Study compares feature representations for ICU mortality prediction.
problem Improving prediction of ICU mortality.
method Compared linear, binary, and new 'hill' representations in a logistic regression model.
result Introduced 'hill' representation outperforms binary and linear representations.
Deep learning models outperform other methods on ICU patient data.
problem Benchmarking deep learning models on healthcare datasets.
method Used MIMIC-III dataset for mortality, length of stay, and ICD-9 code prediction tasks.
result Deep learning models consistently outperform other methods, especially with raw clinical data.
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.
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%
MIMIC-Extract transforms EHR data for reproducible healthcare machine learning.
problem Lack of accessible, standardized healthcare data for machine learning.
method Open-source pipeline for converting raw EHR data into usable dataframes.
result Demonstrates utility through benchmark tasks and baseline results.
Fairmetrics evaluates fairness in ML models for specific groups.
problem Ensuring models do not produce biased outcomes for specific groups.
method User-friendly R package for evaluating group-based fairness criteria.
result Rigorous evaluation of multiple fairness metrics.
Paper proposes mimic learning to share intrusion detection models without private data.
problem Difficulty in obtaining labelled training data for intrusion detection models due to privacy concerns.
method Use of mimic learning to transfer knowledge from a teacher model trained on private data to a student model.
result Student model mimics teacher model without access to private data.
LMUTs mimic neural Q functions, making RL models more interpretable.
problem Limited interpretability of neural Q functions in DRL models.
method Developed LMUTs for approximating neural network predictions in DRL models, using an on-line algorithm.
result LMUTs mimic Q functions better than five baseline methods, facilitating better understanding of learned knowledge.
Paper introduces a new test for conditional independence using weighted partial copulas.
problem Testing conditional independence between variables.
method The approach uses a weighted partial copula function and a bootstrap procedure to compute regions of rejection.
result The proposed test has competitive power compared to existing methods.
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.
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.
Wide Learning automates feature engineering in healthcare datasets.
problem Time-consuming and expert-demanding feature engineering in ML.
method Proposes a Wide Learning architecture for automated feature recommendation.
result Wide Learning reaches 94.38% accuracy in PhysioNet Challenge 2016 dataset.
Generative model combines deep neural networks and ensemble methods for patient trajectory classification.
problem Classifying patient trajectories with scarce longitudinal data.
method Tensor factorization using deep recurrent neural networks and ensemble strategies.
result Achieved AUC over 0.85 for in-hospital mortality prediction.
Deep learning predicts patient trajectories in open Mimic-III dataset.
problem Predicting future medical conditions from patient history.
method Two parallel bi-directional Minimal Gated Recurrent Unit networks trained on Mimic-III dataset.
result Significant improvements in automated medical prognosis measured by Recall@k.
New method offsets DML's error-compounding issue and provides more stable causal parameter estimates.
problem Estimating ATE from observational data with robustness and stability.
method Robust Causal Learning (RCL) method to offset DML's deficiencies.
result RCL estimators are more stable and perform better than DML and traditional estimators.
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.
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.
Study shows mimicry attacks fail even with ASV-assisted target selection.
problem Can mimicry attacks be successful with ASV-assisted target selection?
method Used ASV to select target speakers for mimicry attacks and tested with x-vector system.
result Mimics did not succeed in spoofing the x-vector system, but relative ordering of targets was consistent.
Bayesian emulator tackles models with discontinuities efficiently.
problem Uncertainty quantification of models with multiple discontinuities.
method TENSE framework, designed correlation structures, single emulator object.
result Efficient emulation of complex models with multiple discontinuities.
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.
A meta-algorithm tests conditional independence using generative and classification methods.
problem Determining conditional independence between variables.
method Mimic and Classify: a two-step process involving generative and classification models.
result The meta-algorithm effectively distinguishes between joint and conditional independence distributions.
A new method embeds distributions in a common space for optimal transport comparison.
problem Comparing distributions in different metric spaces.
method Sub-embedding robust Wasserstein (SERW) distance.
result SERW mimics GW distance properties and provides a cost relation.
Model predicts patient outcomes from EHR data by limiting feature interactions.
problem Predicting patient outcomes from noisy, missing EHR data.
method Recurrent neural network with feature interaction restriction.
result Improvement in AU-ROC for mortality prediction (1.1% improvement, p<0.01).
Paper proposes an adversarial sampling method for efficient extreme classification.
problem Training classifiers over many classes is computationally expensive.
method Adversarial sampling to draw negative samples from an adversarial model.
result Significantly reduces training time by an order of magnitude.
New model for learning from noisy human comparisons, improving search efficiency.
problem Designing efficient algorithms for content search with noisy human feedback.
method Introducing a weak oracle model for comparison-based queries and developing WORCS-I and WORCS-II algorithms.
result Provable algorithms locating target objects with close to entropy of target distribution.
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.
The paper optimizes dividend strategies for companies with assets and liabilities under solvency constraints.
problem Maximizing dividends while adhering to solvency requirements in the face of correlated asset and liability movements.
method Developed verification lemmas to show optimal barrier dividend strategies in two cases: with and without shareholder funding.
result Optimal dividend strategies are barrier-type, derived in closed form and illustrated.
Mime algorithm improves federated learning by adapting centralized methods.
problem Mitigating client drift in federated learning.
method Combines control variates and server-level statistics to adapt centralized algorithms to federated learning.
result Mime outperforms any centralized method in federated learning.
Behavior cloning can achieve horizon-independent sample complexity in offline imitation learning.
problem Sample complexity in imitation learning increases with problem horizon.
method New analysis of behavior cloning with logarithmic loss.
result Behavior cloning can achieve linear dependence on horizon in offline IL under dense rewards.
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.
Investors mimic others' portfolios to reduce risk, leading to mutual funds that optimize this behavior.
problem Reduction of estimation risk in portfolio choice for mean-variance investors.
method Introduces penalties for deviations from group compositions, derived optimal portfolio weights.
result Explicit analytical solution for optimal portfolio weights in mutual funds.
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.
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.
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
DeepMoTIon mimics human navigation in crowds for safe robot movement.
problem Safe navigation of robots in human environments.
method Trains a neural network on pedestrian data to predict human velocity and navigate safely.
result DeepMoTIon outperforms other methods in human imitation and safety compliance.
Develops Hodge theory for foliations using perturbed Laplacians.
problem Creating a Hodge theory for foliations.
method Mimicking Witten's approach to Morse theory with perturbations of the Laplacian.
result Establishes a Hodge theory for tangential cohomology of foliations.
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.
New method steals deep neural network knowledge using unlabeled data.
problem Stealing knowledge from protected deep neural networks.
method Generating composite images to mimic mentor neural networks.
result Student network successfully mimics mentor network and steals knowledge.
The study creates benchmarks for clinical time series data to evaluate machine learning models.
problem Lack of publicly available benchmark data sets for healthcare research.
method Proposed four clinical prediction benchmarks using MIMIC-III data, evaluated various deep supervision and multitask training methods.
result Demonstrated the effectiveness of deep supervision, multitask training, and data-specific architectural modifications on neural models.
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.
This work compresses reinforcement learning models for Atari games, improving localization.
problem Expensive deep neural networks in reinforcement learning.
method Model compression, global max-pooling, Actor-Mimic, weakly supervised localization.
result Compression reduces model size to 3% of original, enabling object localization.
Deep convolutional nets are essential for accurate learning on CIFAR-10.
problem Training shallow models to mimic deep convolutional nets on CIFAR-10.
method Used distillation to train shallow feed-forward nets on CIFAR-10, demonstrating the necessity of multiple convolutional layers.
result Accurate models on CIFAR-10 require multiple convolutional layers, even when trained with distillation.
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.
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.
Clinical models trained on EHRs degrade in performance over time due to data drift.
problem Model performance degradation over time in clinical settings.
method Accessed year of care for each record in MIMIC, aggregated features into clinical concepts, and tested mitigation strategies.
result State-of-the-art models show significant performance drops when tested on future data compared to historical data.
Graph Prototypical Networks improve few-shot node classification on attributed networks.
problem Few-shot node classification in attributed networks with limited labeled instances.
method Graph Prototypical Networks (GPN) using meta-learning to extract meta-knowledge and identify informative labeled instances.
result GPN achieves superior performance in few-shot node classification.
The paper tackles ICU discharge strategies by evaluating optimal stopping scenarios.
problem Optimal stopping in ICU discharge with complex causal inference challenges.
method Generalized g-formula implementation and application to MIMIC-IV dataset.
result Improved ICU discharge strategies demonstrated with the g-formula approach.
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.