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.
Automated medical protocol uses neural networks and decision trees.
problem Improving healthcare delivery through automated decision-making.
method Hybrid model combining neural networks and decision trees.
result Effective early decisions for patient care.
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.
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
problem Lack of uncertainty representation in deep learning methods for patient-centered healthcare decisions.
method MCU-Net framework using U-Net and Monte Carlo Dropout with four uncertainty metrics.
result MCU-Net maximizes automated performance and refers truly uncertain cases.
Cardea automates machine learning for EHRs, improving model building efficiency.
problem Lack of a trusted, open-source framework for automated machine learning in EHRs.
method Uses FHIR for data structure, AUTOML frameworks for feature engineering, model selection, and tuning, and an adaptive data assembler.
result Demonstrates framework's effectiveness on 5 prediction tasks, highlighting its flexibility and human competitiveness.
Machine learning reduces workload in healthcare systematic reviews by 70%.
problem Efficiently screening abstracts for systematic reviews in healthcare.
method Training SVM classifiers on labeled abstracts to classify RCTs.
result SVM classifier achieved 90% accuracy and 0.84 F1 score.
In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach…
System automates identification of cancer drug repurposing from PubMed.
problem Manual extraction of cancer drug repurposing evidence from scientific publications is infeasible.
method NLP pipeline including querying, filtering, entity extraction, classification, and study type classification.
result Automated system extracts cancer drug repurposing evidence from PubMed abstracts.
Methodology creates holdout and test/train sets for ML studies, preserving data for future research.
problem Preserving data for future research studies that are analysis-naive.
method Modification of k-fold cross-validation, randomization, and three-way split (holdout, test, training).
result Efficiently creates holdout and test/train sets without forcing.
Develops ML-DQA for healthcare data quality assurance.
problem Inconsistent use of real-world data in machine learning projects.
method Develops ML-DQA framework based on RWD best practices.
result Five generalizable practices emerge from ML-DQA implementation.
Automated model assesses online health info quality using machine learning.
problem Low quality health information on the internet poses risks to patients.
method Used machine learning models, specifically hierarchical encoder attention-based neural networks (HEA) with BERT and BioBERT embeddings.
result HEA models outperform traditional models in evaluating health info quality.
To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources req…
Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical…
This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co-occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented wi…
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.
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.
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.
LLMs excel at summarizing and repairing complex models without needing full models.
problem Understanding and repairing complex models like GAMs.
method Hierarchical reasoning and extensive background knowledge.
result LLMs can detect anomalies, describe reasons, and suggest repairs.
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 assesses CNN model robustness to noise in low-cost CT scans.
problem Evaluate CNN model performance on noisy, artifact-prone low-cost CT images.
method Developed and tested a CNN model for head CT triage, varying tube current and projections.
result Model remains robust to reduced tube current and fewer projections, maintaining AUROC close to original.
Paper analyzes AI's impact on job tasks, predicting future demands.
problem AI's impact on job tasks and potential technological unemployment.
method Dynamic task shares analysis using ARIMA model on large job postings dataset.
result AI has risen in high wage occupations, predicting future task demands.
Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the potential to be used as point-of-care for cardiovascular disease diagnostic systems. Such wearable and wireless ECG systems require automatic detection of cardiovascular disease. Even in the primary care, automation of ECG diagnostic systems will …
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.
EAML combines human and machine learning for better predictions.
problem Limited data and trust in machine learning models.
method Automated method guiding expert knowledge integration into machine models.
result Improved predictions and generalizability with less data.
Paper uses ML to predict utility in APS dialogue outcomes.
problem Predict utility for different user subpopulations in APS.
method Develops EAI and EDS ML methods to predict utility functions.
result EDS more effective at predicting utility functions.
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%.
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.
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.
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.
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.
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.
VHGM-MAE generates synthetic humans from healthcare data.
problem Handling high-dimensional, sparse healthcare data with missing values.
method Masked autoencoder (MAE) tailored for healthcare data, addressing heterogeneity, missingness, and high-dimensionality.
result VHGM-MAE outperforms existing methods in missing value imputation and synthetic data generation.
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.
Optimal healthcare investment timing in a dynamic model with mortality risk.
problem Optimal timing of healthcare investment to reduce mortality risk.
method Dynamic framework, stochastic control, optimal stopping problem, dual transformation.
result Characterization of the optimal investment boundary and numerical solutions.
From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public healt…
Somed2Vec learns medical concept embeddings from SNOMED-CT, improving healthcare analytics.
problem Lack of effective vector representations for medical concepts in healthcare analytics.
method Graph-based representation learning using random walks and Poincaré embeddings on SNOMED-CT.
result Concept embeddings from SNOMED-CT significantly outperform state-of-the-art embeddings.
Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.
problem Jointly satisfying distribution-free coverage guarantees and risk-adaptive precision in clinical decision-making.
method Integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.
result Achieves target coverage (94.3% vs 95% target) with adaptive precision, 21% narrower intervals for low-uncertainty cases.
Hybrid AI and rule-based framework de-identifies medical imaging data.
problem De-identifying medical imaging data to protect PHI and PII.
method Combines rule-based and AI techniques with uncertainty quantification.
result Robust performance across benchmark datasets and regulatory standards.
Framework for AI healthcare products from concept to market.
problem Failure of AI products to reach clinics despite promising potential.
method Decision-making framework for AI healthcare product development.
result Guides through a three-phase process to market launch of validated AI products.
Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficie…