Browsing by Author "Ju, Yilong"

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    A novel automated junctional ectopic tachycardia detection tool for children with congenital heart disease
    (Elsevier, 2022) Waugh, Jamie L. S.; Patel, Raajen; Ju, Yilong; Patel, Ankit B.; Rusin, Craig G.; Jain, Parag N.
    Background Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease (CHD), with marked resemblance to normal sinus rhythm (NSR) often leading to delay in diagnosis. Objective To develop a novel automated arrhythmia detection tool to identify JET. Methods A single-center retrospective cohort study of children with CHD was performed. Electrocardiographic (ECG) data produced by bedside monitors is captured automatically by the Sickbay platform. Based on the detection of R and P wave peaks, 2 interpretable ECG features are calculated: P prominence median and PR interval interquartile range (IQR). These features are used as input to a simple logistic regression classification model built to distinguish JET from NSR. Results This study analyzed a total of 64.5 physician-labeled hours consisting of 509,833 cardiac cycles (R-R intervals), from 40 patients with CHD. The extracted P prominence median feature is much smaller in JET compared to NSR, whereas the PR interval IQR feature is larger in JET compared to NSR. The area under the receiver operating characteristic curve for the unseen patient test cohort was 93%. Selecting a threshold of 0.73 results in a true-positive rate of 90% and a false-positive rate of 17%. Conclusion This novel arrhythmia detection tool identifies JET, using 2 distinctive features of JET in ECG—the loss of a normal P wave and PR relationship—allowing for early detection and timely intervention.
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    Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures
    (PNAS, 2022) Bajomo, Mary M.; Ju, Yilong; Zhou, Jingyi; Elefterescu, Simina; Farr, Corbin; Zhao, Yiping; Neumann, Oara; Nordlander, Peter; Patel, Ankit; Halas, Naomi J.; Laboratory for Nanophotonics
    Surface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures.
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    Mechanistic Understanding of ML/AI Systems Through Interdisciplinary Scientific Applications
    (2024-12-03) Ju, Yilong; Patel, Ankit B.
    Modern artificial intelligence (AI) systems have achieved remarkable success across various scientific domains. However, fundamental questions remain about how these systems learn, make decisions, and generalize across different applications. This dissertation addresses these questions by systematically analyzing and improving AI systems through applications in physics, chemistry, and healthcare, demonstrating how mechanistic understanding can enhance practical performance. First, we develop a unifying framework for understanding convolutional neural networks (CNNs) in quantum physics applications. We show how CNNs efficiently approximate quantum wavefunctions in exponentially large Hilbert spaces using only linearly many parameters by connecting them to maximum entropy models and correlator product states. This analysis reveals how CNNs leverage quantum system symmetries and entanglement properties, leading to a new training algorithm that significantly reduces convergence time or number of parameters while maintaining accuracy. This work establishes a bridge between physics and machine learning, providing a template for analyzing other neural architectures and suggesting when they might succeed or fail in solving certain physics problems. Second, we develop a series of machine learning approaches for chemical spectroscopy analysis. The Characteristic Peak Extraction algorithm improves accuracy for identifying chemical components in complex mixtures, while our Characteristic Peak Similarity metric enables accurate matching between different types of spectroscopic measurements. These tools are being actively tested to detect harmful chemicals in environmental samples and human organs, including polycyclic aromatic hydrocarbons in soil and placenta samples. This work creates more accessible and efficient tools for environmental monitoring, addressing a longstanding challenge in the field of analytical chemistry where traditional chemical spectroscopy methods require extensive laboratory facilities, expert knowledge, and time-consuming analysis procedures. Finally, we advance medical diagnostics by creating interpretable deep learning models for ECG analysis that achieves high accuracy in detecting junctional ectopic tachycardia. Through explainable AI techniques, we systematically analyze how these networks make decisions by identifying key ECG features that align with clinical expertise, categorizing error patterns, and conducting root cause analysis of misclassifications. This mechanistic understanding not only validates the model's reasoning against clinical expertise but also provides insights for model improvement and clinical deployment. Beyond the immediate clinical impact, this contribution provides a framework for developing trustworthy AI systems in healthcare, where understanding decision-making processes is crucial for clinical adoption. Together, these contributions advance our understanding of AI systems while demonstrating their practical impact across multiple scientific disciplines. The frameworks and methodologies developed in this thesis provide a foundation for building more interpretable, efficient, and reliable AI systems.