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Found 16 papers, 4 papers with code
Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines
Results All four LLMs exhibit improved performance when enhanced with CPGs compared to the baseline ZSP.
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements.
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative AI, and we hope that it will inspire and inform future research in this area.
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study
Objective: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of post-stroke patients treated at the University of Pittsburgh Medical Center.
Hyperbolic Molecular Representation Learning for Drug Repositioning
We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space.
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing
The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts.
Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space
We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the knowledge-based drug-drug similarity to induce the clustering of drugs in hyperbolic space.
Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large.
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Many real datasets contain values missing not at random (MNAR).
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing.
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values
The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG.
Markov Boundary Discovery with Ridge Regularized Linear Models
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response.
Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data
However, the proposed algorithm using a CDM outperforms the proposed algorithm using a DM only when sample sizes are above several hundred.
Counting Markov Blanket Structures
Learning Markov blanket (MB) structures has proven useful in performing feature selection, learning Bayesian networks (BNs), and discovering causal relationships.
Markov Blanket Ranking using Kernel-based Conditional Dependence Measures
Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences.
Deep Multiple Kernel Learning
Deep learning methods have predominantly been applied to large artificial neural networks.
