18 citations · 22 across the 6 of their papers we have counts for
6 papers
A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi +4
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their…
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation
Ahmad Wisnu Mulyadi, Heung-Il Suk
Extensive adoption of electronic health records (EHRs) offers opportunities for their use in various downstream clinical analyses. To accomplish this purpose, enriching an EHR coho…
XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning
Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh +2
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristic…
Deep Efficient Continuous Manifold Learning for Time Series Modeling
Seungwoo Jeong, Wonjun Ko, Ahmad Wisnu Mulyadi +1
Modeling non-Euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields. Particularly, a symmetric positive defi…
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction
Eunji Jun, Ahmad Wisnu Mulyadi, Jaehun Choi +1
Electronic health records (EHR) are characterized as non-stationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inher…
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
Ahmad Wisnu Mulyadi, Eunji Jun, Heung-Il Suk
Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality, which become major obstacles in drawin…