Back to search

Article

PHO-Agents: A Large Language Model–Powered Multi-Agent System for Predicting Health Outcomes

2026-06-29

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Objective</h4> Predicting health outcomes from electronic health records (EHRs) is challenging because traditional models rely on structured data and often ignore external medical knowledge. We propose an approach that integrates structured EHR with text-based clinical evidence to improve prediction and interpretability. <h4>Methods</h4> We introduce PHO-Agents, a multi-agent system powere...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
e83e4dfa-c676-55fd-bfe1-96272015b67c
DOI
10.64898/2026.06.19.26355815
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
PHO-Agents: A Large Language Model–Powered Multi-Agent System for Predicting Health OutcomesDOI 10.64898/2026.06.19.26355815
AI Agents in Clinical Medicine: A Systematic ReviewSemantic proximity
Med-AgentX: Multimodal Large Language Model Agents with Explainable Reinforcement Learning for Trustworthy Biomedical Decision SupportSemantic proximity
Comparative Performance of agentic AI and Physicians in Taking Clinical History across Leading Large Language Models (LLMs)Semantic proximity
Augmenting Large Language Models and Retrieval-Augmented Generation with an Evidence-Based Medicine-Enabled Agent SystemSemantic proximity
A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health recordsSemantic proximity
Evaluating Large Language Model Diagnostic Performance on JAMA Clinical Challenges via a Multi-Agent Conversational FrameworkSemantic proximity
Clinically grounded multi-agent artificial intelligence for preventive health managementSemantic proximity
Multidisciplinary large language model agent teams for precision oncology enhance complex gynecologic oncology decision supportSemantic proximity
Artificial Intelligence - Large Language Models (LLMs) and Agentic AI in Clinical Decision Support in Outpatient, General Practice and Acute Settings: A Systematic Review of Diagnostic Accuracy, Comparative Efficacy, Clinical Outcomes and Implementation FactorsSemantic proximity
PhenoAgent: agentic LLM framework for phenotyping electronic health records via structured query decomposition and self-correctionSemantic proximity
AI-HOPE: An AI-Driven conversational agent for enhanced clinical and genomic data integration in precision medicine researchSemantic proximity
Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning TasksSemantic proximity
Select a neighboring publication to make it the new centre.