Qianyu Yuan

Preparation and Study on Sulfated Nanocellulose/Anthocyanin pH-Sensitive Packaging Materials to Track Food Freshness

Lan Yang, Qianyu Yuan, Chien‐Teng Hsieh, Ching-Wen Lou et autres

Meat products are prone to spoilage during storage and transportation due to the decomposition of amino acids and proteins, which generates volatile amines and elevates pH levels. In recent years, research on pH indicator labels for food has significantly increased. This study …

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Ensuring Reliability of Curated Electronic Health Record–Derived Data: The Validation of Accuracy for Large Language Model–/Machine Learning–Extracted Information and Data (VALID) Framework

Melissa Soria Estévez, Nisha Singh, Lauren Dyson, Blythe Adamson et autres

Large language models (LLMs) are increasingly used to extract clinical data from electronic health records, offering significant improvements in scalability and efficiency for real-world data (RWD) curation in oncology. However, the adoption of LLMs introduces new challenges in ensuring the reliability, accuracy, …

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Klinische Ergebnisse der perioperativen Immuntherapie beim resektablen, nicht-kleinzelligen Lungenkarzinom

Aakash P. Desai, Karen Schwed, Laurynas Kalesinskas, Qianyu Yuan et autres

Bedeutung: Lungenkrebs ist nach wie vor eine der häufigsten Ursachen für krebsbedingte Todesfälle. Das nicht-kleinzellige Lungenkarzinom (NSCLC) im Frühstadium macht dabei 40–45% der Neudiagnosen aus. Trotz der Einführung der perioperativen Chemoimmuntherapie sind die klinischen Daten zu ihrer Anwendung und ihren Ergebnissen nach …

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MSR39 Assessing Bias in LLM-Extracted Real-World Data: A Health Equity Analysis of Access to Care and Outcomes in Metastatic Breast Cancer

Olive Mbah, Gene G. Ho, Catherine Keane, Qianyu Yuan et autres

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Association Between Whole Blood Cell-Derived Inflammatory Markers and All-Cause Mortality in Patients with Sepsis-Related Acute Kidney Injury and Development of a Machine Learning Prognostic Model

Xinghe Shangguan, Yongjie Luo, Xue Wu, Jianning Xu et autres

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Abstract B006: Using large language models for scalable extraction of real-world progression events across multiple cancer types

Aaron Benjamin Cohen, Konstantin Krismer, Kelly Magee, James Gippetti et autres

Abstract Background: Accurate identification of cancer progression events from electronic health records (EHRs) can help enable promising oncology applications such as predicting disease trajectory, assessing treatment efficacy, and generating real-world evidence. These use cases require both large-scale and high-quality data but manual …

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MSR24 Leveraging Machine Learning to Assess the Association of Rash and Survival in Patients With Advanced NSCLC

Qianyu Yuan, Aaron Dolor, Yunzhi Qian, Melissa Estévez et autres

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MSR142 Performance Assessment and Validation of Real-World Response Data Generated Using a Deep Learning-Based Natural Language Processing Model Across Multiple Solid Tumors

Kelly Magee, Qianyu Yuan, Auriane Blarre, Aaron Benjamin Cohen et autres

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Clinical Outcomes of Perioperative Immunotherapy in Resectable Non–Small Cell Lung Cancer

Aakash P. Desai, Karen Schwed, Laurynas Kalesinskas, Qianyu Yuan et autres

Importance: Lung cancer remains a leading cause of cancer-related mortality, with early-stage non-small cell lung cancer (NSCLC) accounting for 40% to 45% of new diagnoses. Despite the adoption of perioperative chemoimmunotherapy, clinical data on its use and outcomes remain limited. Objective: To …

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Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) Framework

Melissa Estévez, Nisha Singh, Lauren Dyson, Blythe J. S. Adamson et autres

Large language models (LLMs) are increasingly used to extract clinical data from electronic health records (EHRs), offering significant improvements in scalability and efficiency for real-world data (RWD) curation in oncology. However, the adoption of LLMs introduces new challenges in ensuring the reliability, …

Concordance of response-based clinical trial and machine learning–generated real-world end points.

Qianyi Zhang, Konstantin Krismer, Yichen Lu, Qianyu Yuan et autres

e13625 Background: Real-world evidence (RWE) is increasingly used to complement clinical trial data in oncology, providing rapid insights to inform study design and drug development. Using deep learning, natural language processing (NLP)-based machine learning models, we developed a real-world response (rwR) approach.This …

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Exploration and application prospect of the advanced technology of time–temperature indicators

Lan Yang, Qianyu Yuan, Ting‐Ting Li, Ching‐Wen Lou et autres

The time–temperature indicator (TTI) is a kind of indicator that can monitor the safety of food, drugs and other products. It produces the cumulative effect of time–temperature through physical and mechanical deformation, chemical or biological reaction, and makes an irreversible and obvious …

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