Functionalized Nanofinger Enhances Pretrained Language Model Performance for Ultrafast Early Warning of Heart Attacks.

Journal: ACS applied bio materials
Published Date: Jul 20, 2025

Abstract

Heart attacks are the leading cause of death worldwide, which means an accurate early warning system is needed. Traditional methods, such as an electrocardiogram (ECG) and blood test, usually require expert interpretation and take more than 15 min to obtain diagnostic results, which often results in delayed treatment. Our previous work [Liu, Z. 2023, 19(2), e2204719] developed a functionalized nanofinger platform utilizing Raman spectroscopy and machine learning (ML) to detect heart attack-related biomarkers, brain natriuretic peptide (BNP) in blood samples, achieving a high true positive rate (98%) but with limited true negative accuracy. This study enhances diagnostic accuracy by integrating the AI-driven analysis of patient symptoms with blood biomarker analysis. We incorporate additional patient features, including current and historical symptoms. We fused the biomarker test results, which is a one-dimensional probability vector ranging from 0 to 1, with patient symptom description as part of the sentence, which is then processed by a fine-tuned pretrained language model to generate embeddings and passed to a classification head for diagnosis. For data augmentation, we employ generative models to synthesize realistic patient cases, expanding the data set and improving model robustness. Our approach achieves an accuracy rate of 99.19%, outperforming conventional diagnostic methods. Given the difficulty of obtaining sufficient clinical data, this study presents a scalable AI-based solution for early-stage heart attack detection by integrating functionalized nanofinger-based blood analysis with large language models (LLMs) and leveraging generative models to synthesize realistic patient cases for data augmentation. This approach significantly advances the automation of diagnosis in modern healthcare and can be readily adapted to other disease detection tasks.

Authors

  • Hongming Zhang
    College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
  • Zerui Liu
    Faculty of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.
  • Heming Sun
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089-0271, United States.
  • Yunxiang Wang
    National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Ting-Hao Hsu
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089-0271, United States.
  • Sushmit Hossain
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089-0271, United States.
  • Nishat Tasnim Hiramony
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089-0271, United States.
  • Himaddri Roy
    Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089-0271, United States.
  • Hao Zhou
    State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan, Hubei 430030, China.
  • Matthew Tan
    West Windsor-Plainsboro High School South, 346 Clarksville Rd, Princeton Junction, New Jersey 08550, United States.
  • Edward J Liu
    Los Altos High School, 201 Almond Avenue, Los Altos, California 94022, United States.
  • Yihao Wang
    Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China.
  • Fanxin Liu
    Department of Applied Physics, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China.
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

Keywords

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