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Found 35 papers, 18 papers with code
Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features.
Self-Consistent Conformal Prediction
Conformal prediction helps decision-makers quantify uncertainty in point predictions of outcomes, allowing for better risk management for actions.
Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data
Given a web-based calibration set, we apply conformal prediction with a novel conformity score that accounts for potential errors in retrieved web data.
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
We then use a large language model to paraphrase prompt templates that convey the specific tasks to be conducted on each image, and through this process, we create a multi-modal and multi-task training dataset comprising input and output images along with annotated instructions.
Conformalized Unconditional Quantile Regression
We develop a predictive inference procedure that combines conformal prediction (CP) with unconditional quantile regression (QR) -- a commonly used tool in econometrics that involves regressing the recentered influence function (RIF) of the quantile functional over input covariates.
Conformal Time-series Forecasting
Current approaches for multi-horizon time series forecasting using recurrent neural networks (RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical application domains where an uncertainty estimate is also required.
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
In this paper, we introduce a 3-dimensional evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion.
Learning Matching Representations for Individualized Organ Transplantation Allocation
Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients.
Estimating Structural Target Functions using Machine Learning and Influence Functions
Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources.
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging.
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
In this paper, we develop an approximate Bayesian inference scheme based on posterior regularisation, wherein unlabelled target data are used as "pseudo-labels" of model confidence that are used to regularise the model's loss on labelled source data.
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data.
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context -- we treat each country as a distinct data point, and exploit variations of policies across countries to learn country-specific policy effects.
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
Attentive State-Space Modeling of Disease Progression
Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics.
Demystifying Black-box Models with Symbolic Metamodels
A symbolic metamodel is a model of a model, i. e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation.
The Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
To address this question, we develop the discriminative jackknife (DJ), a formal inference procedure that constructs predictive confidence intervals for a wide range of deep learning models, is easy to implement, and provides rigorous theoretical guarantees on (1) and (2).
Lifelong Bayesian Optimization
In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time.
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
The estimation of treatment effects is a pervasive problem in medicine.
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability.
Ranked #1 on
Disease Trajectory Forecasting
on UK CF trust
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
AUTOPROGNOSIS optimizes ensembles of pipeline configurations efficiently using a novel batched Bayesian optimization (BO) algorithm that learns a low-dimensional decomposition of the pipelines high-dimensional hyperparameter space in concurrence with the BO procedure.
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data, this is a central problem in various application domains, including healthcare, social sciences, and online advertising.
Deep Counterfactual Networks with Propensity-Dropout
The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers.
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model
We report the development and validation of a data-driven real-time risk score that provides timely assessments for the clinical acuity of ward patients based on their temporal lab tests and vital signs, which allows for timely intensive care unit (ICU) admissions.
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis
Our model captures "informatively sampled" patient episodes: the clinicians' decisions on when to observe a hospitalized patient's vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient's latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process.
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS).
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare.
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU).
Personalized Donor-Recipient Matching for Organ Transplantation
Organ transplants can improve the life expectancy and quality of life for the recipient but carries the risk of serious post-operative complications, such as septic shock and organ rejection.
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes
Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit (ICU) admissions for clinically deteriorating patients.
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
We develop a Bayesian model for decision-making under time pressure with endogenous information acquisition.
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs.
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening
A cluster of patients is a set of patients with similar features (e. g. age, breast density, family history, etc.
