
Patient Outcome and Zeroshot Diagnosis Prediction with Hypernetworkguided Multitask Learning
Multitask deep learning has been applied to patient outcome prediction f...
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Multitask Balanced and Recalibrated Network for Medical Code Prediction
Human coders assign standardized medical codes to clinical documents gen...
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Medical SANSformers: Training selfsupervised transformers without attention for Electronic Medical Records
We leverage deep sequential models to tackle the problem of predicting h...
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Deep Learning for Depression Recognition with Audiovisual Cues: A Review
With the acceleration of the pace of work and life, people have to face ...
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Multitask Recalibrated Aggregation Network for Medical Code Prediction
Medical coding translates professionally written medical reports into st...
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Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study
Unsupervised pretraining is an integral part of many natural language pr...
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A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models
Using deep latent variable models in causal inference has attracted cons...
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Medical Code Assignment with Gated Convolution and NoteCode Interaction
Medical code assignment from clinical text is a fundamental task in clin...
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Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
Medical code assignment, which predicts medical codes from clinical text...
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Informative Gaussian Scale Mixture Priors for Bayesian Neural Networks
Encoding domain knowledge into the prior over the highdimensional weigh...
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge ...
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Batch simulations and uncertainty quantification in Gaussian process surrogatebased approximate Bayesian computation
Surrogate models such as Gaussian processes (GP) have been proposed to a...
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Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Surrogate models such as Gaussian processes (GP) have been proposed to a...
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Errorsinvariables Modeling of Personalized TreatmentResponse Trajectories
Estimating the effect of a treatment on a given outcome, conditioned on ...
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Parallel Gaussian process surrogate method to accelerate likelihoodfree inference
We consider Bayesian inference when only a limited number of noisy logl...
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Recovering Pairwise Interactions Using Neural Networks
Recovering pairwise interactions, i.e. pairs of input features whose joi...
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A Bayesian model of acquisition and clearance of bacterial colonization
Bacterial populations that colonize a host play important roles in host ...
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Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Predicting the efficacy of a drug for a given individual, using highdim...
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Efficient acquisition rules for modelbased approximate Bayesian computation
Approximate Bayesian computation (ABC) is a method for Bayesian inferenc...
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Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
Providing accurate predictions is challenging for machine learning algor...
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Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Approximate Bayesian computation (ABC) can be used for model fitting whe...
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Multiple Output Regression with Latent Noise
In highdimensional data, structured noise caused by observed and unobse...
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Pekka Marttinen
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