Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data…
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168,742 papers · 148 categories
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48 results for “mimic learning”
Paper proposes sharing models instead of data for smart health predictions.
problem Sharing sensitive medical data is legally restricted and challenging.
method Train a teacher model on sensitive data, then transfer its knowledge to a student model without accessing original data.
result Student model mimics teacher model performance in making accurate predictions.
Study re-evaluates MIMIC-III codes, finding many are under-coded.
problem Validity of MIMIC-III clinical codes is questionable.
method Open-source, reproducible methodology for assessing codes.
result Most frequently assigned codes are under-coded up to 35%
Relational Mimic improves visual imitation learning from video demonstrations.
problem Improving robustness and sample efficiency in visual imitation learning.
method Combines generative adversarial networks and relational learning.
result Improves agent performance in challenging locomotion tasks.
Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the…
GRU-D detects age-specific missing patterns in vital signs.
problem Temporal missingness in clinical time series data.
method Gated recurrent unit with decay mechanisms (GRU-D) trained on MIMIC-IV vital signs.
result GRU-D achieves AUROC 0.780 and AUPRC 0.810 on bootstrapped data.
Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to …
Study evaluates federated learning with differential privacy on MIMIC-III, improving model performance with careful parameter tuning.
problem Training machine learning models on privacy-sensitive data sets locked in healthcare facilities.
method Extensive evaluation of federated and differential privacy techniques on MIMIC-III dataset, analyzing various parameters.
result Careful parameter tuning is crucial for federated learning with differential privacy, especially for data distribution and communication strategies.
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learningstat.ML
FMI uses matching to mimic interventions for causal feature learning.
problem Challenges in causal discovery from observational data.
method Feature Matching Intervention (FMI) using matching to emulate perfect interventions.
result FMI outperforms in identifying causal features from observational data.
Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendationscs.LG
New model mimics neural next item recommendation using Hankel matrices.
problem Next item recommendation efficiency and structural knowledge capture.
method Tensor factorization with Hankel matrix representation.
result Model performs competitively with neural networks but is simpler.
Given independent samples generated from the joint distribution , we study the problem of Conditional Independence (CI-Testing), i.e., whether the joint equals the CI distribution $p^{CI}(\mathbf{x},\mathbf{y},\mathbf{z})= p(\mathbf{z}) p(\mathbf{y}|\mathbf{z})p(\mathbf{x}|\mathbf{z…
Scoping review and benchmarking of synthetic EHR data generation methods.
problem Creating realistic synthetic electronic health records for research and training.
method Conducted a scoping review and benchmarked seven methods on open-source EHR datasets.
result GAN-based methods excel in fidelity and utility, while rule-based methods excel in privacy protection.
Yes, they do. This paper provides the first empirical demonstration that deep convolutional models really need to be both deep and convolutional, even when trained with methods such as distillation that allow small or shallow models of high accuracy to be trained. Although previous research showed that shallow feed-for…
Paper simplifies complex sports analytics models for better understanding.
problem Difficulty in interpreting deep learning models for sports analytics.
method Developed a linear model tree that mimics deep learning models and explains its knowledge.
result The linear model tree achieves high accuracy and provides actionable insights.
Study develops an interpretable model for early mortality prediction in elderly MODS patients.
problem High mortality risk in elderly patients with MODS, unsatisfactory current scoring systems.
method Used eXtreme Gradient Boosting with SHapley Additive exPlanations on MIMIC-III, eICU-CRD, and PLAGH-S datasets.
result Interpretable model outperforms baseline models and clinical scores in predicting hospital mortality.
Simulates sepsis treatment decisions using a world model approach.
problem Predicting optimal sepsis treatment actions based on noisy EHR data.
method Uses a Variational Auto-Encoder and Mixture Density Network (MDN-RNN) to model sepsis patient trajectories.
result Simulator learns from MIMIC dataset to predict patient states.
Neural networks mimic algorithms to solve complex problems.
problem Current machine learning methods struggle with generalisation and efficiency.
method Representing algorithms in a continuous space and adapting them to real-world problems.
result Neural networks can execute classical algorithms more efficiently.
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with…
Deep learning predicts ICU mortality with enhanced interpretability.
problem Improving mortality prediction accuracy and clinician trust in AI.
method Trained a deep learning model on MIMIC-III to interpret nursing notes.
result Model reaches ROC of 0.8629, outperforming SAPS-II.
We consider technology-assisted mimicry attacks in the context of automatic speaker verification (ASV). We use ASV itself to select targeted speakers to be attacked by human-based mimicry. We recorded 6 naive mimics for whom we select target celebrities from VoxCeleb1 and VoxCeleb2 corpora (7,365 potential targets) usi…
Imitation Learning is a sequential task where the learner tries to mimic an expert's action in order to achieve the best performance. Several algorithms have been proposed recently for this task. In this project, we aim at proposing a wide review of these algorithms, presenting their main features and comparing them on…
MedCAT extracts valuable medical information from unstructured text.
problem Extracting structured information from unstructured biomedical documents.
method Unsupervised machine learning for disambiguation of entities.
result Improved entity detection and linking compared to existing tools.
Framework improves clinical timeline reconstruction from text and tables.
problem Temporal precision and event timing in clinical narratives and EHRs.
method Retrieval-augmented multimodal alignment framework.
result Consistently improves absolute timestamp accuracy and temporal concordance.
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do…
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly c…
Reinforcement learning mimics expert behavior.
problem Learning from expert demonstrations in reinforcement learning.
method Reduction to reinforcement learning with a stationary reward.
result Expert reward can be recovered and imitation learning is bounded.
Deep learning predicts heart failure readmission from clinical notes.
problem Predicting and preventing heart failure readmission.
method Convolutional Neural Networks (CNN) trained on clinical notes.
result Deep learning models outperform traditional machine learning methods in readmission prediction.
A concise review of recent few-shot meta-learning methods.
problem Mimicking human fast adaptation to new concepts based on prior knowledge.
method Categorized into four branches based on technical characteristics.
result Current challenges and future prospects identified.
New method improves compatibility of risk stratification models without sacrificing accuracy.
problem Compatibility issues arise when updating clinical machine learning models.
method Proposes rank-based compatibility measure and new loss function.
result Increased compatibility of models by 0.019 with no loss in discriminative performance.
There has been significant recent interest towards achieving highly efficient deep neural network architectures. A promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over succe…
Study benchmarks uncertainty quantification in chest X-ray classification.
problem Reliable uncertainty quantification for medical AI models.
method Evaluation of 13 uncertainty quantification methods on MIMIC-CXR-JPG dataset.
result Insights into effectiveness and disentanglement of epistemic and aleatoric uncertainties.
We present a method to develop a Hodge theory for tangential cohomology of foliations by mimicing Witten's approach to ordinary Morse theory by perturbations of the Laplacian
New learning algorithm mimics biological neural networks.
problem Biologically implausible backpropagation for directed neural networks.
method Introduces new neuronal dynamics and learning rule for arbitrary architectures, sparsity-inducing pruning method, and dynamical-systems characterization.
result Prunes irrelevant connections and improves learning efficiency.
We study multiple rule-based and machine learning (ML) models for sepsis detection. We report the first neural network detection and prediction results on three categories of sepsis. We have used the retrospective Medical Information Mart for Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) p…
is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. It is inspired by and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that…
We construct a canonically defined affine connection in sub-Riemannian contact geometry. Our method mimics that of the Levi-Civita connection in Riemannian geometry. We compare it with the Tanaka-Webster connection in the three-dimensional case.
Learning with Physical Constraintsphysics.flu-dyn
Three physics-constrained regression exercises for image velocimetry and turbulence modeling.
problem Image velocimetry and turbulence modeling challenges.
method Physics-constrained regression exercises implemented as toy problems.
result Python codes provided for all exercises.
Two different spacetimes can mimic each other's boundary measurements.
problem Lorentzian Calderón problem
method Counterexample construction
result Non-isometric spacetimes can have identical boundary measurements
A new distillation method transfers channel information from teacher to student.
problem Transfer knowledge from teacher to student with fewer parameters and calculations.
method Channel Distillation (CD) and Guided Knowledge Distillation (GKD) with loss decay.
result Achieved 27.68% top-1 error on ImageNet with ResNet18, outperforming state-of-the-art methods.
As state-of-the-art deep neural networks are deployed at the core of more advanced Al-based products and services, the incentive for copying them (i.e., their intellectual properties) by rival adversaries is expected to increase considerably over time. The best way to extract or steal knowledge from such networks is by…
Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unitcs.LG
New model predicts ICU patient stays more accurately.
problem Efficient ICU bed allocation under resource constraints.
method Temporal Pointwise Convolutional Networks (TPC) combining temporal and pointwise convolutions.
result Significant performance improvements over LSTM and Transformer models.
In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narrativ…
Paper explores limits of imitation learning in MDPs, setting new suboptimality bounds.
problem Understanding the statistical limits of imitation learning in MDPs.
method Analyzes minimax statistical limits in two settings: pre-interaction and interaction.
result Establishes suboptimality bounds for imitation learning in MDPs, showing improvements with knowledge of transition.
This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervi…
This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values of which are initialized randomly. The evaluation function of the program is evolved by learning from databases of (human) grandmaster games…
PIN models feature interactions using a neural network that mimics decision trees.
problem Modeling feature interactions in tabular data for predictive modeling.
method Tree-like Pairwise Interaction Network (PIN) architecture that captures pairwise feature interactions through a shared feed-forward neural network.
result PIN outperforms traditional and modern neural networks benchmarks in predictive accuracy.
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, where the paradigm of maximizing sharpness subject to calibration is popular, has addressed this problem by using scoring rules beyond MLE, s…
The paper develops a faster surrogate model for simulators using hybrid methods.
problem The need for faster validation of automotive technologies using simulators.
method Testing classical methods and building hybrid models combining them.
result A hybrid surrogate model outperforms classical methods in multivariate time series prediction.