Problem
Research questions and friction points this paper is trying to address.
oncology information
structured data
clinical context
abstraction burden
scalable methods
From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction
🤖 AI Summary
研究针对肿瘤文档信息提取难题，提出并评估了一种名为nMAS的多代理系统工作流方法，实现临床相关结构化数据的高效提取。
📝 Abstract
Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and requiring accurate attribution across specimens, tumors, biomarkers, and time points, while manual cancer-registry abstraction can require 27.2 minutes per case, highlighting the need for scalable methods that preserve clinical context while converting documentation into structured data. We evaluate the Nimblemind Multi-Agent System (nMAS), a configurable oncology information-extraction workflow which extracts clinically relevant structured fields from fragmented oncology documentation. The extraction task uses a clinician-informed schema of 328 attributes spanning report metadata, diagnosis, staging, and cancer-type-specific information. nMAS separates clinician-defined field specifications from model execution and combines complexity-aware extraction, report-level consolidation, and source-grounded validation. The retrospective evaluation included 230 de-identified oncology documents from 40 patients and 418 clinician-reviewed document-field pairs containing 1,126 non-empty reference values. Evaluation focused on fields identified by clinicians as present in the source documents rather than exhaustively annotating all 328 schema fields. nMAS achieved a rank-weighted value-level precision of 82.6%, recall of 87.5%, and F1 of 85.0%, compared with an F1 of 66.4% for an independently implemented UMA-style MiniMax M2.5 comparator. These findings support the feasibility of using a configurable, source-grounded extraction workflow to convert fragmented oncology documentation into reusable structured data.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Agent System
Oncology Information Extraction
Configurable Workflow
Source-Grounded Validation
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