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Desney Tan

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JBHI Journal 2022 Journal Article

A Comparison of Wearable Tonometry, Photoplethysmography, and Electrocardiography for Cuffless Measurement of Blood Pressure in an Ambulatory Setting

  • Rebecca Mieloszyk
  • Hope Twede
  • Jonathan Lester
  • Jeremiah Wander
  • Sumit Basu
  • Gabe Cohn
  • Greg Smith
  • Dan Morris

Objective: While non-invasive, cuffless blood pressure (BP) measurement has demonstrated relevancy in controlled environments, ambulatory measurement is important for hypertension diagnosis and control. We present both in-lab and ambulatory BP estimation results from a diverse cohort of participants. Methods: Participants (N=1125, aged 21-85, 49. 2% female, multiple hypertensive categories) had BP measured in-lab over a 24-hour period with a subset also receiving ambulatory measurements. Radial tonometry, photoplethysmography (PPG), electrocardiography (ECG), and accelerometry signals were collected simultaneously with auscultatory or oscillometric references for systolic (SBP) and diastolic blood pressure (DBP). Predictive models to estimate BP using a variety of sensor-based feature groups were evaluated against challenging baselines. Results: Despite limited availability, tonometry-derived features showed superior performance compared to other feature groups and baselines, yieldingprediction errors of 0. 32 $\pm$ 9. 8 mmHg SBP and 0. 54 $\pm$ 7. 7 mmHg DBP in-lab, and 0. 86 $\pm$ 8. 7 mmHg SBP and 0. 75 $\pm$ 5. 9 mmHg DBP for 24-hour averages. SBP error standard deviation (SD) was reduced in normotensive (in-lab: 8. 1 mmHg, 24-hr: 7. 2 mmHg) and younger (in-lab: 7. 8 mmHg, 24-hr: 6. 7 mmHg) subpopulations. SBP SD was further reduced 15–20% when constrained to the calibration posture alone. Conclusion: Performance for normotensive and younger participants was superior to the general population across all feature groups. Reference type, posture relative to calibration, and controlled vs. ambulatory setting all impacted BP errors. Significance: Results highlight the need for demographically diverse populations and challenging evaluation settings for BP estimation studies. We present the first public dataset of ambulatory tonometry and cuffless BP over a 24-hour period to aid in future cardiovascular research.

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AAAI Conference 2012 Conference Paper

Learning to Learn: Algorithmic Inspirations from Human Problem Solving

  • Ashish Kapoor
  • Bongshin Lee
  • Desney Tan
  • Eric Horvitz

We harness the ability of people to perceive and interact with visual patterns in order to enhance the performance of a machine learning method. We show how we can collect evidence about how people optimize the parameters of an ensemble classification system using a tool that provides a visualization of misclassification costs. Then, we use these observations about human attempts to minimize cost in order to extend the performance of a state-of-the-art ensemble classification system. The study highlights opportunities for learning from evidence collected about human problem solving to refine and extend automated learning and inference.

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AAAI Conference 2012 Conference Paper

Performance and Preferences: Interactive Refinement of Machine Learning Procedures

  • Ashish Kapoor
  • Bongshin Lee
  • Desney Tan
  • Eric Horvitz

Problem solving procedures have been typically aimed at achieving well defined goals or satisfying straightforward preferences. However, learners and solvers may often generate rich multiattribute results with procedures guided by sets of controls that define different dimensions of quality. We explore methods that enable people to explore and express preferences about the operation of classification models in supervised multiclass learning. We leverage a leave one out confusion matrix that provides users with views and real time controls of a model space. The approach allows people to consider in an interactive manner the global implications of local changes in decision boundaries. We focus on kernel classifiers and show the effectiveness of the methodology on a variety of tasks.

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AAAI Conference 2011 Conference Paper

Effective End-User Interaction with Machine Learning

  • Saleema Amershi
  • James Fogarty
  • Ashish Kapoor
  • Desney Tan

End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.

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