arXiv research

Study finds non-adherence to schizophrenia meds leads to earlier adverse events.

Smart bin monitors predict medication adherence with high accuracy.

Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. Thi…

Simulation framework assesses ROI of chronic disease adherence and policy timing.

OptiLIME improves LIME explanations by balancing stability and adherence.

System predicts HIV patients at risk of dropping out of care.

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. T…

Framework detects brain tumors robustly from MRI images.

Religious adherence reduces corporate greenwashing behavior.

The paper tackles AI advice giving by considering adherence levels and defer options.

New method quantifies uncertainty at class level for better decision-making.

HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.

Study optimal and equitable encouragement policies for treatment adherence.

Algorithm minimizes regret while adhering to unknown safety constraints.

Proposes a Gaussian process model for constrained dynamics learning.

Improves medication name inference for telemedicine and conversational agents.

The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …

Two large medical dialogue datasets for improving healthcare.

Proposes guidelines for developing medical AI products.

New approach tackles non-Markovian behavior in maternal health programs.

E2C separates planning and execution in LLMs, improving efficiency and performance.

Paper tackles inventory management with deep learning, improving performance and adherence to constraints.

Automated system extracts medication regimens from medical conversations.

The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time…

New bandit model for healthcare intervention planning.

This paper benchmarks privacy-preserving machine learning on medical images.

Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more…

Machine learning constructs problem-based medical records from electronic health records.

latrend simplifies longitudinal clustering for numeric measurements.

Enhances medical code predictions for multi-morbidity patients using text classification.

Paper detects bias in AI medical models using CART.

The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general questio…

A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. By the third kind of Bayes' theorem, we can directly convert a Shannon's channel into an optimized semantic channel. When a sample is not big enough, we can use a truth function with paramete…

Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that h…

This study applies neural models to automatically recognize medical entities from natural language.

Survey of deep learning methods for medical anomaly detection.

MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.

Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, registration researchers focused on an alternative way to represent medical images. An alternative way to analyze medical imaging is by underst…

Privacy-preserving deep learning for medical data across distributed platforms.

RL algorithms with medical integration improve personalized treatment recommendations.

Review of deep learning methods in medical image registration.

IAGAN method improves medical image reconstruction by incorporating adaptive GAN priors.

Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset,…

Improves reliability of medical diagnosis uncertainty estimates.

Independent component analysis (ICA) is a widely used BSS method that can uniquely achieve source recovery, subject to only scaling and permutation ambiguities, through the assumption of statistical independence on the part of the latent sources. Independent vector analysis (IVA) extends the applicability of ICA by joi…

Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize imag…

DiffDenoise preserves fine structures in medical images using conditional diffusion models.

This paper tackles label noise in deep learning for medical image analysis.