Search Results for author:
Found 10 papers, 4 papers with code
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data
However, this problem is challenging due to the spatial heterogeneity of the environment and the sparsity of accidents in space and time.
Optimizing Warfarin Dosing using Deep Reinforcement Learning
We tested the robustness of our dosing protocol on a second PK/PD model and showed that its performance is comparable to the set of baseline protocols.
A predictive model for kidney transplant graft survival using machine learning
A machine learning method may provide improved prediction of transplant outcomes and help decision-making.
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US.
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume.
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label.
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better.
Realistic risk-mitigating recommendations via inverse classification
Use of such past probabilities ties historical behavior to the present, allowing for more information to be taken into account when making initial probability estimates and subsequently performing inverse classification.
Generalized Inverse Classification
To solve such a problem, we propose three real-valued heuristic-based methods and two sensitivity analysis-based comparison methods, each of which is evaluated on two freely available real-world datasets.
A budget-constrained inverse classification framework for smooth classifiers
In this paper we propose a general framework and method that overcomes these and other limitations.
