15 citations · 26 across the 7 of their papers we have counts for
5 papers · 1 filter
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has t…
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Zhi Chen, Sarah Tan, Urszula Chajewska +2
Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large…
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
Benjamin Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs +1
Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities…
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and ba…
Extracting Expert's Goals by What-if Interpretable Modeling
Chun-Hao Chang, George Alexandru Adam, Rich Caruana +1
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify…