2,023 research outputs found
An outreach dental project to a remote drug rehabilitation centre
published_or_final_versio
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the
relation of collaborative similarity. Nevertheless, there exist multiple
relations between items in real-world scenarios. Distinct from the
collaborative similarity that implies co-interact patterns from the user
perspective, these relations reveal fine-grained knowledge on items from
different perspectives of meta-data, functionality, etc. However, how to
incorporate multiple item relations is less explored in recommendation
research. In this work, we propose Relational Collaborative Filtering (RCF), a
general framework to exploit multiple relations between items in recommender
system. We find that both the relation type and the relation value are crucial
in inferring user preference. To this end, we develop a two-level hierarchical
attention mechanism to model user preference. The first-level attention
discriminates which types of relations are more important, and the second-level
attention considers the specific relation values to estimate the contribution
of a historical item in recommending the target item. To make the item
embeddings be reflective of the relational structure between items, we further
formulate a task to preserve the item relations, and jointly train it with the
recommendation task of preference modeling. Empirical results on two real
datasets demonstrate the strong performance of RCF. Furthermore, we also
conduct qualitative analyses to show the benefits of explanations brought by
the modeling of multiple item relations
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
Predicting the future health information of patients from the historical
Electronic Health Records (EHR) is a core research task in the development of
personalized healthcare. Patient EHR data consist of sequences of visits over
time, where each visit contains multiple medical codes, including diagnosis,
medication, and procedure codes. The most important challenges for this task
are to model the temporality and high dimensionality of sequential EHR data and
to interpret the prediction results. Existing work solves this problem by
employing recurrent neural networks (RNNs) to model EHR data and utilizing
simple attention mechanism to interpret the results. However, RNN-based
approaches suffer from the problem that the performance of RNNs drops when the
length of sequences is large, and the relationships between subsequent visits
are ignored by current RNN-based approaches. To address these issues, we
propose {\sf Dipole}, an end-to-end, simple and robust model for predicting
patients' future health information. Dipole employs bidirectional recurrent
neural networks to remember all the information of both the past visits and the
future visits, and it introduces three attention mechanisms to measure the
relationships of different visits for the prediction. With the attention
mechanisms, Dipole can interpret the prediction results effectively. Dipole
also allows us to interpret the learned medical code representations which are
confirmed positively by medical experts. Experimental results on two real world
EHR datasets show that the proposed Dipole can significantly improve the
prediction accuracy compared with the state-of-the-art diagnosis prediction
approaches and provide clinically meaningful interpretation
Geometric texture indicators for safety on AC pavements with 1mm 3D laser texture data
AbstractSurface texture and friction are two primary characteristics for pavement safety evaluation. Understanding their relationship is critical to reduce potential traffic crashes especially at wet conditions. Texture data obtained from existing systems are restricted on either a small portion on pavement surface or one line-of-sight profile, and the currently used texture indicators, such as Mean Profile Depth (MPD), and Mean Texture Depth (MTD) only reveal partial aspects of texture property. With the emerging 3D laser imaging technology, acquiring full-lane 3D pavement surface data at sub-millimeter resolution and at highway speeds has been made possible via the newly developed PaveVision3D Ultra data collection system. In this study using 1mm 3D data collected from PaveVision3D Ultra, four types of texture indicators (amplitude, spacing, hybrid, and functional parameters) are calculated to represent various texture properties for pavement friction estimation. The relationships among those texture indicators and pavement friction are examined. MPD and Skewness – two height texture parameters, Texture Aspect Ratio (TAR) – a spatial parameter, and Surface Bearing Index (SBI) – a functional parameter are found to be the four most contributing parameters for pavement friction prediction. Finally a multivariate regression model is developed based on residual plot analysis methods to estimate pavement friction with the R-squared value of 0.95. This study would be beneficial in the continuous measurement and evaluation of pavement safety for project- and network-level pavement surveys
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