, Peter, Zimmerman, Lindsay, Luo, Yuan, Yu, Jingzhi, Choi, Yuni, Zmora, Rachel, Lloyd-Jones, Donald, Allen, Norrina Bai
DOI: 10.1093/jamia/ocad240
Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients.
Author(s): Feng, Wei, Wu, Honghan, Ma, Hui, Tao, Zhenhuan, Xu, Mengdie, Zhang, Xin, Lu, Shan, Wan, Cheng, Liu, Yun
DOI: 10.1093/jamia/ocad228
Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.
Author(s): Grabowska, Monika E, Van Driest, Sara L, Robinson, Jamie R, Patrick, Anna E, Guardo, Chris, Gangireddy, Srushti, Ong, Henry H, Feng, QiPing, Carroll, Robert, Kannankeril, Prince J, Wei, Wei-Qi
DOI: 10.1093/jamia/ocad233
The objective of this scoping review is to map methods used to study medication safety following electronic health record (EHR) implementation. Patterns and methodological gaps can provide insight for future research design.
Author(s): Pereira, Nichole, Duff, Jonathan P, Hayward, Tracy, Kherani, Tamizan, Moniz, Nadine, Champigny, Chrystale, Carson-Stevens, Andrew, Bowie, Paul, Egan, Rylan
DOI: 10.1093/jamia/ocad231
To construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to help better represent the often underrepresented physical and psychological CIH approaches in standard terminologies, and to also apply state-of-the-art natural language processing (NLP) techniques to help recognize them in the biomedical literature.
Author(s): Zhou, Huixue, Austin, Robin, Lu, Sheng-Chieh, Silverman, Greg Marc, Zhou, Yuqi, Kilicoglu, Halil, Xu, Hua, Zhang, Rui
DOI: 10.1093/jamia/ocad216
Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction.
Author(s): Lin, Wei-Chun, Chen, Aiyin, Song, Xubo, Weiskopf, Nicole G, Chiang, Michael F, Hribar, Michelle R
DOI: 10.1093/jamia/ocad213
Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments.
Author(s): Casillan, Aimiel, Florido, Michelle E, Galarza-Cornejo, Jamie, Bakken, Suzanne, Lynch, John A, Chung, Wendy K, Mittendorf, Kathleen F, Berner, Eta S, Connolly, John J, Weng, Chunhua, Holm, Ingrid A, Khan, Atlas, Kiryluk, Krzysztof, Limdi, Nita A, Petukhova, Lynn, Sabatello, Maya, Wynn, Julia
DOI: 10.1093/jamia/ocad207
We implemented a chatbot consent tool to shift the time burden from study staff in support of a national genomics research study.
Author(s): Savage, Sarah K, LoTempio, Jonathan, Smith, Erica D, Andrew, E Hallie, Mas, Gloria, Kahn-Kirby, Amanda H, Délot, Emmanuèle, Cohen, Andrea J, Pitsava, Georgia, Nussbaum, Robert, Fusaro, Vincent A, Berger, Seth, Vilain, Eric
DOI: 10.1093/jamia/ocad181
In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients.
Author(s): Zolnoori, Maryam, Sridharan, Sridevi, Zolnour, Ali, Vergez, Sasha, McDonald, Margaret V, Kostic, Zoran, Bowles, Kathryn H, Topaz, Maxim
DOI: 10.1093/jamia/ocad195