New method uses Markov chains for cost-optimal healthcare monitoring.
problem Optimizing healthcare monitoring protocols for patient health characteristics.
method Adapts Markov chain approach to account for random shift sizes, repairs, and time intervals.
result Optimal parameters can differ from traditional medical protocols, showing new insights.
Isthmus platform simplifies ML/AI integration in healthcare.
problem Challenges in deploying ML models in healthcare due to data quality, regulatory, and security issues.
method Turnkey, cloud-based platform addressing data quality, clinical relevance, and regulatory compliance.
result Reduces time to market for operationalizing ML/AI in healthcare.
Big data analytics improves healthcare through early detection and quality life.
problem Limited access to healthcare data hinders evidence-based decision-making.
method Analysis of healthcare data using various tools and techniques.
result Big data analytics enhances healthcare quality and patient outcomes.
Deep learning text embeddings improve fraud detection in healthcare insurance.
problem Improving fraud detection in healthcare insurance claims.
method Proposed deep learning architectures for text embeddings.
result Our approach outperforms other methods in detecting fraudulent claims.
Graphical model predicts rare disease physicians, improving accuracy.
problem Identifying rare disease physicians from imbalanced patient data.
method Factor Graph Approach modeling physician and patient features.
result Graphical model outperforms existing targeting methodologies.
A framework trades-off model deployment cost with accuracy for healthcare risk prediction.
problem Complex cost structures in healthcare limit existing predictive model deployment methods.
method Proposes a novel cost-sensitive structured regularizer using boolean circuits.
result Models are in harmony with underlying cost structure, providing excellent accuracy vs cost tradeoff.
China's wage growth syncs with GDP, unlike in developed countries.
problem Wage growth decoupling from GDP in developed countries.
method Comparative statistical evidence and productivity growth analysis.
result Rural flight from agriculture to manufacturing drives productivity growth in China.
This paper reviews early time series classification methods.
problem Minimizing class prediction delay in time-sensitive applications.
method Divided into four categories: prefix based, shapelet based, model based, and miscellaneous approaches.
result Demonstrates reasonable performance in various applications.
Generative adversarial networks enhance risk prediction in EHRs.
problem Limited labeled data in EHRs for deep learning risk prediction.
method ehrGAN, a modified GAN, generates realistic EHR data for semi-supervised learning.
result Significant improvements in classification tasks with generated data over baselines.
Unified framework for intersectionally fair AI models using MIO.
problem Bias in AI models for high-risk domains.
method Mixed-Integer Optimization (MIO) for fairness and interpretability.
result Improved performance in detecting and mitigating bias at intersections.
Study examines perceptions and attitudes about breast cancer on Twitter.
problem Understanding public perceptions and attitudes towards breast cancer on social media.
method Identified and collected tweets, used topic modeling and sentiment analysis.
result Identified themes and quantified users' perceptions and emotions about breast cancer.
Quantized BNNs maintain uncertainty estimation quality despite reduced precision.
problem Reduced precision in BNNs due to quantization.
method Quantized BNNs with 32-bit weights and activations compressed to 16-bit integers.
result Uniform quantization does not significantly degrade uncertainty estimation quality.
VAE generates synthetic patient data for training deep learning models.
problem Lack of accurate patient data for training deep learning models.
method Used Variational Autoencoders (VAE) to learn latent distribution of patient features and generate new patient records.
result VAE can generate new accurate patient records based on diagnosis.
Care2Vec uses deep learning to classify self-care problems in disabled children.
problem Accurate classification of self-care problems in physically disabled children.
method Care2Vec is a deep learning approach combining unsupervised and supervised learning using Autoencoders and Deep neural networks.
result Care2Vec outperforms traditional methods like Decision trees and Artificial neural networks in self-care classification.
We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (…
Proposes a novel model for healthcare and SME credit risk prediction.
problem Lack of guidance from global view in sequence representation learning for time series modeling.
method Hierarchical Global View-guided (HGV) sequence representation learning framework with GGE and β-Attn modules. result Competitive prediction performance compared with other known baselines.
Synthetic data improves financial models without real data.
problem Lack of real financial data due to privacy and regulation.
method Application of synthetic data across various financial data types.
result Synthetic data enhances financial model accuracy and fairness.
CLOCS uses contrastive learning to improve cardiac signal representations.
problem Lack of labelled data in cardiac signal analysis.
method Contrastive learning across space, time, and patients.
result CLOCS outperforms state-of-the-art methods and achieves strong generalization.
This paper reviews methods to improve AI explainability in finance.
problem Lack of explainability in AI models, especially in finance.
method Categorizes methods to improve explainability of deep learning models.
result Provides a comparative survey of methods to enhance AI explainability.
Probabilistic ML improves healthcare data analysis.
problem Insufficient understanding and incomplete data in healthcare.
method Examination of probabilistic machine learning models for healthcare challenges.
result Probabilistic models enhance healthcare data analysis and model building.
A statistical description and model of individual healthcare expenditures in the US has been developed for measuring value in healthcare. We find evidence that healthcare expenditures are quantifiable as an infusion-diffusion process, which can be thought of intuitively as a steady change in the intensity of treatment …
Optimizes consumption, investment, and healthcare spending for aging populations.
problem Maximizing utility in an aging population with changing mortality rates.
method Solves an optimal stochastic control problem using a novel version of Perron's method.
result Endogenous mortality law that is asymptotically exponential in the old-age limit.
Method estimates causal effects from incremental data, overcoming missing data challenges.
problem Estimating causal effects from non-stationary, incrementally available observational data.
method Continual Causal Effect Representation Learning
result Method achieves continual causal effect estimation without compromising original data.
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
Study identifies what healthcare tasks can and should be automated.
problem Undesired consequences of automation in healthcare.
method Quantitative analysis using machine learning models trained on practitioner and expert ratings.
result Developed an analytical tool (Automatability-Desirability Matrix) for policymakers and leaders.
MPVAA learns holistic patient representations from mixed healthcare data.
problem Learning personalized patient representations from heterogeneous healthcare data.
method Mixed Pooling Multi-View Attention Autoencoder (MPVAA) that integrates non-linear relationships among multiple data modalities.
result MPVAA generates more effective patient representations than state-of-the-art methods.
Survey on securing ML for healthcare, addressing privacy and robustness issues.
problem Security and robustness challenges in healthcare ML/DL applications.
method Overview of security and privacy methods for ML in healthcare.
result Discussion of current research challenges and future directions.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
Study investor sentiment and disagreement on StockTwits during COVID-19.
problem Understanding investor beliefs and sentiment during the pandemic.
method Analysis of social media data (StockTwits) for investor messages.
result Sentiment and disagreement sharply decreased in early March 2020, followed by a reversal.
Study optimal healthcare spending under Epstein-Zin preferences for longevity.
problem Optimizing healthcare spending to extend longevity under Epstein-Zin preferences.
method Formulated Epstein-Zin utilities over a controllable random horizon using backward stochastic differential equations and HJB equations.
result Calibrated model accurately reflects actual mortality data and compares healthcare efficacy between countries.
Study improves conformal prediction for EEG classification in healthcare, enhancing coverage.
problem Uncertainty quantification in clinical predictions, especially in distribution-shifted settings.
method Personalized calibration strategies to improve coverage of prediction sets.
result Coverage improved by over 20 percentage points with comparable prediction set sizes.
A recommendation framework helps users choose healthcare interventions.
problem Choice overload in online healthcare communities.
method Multi-Armed Bandit (MAB) approach with innovative model components.
result Our recommendation design outperforms state-of-the-art systems.
Interpretability of ML models improves healthcare decisions.
problem Ensuring machine learning models are understandable for healthcare users.
method Classifying interpretability into local and global approaches, and model-specific vs. model-agnostic methods.
result Examples of practical interpretability in healthcare, including prediction and treatment optimization.
Machine learning in healthcare faces challenges due to complex data attributes.
problem Complex data attributes hinder accurate insights from machine learning models.
method Discusses preprocessing, model building, and interpretation challenges.
result Understanding data attributes is crucial for successful machine learning in healthcare.
Paper develops a model to predict kidney transplant success.
problem Mismatched deceased donor-recipient kidney leads to post-transplant death.
method Data analysis of 584 imported kidneys from 12 transplant centers.
result Predicting model reduces mortality rate due to kidney mismatch.
Broad learning integrates diverse healthcare data for diagnostics and precision medicine.
problem Integrating various types of healthcare data for better diagnostics and personalized medicine.
method Fusing multi-view data including scalar, tensor, graph, and sequence data for knowledge discovery and machine learning tasks.
result Accurate user profiles and brain connectivity patterns can be created for improved diagnostics and personalized medicine.
Paper develops a method to estimate value of a policy in confounded MDPs.
problem Estimating value of a policy in the presence of unmeasured confounders.
method Uses auxiliary variables to identify target policy's value in a confounded MDP.
result Develops an off-policy value estimator robust to model misspecification.
Machine learning faces challenges in healthcare data, but offers opportunities.
problem Poorly labeled data, multiple endotypes, and underrepresented healthy individuals.
method Review of existing machine learning methods and challenges in healthcare.
result Opportunities for machine learning in healthcare identified.
LMM predicts healthcare costs and risks with improved accuracy.
problem Wasteful healthcare spending and inefficiencies in risk prediction.
method Generative pre-trained transformer trained on patient event sequences.
result Improves cost prediction by 14.1% and chronic conditions prediction by 1.9%.
Introduces Temporal Group LASSO for healthcare time series prediction.
problem Predicting response variables over time in healthcare.
method Multi-task, regularized regression approach for time series data.
result Reduces overfitting, selects predictors, learns smooth patterns over time.
Study improves healthcare time series imputation by considering structured missingness.
problem Structured missingness in clinical data impacts time series imputation models.
method Analysis of different masking strategies on imputation methods using PhysioNet Challenge 2012 dataset.
result Masking choices significantly affect imputation accuracy and clinical prediction.
Unified framework for imputation and prediction in healthcare time series.
problem Time misalignment and data sparsity in healthcare time series.
method MAGIC (Multi-tAsk Gaussian Process for Imputation and Classification) using hierarchical multi-task Gaussian process and functional logistic regression.
result Superior predictive accuracy compared to existing methods in two healthcare applications.
SSPN uses deep learning to estimate risk scores in survival analysis with competing risks.
problem Nonidentifiability of cause-specific survival curves in competing risk survival analysis.
method Siamese Survival Prognosis Network (SSPN) that avoids estimating cause-specific survival curves and optimizes an approximation to the C-discrimination index.
result SSPN estimates pairwise concordant time-dependent risks, improving risk scoring in survival analysis with competing risks.
MCRAGE generates synthetic data to balance healthcare datasets.
problem Imbalanced datasets in healthcare lead to biased model performance for minority groups.
method Generative modeling to create synthetic data for underrepresented classes.
result MCRAGE improves model performance on minority groups.
Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.
problem Understanding what drives healthcare providers to engage with SMS campaigns.
method Used logistic regression, random forest, and neural network models to analyze data.
result Identified key factors influencing engagement with SMS campaigns.
Research predicts healthcare index movements using historical OHLC data.
problem Predicting the directional movement of healthcare indices based on historical data.
method Supervised classification task with a one-step-ahead rolling window, using a diverse feature set including OHLC ratios.
result Robust predictive performance with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6, highlighting the importance of nowcasting features.
Unsupervised model detects healthcare fraud from patient visit data.
problem Detecting fraudulent healthcare bills from patient visit data.
method Uses LSTM and seq2seq models for anomaly detection, normalizes scores with EDF.
result Improves anomaly detection for high class imbalance problems.
Proposes a method to improve rare event prediction in healthcare.
problem Rare event classification in healthcare with low prevalence labels.
method Variational disentanglement approach to semi-parametric learning.
result Outperforms existing alternatives in mortality prediction on COVID-19 cohort.