Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases ("data marts") can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software.
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http://dx.doi.org/10.1136/jamia.2009.000893 | DOI Listing |
Sleep Breath
January 2025
Department of Respiratory and Critical Care Medicine, Medical School of Nantong University, Nantong Key Laboratory of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China.
Background: The pathophysiology of obstructive sleep apnea (OSA) and diabetes mellitus (DM) is still unknown, despite clinical reports linking the two conditions. After investigating potential roles for DM-related genes in the pathophysiology of OSA, our goal is to investigate the molecular significance of the condition. Machine learning is a useful approach to understanding complex gene expression data to find biomarkers for the diagnosis of OSA.
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets. Experimental identification and validation of cancer driver genes are time-consuming and costly.
View Article and Find Full Text PDFJAMIA 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 PDFJ Mol Cell Cardiol Plus
March 2024
National Coalition of Independent Scholars, 125 Putney Road, Battleboro, VT 05301, United States.
Existing cardiovascular studies tend to suffer from small sample sizes and unaddressed confounders. Re-profiling of 9 microarray datasets revealed significant global gene expression differences between 358 failing and 191 non-failing left ventricles independent of age and sex ( = 5.1e-10).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!