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Found 30 papers, 6 papers with code
Early detection of disease outbreaks and non-outbreaks using incidence data
In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur.
An early warning indicator trained on stochastic disease-spreading models with different noises
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies.
Copula-Based Deep Survival Models for Dependent Censoring
A survival dataset describes a set of instances (e. g. patients) and provides, for each, either the time until an event (e. g. death), or the censoring time (e. g. when lost to follow-up - which is a lower bound on the time until the event).
An Effective Meaningful Way to Evaluate Survival Models
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects.
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
Modeling and Forecasting COVID-19 Cases using Latent Subpopulations
Here, we propose two new methods to model the number of people infected with COVID-19 over time, each as a linear combination of latent sub-populations -- i. e., when we do not know which person is in which sub-population, and the only available observations are the aggregates across all sub-populations.
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health.
ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions.
Domain-shift adaptation via linear transformations
A predictor, $f_A : X \to Y$, learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different.
Variational Auto-Encoder Architectures that Excel at Causal Inference
In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects.
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions.
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
Shared Space Transfer Learning for analyzing multi-site fMRI data
The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space.
Learning Disentangled Representations for CounterFactual Regression
We consider the challenge of estimating treatment effects from observational data; and point out that, in general, only some factors based on the observed covariates X contribute to selection of the treatment T, and only some to determining the outcomes Y.
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction.
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
We develop an efficient optimization strategy for this extremely high-dimensional sparse problem, by reducing the number of parameters using a greedy algorithm designed specifically for the problem.
Domain Aggregation Networks for Multi-Source Domain Adaptation
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e. g., recognizing characters of a new font using a set of different fonts.
Simultaneous Prediction Intervals for Patient-Specific Survival Curves
In this paper, we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results.
The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetes
If we can accurately predict a patient's future BG values from his/her current features (e. g., predicting today's lunch BG value given today's diabetes diary entry for breakfast, including insulin injections, and perhaps earlier entries), then it is relatively easy to produce an effective regimen.
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle.
Effective Ways to Build and Evaluate Individual Survival Distributions
This paper first motivates such "individual survival distribution" (ISD) models, and explains how they differ from standard models.
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.
Stochastic Neural Networks with Monotonic Activation Functions
We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise.
Boolean Matrix Factorization and Noisy Completion via Message Passing
Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness.
Revisiting Algebra and Complexity of Inference in Graphical Models
This paper studies the form and complexity of inference in graphical models using the abstraction offered by algebraic structures.
Accurate, fully-automated NMR spectral profiling for metabolomics
This information can be extracted from a biofluid's NMR spectrum.
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning
The cutting plane method is an augmentative constrained optimization procedure that is often used with continuous-domain optimization techniques such as linear and convex programs.
Training Restricted Boltzmann Machine by Perturbation
During the learning, to produce a sample from the current model, we start from a training data and descend in the energy landscape of the "perturbed model", for a fixed number of steps, or until a local optima is reached.
Perturbed Message Passing for Constraint Satisfaction Problems
We introduce an efficient message passing scheme for solving Constraint Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and directly produce a single satisfying assignment.
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors
An accurate model of patient survival time can help in the treatment and care of cancer patients.
