688 citations · 1.4k across the 8 of their papers we have counts for
9 papers
Effective Representations of Clinical Notes
Sebastien Dubois, Nathanael Romano, David C. Kale +2
Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high…
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale +2
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clini…
Intra-day Activity Better Predicts Chronic Conditions
Tom Quisel, David C. Kale, Luca Foschini
In this work we investigate intra-day patterns of activity on a population of 7,261 users of mobile health wearable devices and apps. We show that: (1) using intra-day step and sle…
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
Ryan J. Gallagher, Kyle Reing, David Kale +1
While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyper…
Toward Interpretable Topic Discovery via Anchored Correlation Explanation
Kyle Reing, David C. Kale, Greg Ver Steeg +1
Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts. Unfortunately, encoding experts' knowled…
Modeling Missing Data in Clinical Time Series with RNNs
Zachary C. Lipton, David C. Kale, Randall Wetzel
We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected…