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- W2595384346 abstract "Objective: state-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside. Methods: an artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series. The results are mapped into a physiologic state space for near real-time patient-state tracking. Results: the framework was validated on cardiac beat-to-beat dynamics processed with the multiscale entropy algorithm, and assessed using PhysioNet databases. We then applied our algorithm to predict 28-day mortality for sepsis patients, and found it had greater prognostic accuracy than standard clinical severity scores. Conclusion: we developed a novel framework to classify multiscale features of beat-to-beat dynamics, and performed an initial clinical validation to demonstrate that our approach generates a robust quantification of a patient's state, compatible with real-time bedside implementations. Significance: the framework generates meaningful and actionable patient-specific information, and could facilitate the dissemination of a new class of “always-on” diagnostic tools." @default.
- W2595384346 created "2017-03-23" @default.
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- W2595384346 date "2017-12-01" @default.
- W2595384346 modified "2023-10-16" @default.
- W2595384346 title "A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features" @default.
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- W2595384346 doi "https://doi.org/10.1109/tbme.2017.2684244" @default.
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