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Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
Cheng Ding, Zhicheng Guo, Cynthia Rudin +3
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics. We state the comp…
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges
Dennis Tang, Frank Willard, Ronan Tegerdine +8
In electroencephalogram (EEG) recordings, the presence of interictal epileptiform discharges (IEDs) serves as a critical biomarker for seizures or seizure-like events.Detecting IED…
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Srikar Katta, Harsh Parikh, Cynthia Rudin +1
Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these…
Fast and Interpretable Mortality Risk Scores for Critical Care Patients
Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova +4
Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (…
A Path to Simpler Models Starts With Noise
Lesia Semenova, Harry Chen, Ronald Parr +1
The Rashomon set is the set of models that perform approximately equally well on a given dataset, and the Rashomon ratio is the fraction of all models in a given hypothesis space t…
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
Chiyu Ma, Brandon Zhao, Chaofan Chen +1
We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-base…