We present a sensitivity-analysis and a Monte-Carlo algorithm for evaluating the uncertainty of multivariate microwave calibration models with regression residuals. We then use synthetic data to verify the performance of the algorithms and explore their limitations in the presence of correlated errors. The uncertainties we evaluate can be used to estimate the total uncertainty of a calibrated measurement when combined with the prediction intervals for that measurement.
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http://dx.doi.org/10.1109/tmtt.2020.2983358 | DOI Listing |
Curr Res Toxicol
December 2024
National Institute of Environmental Health Sciences, Division of Translational Toxicology, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
Mechanistically based non-animal methods for assessing skin sensitization hazard have been developed, but are not considered sufficient, individually, to conclusively define the skin sensitization potential or potency of a chemical. This resulted in the development of defined approaches (DAs), as documented in OECD TG 497, for combining information sources in a prescriptive manner to provide a determination of risk or potency. However, there are currently no DAs within OECD TG 497 that can derive a point of departure (POD) for risk assessment.
View Article and Find Full Text PDFInfect Drug Resist
January 2025
Tuberculosis Diagnosis and Treatment Center, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang Province, People's Republic of China.
Background: Immune checkpoint inhibitors (ICIs) have emerged as the first-line treatment for driver-negative advanced non-small cell lung cancer (NSCLC). However, there is uncertainty regarding the availability and timing of ICI initiation in patients with NSCLC combined with pulmonary tuberculosis (TB). Additionally, the implementation of dual therapy for anti-TB and anti-tumor treatment poses significant challenges in terms of avoiding drug-drug interactions and reducing adverse reactions during clinical diagnosis and treatment.
View Article and Find Full Text PDFCureus
December 2024
Family Health Unit New Directions, Unidade Local de Saúde do Alto Ave, Vizela, PRT.
Lung cancer is highly prevalent worldwide and is the leading cause of cancer-related death in Portugal. There is increasing evidence that low-dose computed tomography (LDCT) screening reduces mortality; however, few countries have implemented screening strategies. This review aims to gather the best evidence to assess the relevance of implementing lung cancer screening.
View Article and Find Full Text PDFEnsuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images.
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.
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