The paper improves decision tree stability for health care applications.
problem Stability of decision trees in health care applications.
method Introducing a new distance metric to determine tree stability and proposing a novel training methodology.
result On average, a 4.6% decrease in predictive power yields a 38% increase in model stability.
New fair regression methods improve health care spending predictions for undercompensated groups.
problem Current risk adjustment formulas underpredict spending for specific health groups, leading to unfair compensation.
method Developed new fair regression methods by integrating fairness considerations into the objective function.
result New methods lead to significant improvements in fairness (98%) with minimal impact on overall fit (4%).
A self-attention model improves fraud detection in health care claims.
problem Fraud detection in health care claims with hierarchical data structures.
method Piecewise feed forward neural networks and self-attention neural networks.
result Self-attention model outperforms other models on a dataset of two million health care claims.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
problem Impact of health care capacity on COVID-19 growth in Senegal.
method Compartmental model with logistic growth health care capacity, machine learning projection.
result Condition to avoid overwhelming health care system provided.
Study on privacy-preserving health care models that sacrifice accuracy for data protection.
problem Privacy-preserving models in health care neglect data from the tails, reducing accuracy for small groups.
method Used state-of-the-art differentially private learning methods for clinical prediction tasks.
result Privacy-preserving models in health care exhibit steep tradeoffs between privacy and utility, and disproportionately influence large demographic groups.
Bayesian nonparametric models identify subgroups in hospital stay data.
problem Complex inpatient utilization data with zero inflation, over-dispersion, and skewness.
method Fully Bayesian mixture model with nonparametric clustering.
result Distinct subgroups of patients with lung cancer identified, differing in hospital days, covariates, and covariate relationships.
Improved TF-IDF for word relevance in health-care social media documents.
problem Determining word relevance in informal documents.
method Semantic Sensitive TF-IDF (STF-IDF) method.
result Decreased TF-IDF mean error rate by 50% to 13.7%.
Paper introduces score embedding for Twitter sentiment analysis of health care issues.
problem Analyzing public opinion on health care plans using social media.
method Score embedding, a neural network model for word representations.
result Score embedding effectively captures sentiment and outperforms existing methods.
LHIEM model predicts health, income, and employment over years.
problem Lack of path dependency in health policy simulations.
method Discrete-time microsimulation with Markov chain modules.
result Validates health care financing proposal through detailed modeling.
Online PaLD extends PaLD for semi-supervised online applications.
problem Scalability of unsupervised clustering algorithms for large datasets.
method Adapted partitioned local depth algorithm for online semi-supervised prediction.
result Online PaLD extends cohesion network to new data points efficiently.
Refines diagnostic prediction using causal relationships.
problem Improving accuracy of pain diagnostics prediction.
method Two approaches: 1) Inference of causal relationships, 2) Post-processing refinement.
result Potential for improving pain diagnostics prediction accuracy.
Icebreaker tackles active information acquisition with low data, improving model performance.
problem Deploying machine learning models with minimal training data, where each feature acquisition is costly.
method Proposes a full Bayesian Deep Latent Gaussian Model (BELGAM) with novel inference methods.
result Significantly outperforms previous models in small data settings, improving test time performance.
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
EDDI efficiently finds high-value information with minimal cost.
problem Balancing decision quality with acquisition cost in dynamic information acquisition.
method EDDI uses a partial variational autoencoder (Partial VAE) and an acquisition function to maximize expected information gain.
result Cost reduction and improved decision quality in benchmarks and real-world applications.
INNs produce interval-valued uncertainty scores for DNNs.
problem Uncertainty quantification in deep neural networks.
method Data-driven interval propagating network using interval arithmetic.
result INNs produce sensible lower and upper bounds for prediction error.
FedMD combines model distillation for federated learning with private models.
problem Federated learning's limitation in handling private, independently designed models.
method Transfer learning and knowledge distillation.
result Models achieve 20% average accuracy gain through collaboration.
The paper evaluates the importance of monotonicity in AI fairness across various fields.
problem Ensuring fairness in AI applications across criminology, education, health care, and finance.
method Theoretical reasoning, simulation, and extensive empirical analysis of monotonic neural additive models (MNAMs).
result Monotonicity is essential for fairness in AI ethics and society, especially in criminology, education, health care, and finance.
PIMKL uses pathway knowledge to improve sample classification and provide interpretable molecular signatures.
problem Lack of reliable molecular biomarkers and poor generalization in machine learning for health care.
method Pathway Induced Multiple Kernel Learning (PIMKL) using a mixture of pathway-induced kernels optimized via Multiple Kernel Learning.
result PIMKL provides interpretable molecular signatures and stable predictions.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
Approximate dynamic programming (ADP) has proven itself in a wide range of applications spanning large-scale transportation problems, health care, revenue management, and energy systems. The design of effective ADP algorithms has many dimensions, but one crucial factor is the stepsize rule used to update a value functi…
Improved predictions for rare labels using neural networks and ontologies.
problem Long-tailed frequency distribution in multi-label prediction problems.
method Modified neural network output layer with a Bayesian network of sigmoids leveraging ontology relationships.
result Significant improvements in per-label AUROC and average precision for less common labels.
Survey reviews explainability in AI for healthcare, emphasizing trust and transparency.
problem Lack of transparency hinders AI adoption in healthcare.
method Comprehensive literature review to guide explainable AI design.
result Quantitative evaluation metrics are needed for some explainability properties.
This research improves infection prediction from symptoms across diverse study designs.
problem Predicting infection from symptom data is challenging due to varied study formats and contexts.
method Assesses transfer learning methods for improving prediction across different study types.
result It is possible to use data from one study to predict infection in another, with performance close to or better than using a single dataset.
Estimates causal effect of managed care plans on NYC Medicaid spending.
problem Generalizing causal estimates to a target population not well-represented by randomized studies.
method Conditional cross-design synthesis estimators combining randomized and observational data.
result Estimates causal effect of managed care plans on health care spending.
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
New fair regression method improves fairness in chronic kidney disease classification.
problem Mitigating societal bias in health care for multiple groups.
method Penalized fair regression framework for multiple groups, with penalties for true positive rate disparity.
result Achieves fairness-accuracy frontier beyond existing methods in simulations and real-world data.
Study assesses health plan risk measures for Solvency Capital Requirement.
problem Assessing risk measures for health plans to meet Solvency Capital Requirement.
method Three-part regression model with three GLMs for claim counts, episode allocation, and severity.
result Reduction in regression models compared to traditional methods.
GP-HD uses genetic programming to generate personalized health models.
problem Creating accurate, personalized health models from large health data.
method Genetic Programming framework to generate parameterized dynamical systems models.
result GP-HD models perform similarly to models based on domain knowledge and outperform LSTM models.
Experiment evaluates hospital case cost prediction models using Azure ML.
problem Accurate hospital case cost modelling for efficient financial management.
method Azure Machine Learning Studio tool for comparing 14 regression models.
result Robust regression, boosted decision tree, and decision forest models outperformed others.
Deep-AER system uses lightweight CNN models for EEG-based emotion recognition.
problem Challenging task of emotion recognition using EEG signals.
method Two-level ensemble of lightweight 1D-CNN models trained on a small dataset.
result Deep-AER achieved high accuracies (98.43% and 97.65%) for emotion detection.
Improved DNN calibration without sacrificing accuracy.
problem Poor calibration of over-parametrized DNNs in safety-critical applications.
method Decoupling feature extraction and classification layers, and applying Gaussian priors.
result Significant improvement in model calibration with minimal training cost.
Differential privacy in distributed learning reduces privacy risks.
problem Protecting privacy in machine learning applications with distributed data.
method Secure multi-party sum function and Gaussian mechanism for differential privacy in a distributed setting.
result Asymptotically optimal and practically efficient DP Bayesian inference with diminishing extra cost.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
New algorithm detects changes quickly without knowing parameters, near optimally.
problem Quickest change detection with unknown parameters.
method Leverages theoretical asymptotic properties to derive a scalable approximate algorithm with near optimal performance.
result Detects changes in constant complexity with near optimal performance.
Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.
problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.
A new evolutionary approach discovers shapelets for time series classification.
problem Discovering discriminative shapelets for time series classification.
method Evolutionary computation to evolve shapelets and their lengths.
result The approach finds better shapelets with fewer shapelets and less computational complexity.
Distributed training of deep neural networks over multiple agents.
problem Scarcity of labeled data and computational resources in training neural networks.
method Distributed learning of deep neural networks over multiple agents using semi-supervised learning.
result Performance similar to a regular neural network trained on a single machine.
Generative models assess quality on time-series data using ITS and FITD.
problem Lack of consensus for quality assessment of class-conditional generative models on time-series data.
method Introduced InceptionTime Score (ITS) and Frechet InceptionTime Distance (FITD) to evaluate generative models.
result ITS and FITD combined with TSTR can accurately assess generative model performance on time-series data.
New method selects key features from millions of biological data points.
problem Scalability issue in feature selection for ultra-high dimensional biological data.
method Scaled up HSIC Lasso to handle millions of features.
result Achieves high accuracy with only 20 out of one million features.
Improved robustness in multi-modal sensor fusion with deep learning.
problem Inconsistency in fusion weights leading to poor performance under sensor failures.
method Proposes deep multi-modal sensor fusion architectures with fusion weight regularization and target learning.
result Proposed architectures outperform existing deep learning methods under sensor failures.
Novel method for multiclass ROC curves using multidimensional Gini index.
problem Multiclass performance evaluation, especially for imbalanced datasets.
method Extends ROC curve methodology to multiclass settings using multidimensional Gini index.
result Validated through case studies in health care and finance.
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such coho…
Paper develops deep learning for predicting clinical endpoints from diverse medical records.
problem Predicting clinical endpoints from heterogeneous, irregularly visiting medical records.
method Proposes a novel model with a new gate to control visiting rates of different events.
result Model effectively predicts death and abnormal lab tests with real-world clinical data.
Change detection in multivariate time series has applications in many domains, including health care and network monitoring. A common approach to detect changes is to compare the divergence between the distributions of a reference window and a test window. When the number of dimensions is very large, however, the naive…
Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we deve…
Generative model improves ECG classification with limited data.
problem Poor performance of RNNs with limited channel ECG data.
method Generative Seq2Seq model to fill missing data, followed by discriminative learning.
result Generative approach outperforms standard RNNs in disease prediction.
Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often r…
Alternative model predicts health insurance reimbursement based on contract limitations.
problem Estimating the ratio of reimbursement to health care expenditures after deductibles and copayments.
method Proposes a Zero-One Inflated Beta regression model using GAMLSS.
result The model provides a dependency structure between reimbursement and contract limitations.