National Institutes of Health (NIH)

SCH: AI-Enhanced Multimodal Sensor-on-a-chip for Alzheimer's Disease Detection

We propose a new research paradigm aimed at addressing scientific questions in both biosensing and machine learning for the early prediction of Alzheimer's disease (AD), and at solving a grand challenge in the identification of minimally-invasive AD biomarkers in tear, saliva, and blood. Our goal is to develop a novel and minimally-invasive system that integrates a multimodal biosensing platform and a machine learning framework, which synergistically work together to significantly enhance the detection accuracy. The program will pioneer a novel Multimodal Optical, Mechanical, Electrochemical Nano-sensor with Twodimensional material Amplification (MOMENTA) platform for sensitive and selective detection of AD biomarkers. The sensor outputs are used for training the new Hierarchical Multimodal Machine Learning (HMML) framework, which not only automatically integrates the heterogeneous data from different modalities but also ranks the importance of different biosensors and biomarkers for AD prediction. Moreover, the framework is able to identify potential new biomarkers based on a statistical analysis of the learned weights on the input signals and provide feedback information to further improve the MOMENTA platform design. This interdisciplinary research brings together materials scientists who create new twodimensional (2D) material platforms for sensor enhancement, nanotechnology and device experts who advance chip-scale sensor platforms, data scientists who analyze data with machine learning methods to target early prediction of AD, and AD experts who help to identify potentially new AD biomarkers. The machine-learning-enhanced multi-modal sensor system will not only offer major performance boost compared to state-of-the-art, but also yield critical insights on new biomarker discovery for AD diagnosis at an early stage.

Students

  • Ziyi Yin (Penn State)
  • Ziyang Wang (Rice)
  • Tushar Sanjay Karnik (MIT)

    • Publications

  • AUTOMED: Automated Medical Risk Predictive Modeling on Electronic Health Records
    Suhan Cui, Jiaqi Wang, Xinning Gui, Ting Wang and Fenglong Ma
    Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2022), December 6-9, 2022, Las Vegas, NV, USA. (Regular paper acceptance rate: 167/842 = 19.8%)
  • MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
    Yuan Zhong, Suhan Cui, Jiaqi Wang, Ziyi Yin, Yaqing Wang, Houping Xiao, Mengdi Huai, Ting Wang and Fenglong Ma
    Proceedings of the 24th SIAM International Conference on Data Mining (SDM 2024), accepted. (acceptance rate: 29.2%)
  • Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions
    Suhan Cui, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang and Fenglong Ma
    Proceedings of the 24th SIAM International Conference on Data Mining (SDM 2024), accepted. (acceptance rate: 29.2%)
  • Hierarchical Pretraining on Multimodal Electronic Health Records
    Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang and Fenglong Ma
    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), December 6-10, 2023, Singapore, accepted.
  • ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Risk Levels and Risk Factors
    Junyu Luo, Zhi Qiao, Lucas Glass, Cao Xiao and Fenglong Ma
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), Oct. 21-25, 2023, Birmingham, UK, accepted.
  • pADR: Towards Personalized Adverse Drug Reaction Prediction
    Junyu Luo, Cheng Qian, Xiaochen Wang, Lucas Glass and Fenglong Ma
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), Oct. 21-25, 2023, Birmingham, UK, accepted.

    • Data

  • TBD

    • Softwares

  • AutoMed MedDiffusion AutoFM