MedGCN uses graph convolutional networks to recommend medications and estimate lab tests.
problem Automatically recommend medications and estimate lab tests for cost savings and better patient care.
method Integrates heterogeneous graph relations, learns node embeddings with graph convolutional networks, and uses cross regularization for multi-task learning.
result MedGCN outperforms state-of-the-art models in both medication recommendation and lab test imputation on real-world datasets.
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existi…
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
problem Continuous treatment recommendation in medical settings with survival data.
method Deep Survival Dose Response Function (DeepSDRF) for learning conditional average dose response (CADR) function.
result Similar performance of recommender algorithms based on random search and reinforcement learning.
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
This paper identifies and addresses biases in medical imaging research.
problem Challenges in medical imaging research, including biases in datasets, methods, and evaluation metrics.
method Literature review and analysis of potential biases.
result Recommendations to counteract biases in future research.
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or pri…
Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …
Inpatient2Vec learns representations for inpatients with multi-layer self-attention.
problem Lack of specialized RL methods for inpatient data with strong temporal relations and consistent diagnoses.
method Inpatient2Vec uses a multi-layer self-attention mechanism with two training tasks to learn medical activity, hospital day, and diagnosis representations for inpatients.
result Inpatient2Vec outperforms baselines on semantic similarity and clinical events prediction tasks.
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records (EMRs) that are only just recently being leveraged to i…
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. More…
System exposes study population descriptions in clinical guidelines.
problem Challenges in understanding applicability of clinical guidelines.
method Developed an ontology-enabled prototype system using SIO.
result Allows medical practitioners to better understand study populations.
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural netwo…
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
This paper tackles label noise in deep learning for medical image analysis.
problem Label noise impacts deep learning models in medical image analysis.
method Review of state-of-the-art techniques and experiments with label noise in medical datasets.
result Developed new methods to combat label noise in deep models for medical image analysis.
Develops new methods for risk-aware decision-making in medical bandits.
problem Risk-averse decision-making in medical contexts with limited data.
method Safe, anytime-valid concentration bounds, risk-aware contextual bandits, nonparametric algorithms.
result Improved decision-making algorithms for postoperative patient follow-up.
Foundation models alter medical data science workflow, challenging veridical data science principles.
problem Foundation models disrupt traditional data science practices in medicine.
method Critically examined the medical foundation model lifecycle and its deviation from veridical data science principles.
result Foundation models challenge veridical data science principles of predictability, computability, and stability.
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
problem Identifying financial health and reporting consistency in Kenyan medical insurance companies.
method Advanced clustering techniques (KMeans, DTW) on financial ratios and time series data.
result Four distinct clusters identified, each representing different financial performance and reporting consistency combinations.
Federated approach protects privacy while learning user patterns.
problem Protecting sensitive data in recommendation systems.
method Differentially private prototypes for global and local models.
result Local adaptation improves recommendation accuracy and relevance.
FEET protocol evaluates foundation models across three scenarios.
problem Lack of standardized evaluation protocols for foundation models.
method Structured evaluation protocol across three use cases: frozen, few-shot, and fine-tuned embeddings.
result Demonstrates comprehensive assessment of foundation models' effectiveness.
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervised latent Dirichlet allocation that balances two goals: faithful generative explanations of high-dimensional data and accurate prediction of…
Paper uses AI to improve medical diagnosis accuracy.
problem Improving accuracy of medical diagnoses.
method Heuristic frequentist and Bayesian approaches applied to a nationwide dataset.
result Algorithm outperforms human doctors in detecting abnormal births.
Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.
problem High recall rate and regulatory workload in FDA's 510(k) pathway.
method Developed machine learning models to estimate recall risk and proposed a data-driven clearance policy.
result Conservative evaluation of policy shows a 32.9% improvement in recall rate and 40.5% reduction in workload.
Paper tackles missing data in medical records using advanced optimization.
problem Missing data in medical records hinders machine learning applications.
method Applied multi-level stochastic optimization for imputation.
result The method significantly outperforms current approaches and reduces error up to 75%.
Solves signal recovery from few linear measurements using convex duality.
problem Recovering signals from limited linear measurements in various applications.
method Develops a convex-concave min-max reformulation for linear inverse problems.
result Simple ascent-descent algorithms for solving linear inverse problems.
The study evaluates AI model performance measures for medical use.
problem Selecting appropriate performance measures for AI models in medical practice.
method Assessed 32 performance measures across five domains for binary outcomes.
result 17 measures are both proper and reflect decision-analytic performance.
In most real-world settings such as recommender systems, finance, and healthcare, collecting useful information is costly and requires an active choice on the part of the decision maker. The decision-maker needs to learn simultaneously what observations to make and what actions to take. This paper incorporates the info…
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc…
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.
problem Learning new concepts without examples, especially in medical imaging.
method Uses existing knowledge and auxiliary information to predict unknown concepts.
result Effective in diagnosing COVID-19 from chest X-rays.
Spectral decoupling improves neural network generalization in medical imaging.
problem Poor generalization of neural networks trained on medical imaging data.
method Spectral decoupling, a regularization technique that encourages learning more features.
result Spectral decoupling increases network robustness and performance on external datasets.
Model predicts VKA dosage for Indian patients, aiding in safe medication.
problem Safe and accurate dosing of VKA drugs for Indian patients.
method Support Vector Machine (SVM) Regression model trained on patient data.
result Predicted dosages closely match actual dosages.
Study uses machine learning to analyze state drug policies and reduce overdose deaths.
problem Epidemic opioid overdose rates and ineffective state-level policies.
method Hierarchical clustering of 138 binomial variables to generate policy bundles, then regression analysis.
result Balancing certain policies leads to reduced overdose deaths, but only after second year.
Public data boosts MICCAI papers' citations, but sharing remains low.
problem Slow adoption of public benchmarks in medical image computing.
method Analysis of MICCAI papers from 2014-2018.
result Public data boosts citations, but sharing is low.
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit…
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement lear…
We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards i…
Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and impro…
AGATHA predicts promising research directions from scientific data.
problem Efficiently winnowing thousands of potential targets in drug discovery.
method Deep-learning hypothesis generation system that ranks plausible term-pairs.
result Best-in-class performance on an established benchmark, high recommendation scores across subdomains.
A new estimator reduces variance in slate bandit OPE.
problem Large action spaces in slate bandits cause high variance in OPE.
method Develops Latent IPS (LIPS) to optimize slate abstractions for low variance and bias.
result LIPS substantially outperforms existing estimators in scenarios with non-linear rewards and large slate spaces.
AI improves healthcare diagnostics and predictions.
problem Data heterogeneity and model limitations in AI for health.
method Review of AI applications in health informatics.
result AI enhances disease diagnosis and prediction.
Improves medication name inference for telemedicine and conversational agents.
problem Challenges in mapping user-friendly medication names to standardized ones.
method Entity-boosted two-tower neural network for ranking SMN to DMP.
result State-of-the-art results achieved with improved attention-based ranking.
Medical deconfounder uses EHRs to estimate treatment effects without confounders.
problem Bias in assessing treatment effects from EHRs due to unobserved confounders.
method Develops a machine learning algorithm (medical deconfounder) to adjust for confounders.
result Medical deconfounder produces more accurate treatment effect estimates and identifies effective medications.
The paper proposes a policy learning framework for interpretable personalization.
problem Effective personalization of goods and services to improve revenues and maintain competitive edge.
method Policy learning with linear decision boundaries using causal inference and Bayesian optimization.
result The learned policy improves net sales revenue by 88.2% and provides insights into important features.
Estimates causal effects from patient trajectories using DeepACE model.
problem Estimating causal effects from observational data in medical practice.
method DeepACE model using iterative G-computation formula and sequential targeting procedure.
result DeepACE achieves state-of-the-art performance in estimating time-varying ACEs.
Two large medical dialogue datasets for improving healthcare.
problem Improving healthcare through better dialogue systems.
method Building two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN.
result The datasets are the largest medical dialogue datasets to date.
Proposes guidelines for developing medical AI products.
problem Lack of clear pathways for regulating medical AI.
method Statistical risk perspective and deep understanding of machine learning methodologies.
result Enhanced development of medical AI products and regulations.
This study predicts diabetes complications using financial records and neural networks.
problem Managing chronic diseases like diabetes in patients.
method Used financial records from health plans, applied self-attentive recurrent neural networks.
result Successfully predicted diabetes complications with an AUC of 0.81-0.94, 60-240 days ahead.
Paper tackles medical question similarity using domain-relevant embeddings.
problem Identifying same-question pairs in medical contexts.
method Semi-supervised pre-training of a neural network on medical question-answer pairs.
result Our model achieves 82.6% accuracy on medical question similarity task.