Objective: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.
Materials And Methods: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy.
Results: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address.
Discussion: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.
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http://dx.doi.org/10.1093/jamia/ocac057 | DOI Listing |
JAMIA Open
February 2025
Department of Medicine, University of Wisconsin-Madison, Madison, WI 53792, United States.
Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.
Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.
Heliyon
December 2024
Univ Coimbra, Institute of Integrated Clinical Practice, Faculty of Medicine, Coimbra, Portugal.
[This corrects the article DOI: 10.1016/j.heliyon.
View Article and Find Full Text PDFMethodsX
June 2025
Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Str., 185910 Petrozavodsk, Russia.
[This corrects the article DOI: 10.1016/j.mex.
View Article and Find Full Text PDFJ Pathol Inform
January 2025
Harvard Medical School, Boston, MA, United States of America.
Objective: Thrombocytopenia is a common complication of hematopoietic stem-cell transplantation (HSCT), though many patients will become immune refractory to platelet transfusions over time. We built and evaluated an electronic health record (EHR)-integrated, standards-based application that enables blood-bank clinicians to match platelet inventory with patients using data previously not available at the point-of-care, like human leukocyte antigen (HLA) data for donors and recipients.
Materials And Methods: The web-based application launches as an EHR-embedded application or as a standalone application.
3 Biotech
February 2025
Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai, 200093 China.
Unlabelled: The study aims to investigate the clinicopathological significance of MRPL24 in human cancers, with a particular focus on breast cancer (BC). Comprehensive bioinformatics analyses were conducted using data from The Cancer Genome Atlas (TCGA) and various advanced database, including cBioPortal, UALCAN, TIMER, Prognoscan, TISIDB, KM Plotter, and The Human Protein Atlas, to provide a detailed evaluation of MRPL55's role in cancer. The findings were further validated through experimental studies.
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