← 研踪 PaperTrailarXiv:2611.15798 · v1
Bag of concepts can enhance other text representations for medical systematic review screening
首次提交 2026-11-12 02:11
版本更新 2026-11-12 02:11
雷达观测 2026-11-13 14:16
摘要
The paper examines the choice of text representation for semi-automated systematic literature review (SLR) systems based on active learning. Motivated by the insufficient attention that the bag of concepts representation has received so far, the paper discusses the implementation and experimental evaluation of several variations thereof. The experimental study was performed on medical SLR case studies, with results reported for random forest classification, cluster initialization and uncertainty sampling. The utility of bag of concepts features obtained using different ontologies is evaluated by applying them within the active learning framework to sequentially create classification models based on training data iteratively expanded with expert annotations. Systematic experiments compare BoW, BoC, PubMedBERT, Jina, Gemma and Qwen3 representations, together with combinations of BoC and each of the other representations. The evaluation uses workload savings and performance profiles adapted from optimization benchmarking. The results suggest that concept-based features can provide complementary information, with the BoC-augmented representations achieving higher mean workload savings on the DERP cases than their corresponding base representations. The Qwen3+BoC representation gives the best average performance in this comparison. The effects of augmentation vary across case studies and base representations. The paper also concentrates on the impact of ontology choice on document classification outcomes. In particular, the paper compares the predictive value of bag of concepts representations based on chosen ontologies with that of representations obtained using MedCAT with a UMLS-based dictionary.
作者备注：16 pages, 5 figures, 6 tables
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-12 02:11 · 最新观测版本
Bag of concepts can enhance other text representations for medical systematic review screening
The paper examines the choice of text representation for semi-automated systematic literature review (SLR) systems based on active learning. Motivated by the insufficient attention that the bag of concepts representation has received so far, the paper discusses the implementation and experimental evaluation of several variations thereof. The experimental study was performed on medical SLR case studies, with results reported for random forest classification, cluster initialization and uncertainty sampling. The utility of bag of concepts features obtained using different ontologies is evaluated by applying them within the active learning framework to sequentially create classification models based on training data iteratively expanded with expert annotations. Systematic experiments compare BoW, BoC, PubMedBERT, Jina, Gemma and Qwen3 representations, together with combinations of BoC and each of the other representations. The evaluation uses workload savings and performance profiles adapted from optimization benchmarking. The results suggest that concept-based features can provide complementary information, with the BoC-augmented representations achieving higher mean workload savings on the DERP cases than their corresponding base representations. The Qwen3+BoC representation gives the best average performance in this comparison. The effects of augmentation vary across case studies and base representations. The paper also concentrates on the impact of ontology choice on document classification outcomes. In particular, the paper compares the predictive value of bag of concepts representations based on chosen ontologies with that of representations obtained using MedCAT with a UMLS-based dictionary.
查看此版本解读 →查看此版本原文 ↗