Correction to: In with the old, in with the new: machine learning for time to event biomedical research.
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DOI: 10.1093/jamia/ocac243
Author(s):
DOI: 10.1093/jamia/ocac243
Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development [...]
Author(s): Zhang, Meina, Zhu, Linzee, Lin, Shih-Yin, Herr, Keela, Chi, Chih-Lin, Demir, Ibrahim, Dunn Lopez, Karen, Chi, Nai-Ching
DOI: 10.1093/jamia/ocac231
A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP.
Author(s): Wan, Nicholas C, Yaqoob, Ali A, Ong, Henry H, Zhao, Juan, Wei, Wei-Qi
DOI: 10.1093/jamia/ocac234
Many genetic variants are classified, but many more are variants of uncertain significance (VUS). Clinical observations of patients and their families may provide sufficient evidence to classify VUS. Understanding how long it takes to accumulate sufficient patient data to classify VUS can inform decisions in data sharing, disease management, and functional assay development.
Author(s): Casaletto, James, Cline, Melissa, Shirts, Brian
DOI: 10.1093/jamia/ocac232
This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.
Author(s): Marsolo, Keith, Kiernan, Daniel, Toh, Sengwee, Phua, Jasmin, Louzao, Darcy, Haynes, Kevin, Weiner, Mark, Angulo, Francisco, Bailey, Charles, Bian, Jiang, Fort, Daniel, Grannis, Shaun, Krishnamurthy, Ashok Kumar, Nair, Vinit, Rivera, Pedro, Silverstein, Jonathan, Zirkle, Maryan, Carton, Thomas
DOI: 10.1093/jamia/ocac229
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocac255
There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders' theories concerning how and in what contexts AI is likely to become integrated into pathology.
Author(s): King, Henry, Williams, Bethany, Treanor, Darren, Randell, Rebecca
DOI: 10.1093/jamia/ocac254
While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework.
Author(s): Singh, Neetu, Varshney, Upkar
DOI: 10.1093/jamia/ocac253
Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications.
Author(s): Dolin, Robert H, Heale, Bret S E, Alterovitz, Gil, Gupta, Rohan, Aronson, Justin, Boxwala, Aziz, Gothi, Shaileshbhai R, Haines, David, Hermann, Arthur, Hongsermeier, Tonya, Husami, Ammar, Jones, James, Naeymi-Rad, Frank, Rapchak, Barbara, Ravishankar, Chandan, Shalaby, James, Terry, May, Xie, Ning, Zhang, Powell, Chamala, Srikar
DOI: 10.1093/jamia/ocac246
Online health communities (OHCs) have been identified as important outlets for social support and community connection for adolescents and young adults (AYAs) living with chronic illnesses. Despite evident benefits, there remains a gap in research on methods to maximize sustained patient engagement within OHCs. This study assessed per-patient daily commenting rates over time, as well as associations with program staff and volunteer-facilitated events and engagement.
Author(s): Walker, Andrew L, Swygert, Anna, Marchi, Emily, Lebeau, Kelsea, Haardörfer, Regine, Livingston, Melvin D
DOI: 10.1093/jamia/ocac252