SCH: INT: Data-In-Motion Prediction and Assessment of Acute Respiratory Distress Syndrome

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The goal of this project is to develop new computational approaches for synthesizing streams of real-time electronic health data for health monitoring and early disease detection. The team will utilize these technologies to address the problem of monitoring patients with lung disease to identify Acute Respiratory Distress Syndrome (ARDS). ARDS is an ideal problem, because it is frequently missed by clinicians with wide-ranging consequences to patients. The project will develop two emerging concepts in machine learning, learning with privileged information and uncertainty, both of which have relevance in healthcare. It will also develop new approaches for integrating different data types, including waveforms (e.g. electrocardiograms), images (e.g. chest x-rays), and numeric data (e.g. laboratory results) to more effectively assist clinicians in medical diagnosis. The project will also establish a multidisciplinary learning platform for computer-assisted health decision support systems to prepare students, postdocs, and early career clinical scientists in precision medicine using highly effective mathematical tools. It will also include participation of groups underrepresented in STEM through recruiting new students, integrating the research training in a highly diverse laboratory, and exploring multidisciplinary, research applied to real-world problems in biomedical science and engineering.This project proposes to extend machine learning techniques to a) incorporate privileged information in algorithm training (data routinely available in retrospective databases but not live clinical environments) and b) to account of uncertainty in training labels (because even medical experts have uncertainty in medical diagnosis). These approaches will lead to more accurate and efficient algorithms for the detection of medical conditions where diagnostic uncertainty is common. The project will develop effective signal processing techniques to identify perturbations associated with respiratory insufficiency and ARDS development in time series data. The project will also develop image processing techniques that extract clinically relevant features from digital chest radiographs of the lungs that could improve the accuracy of real-time clinical diagnosis in many respiratory that are frequently difficulty to distinguish among. Finally, this project will integrate these novel methodologies to develop a clinical decision support system.