Positioning manuscripts in the scientific landscape with agentic AI
本文提出PASS系统，通过理解论文内容及文献背景预测最佳发表期刊，帮助研究人员解决发表过程中的不确定性问题。
0 citationsRead paper
Selected work
Representative Papers
Extracting Post-Acute Sequelae of SARS-CoV-2 Infection Symptoms from Clinical Notes via Hybrid Natural Language Processing
Post-acute sequelae of SARS-CoV-2 infection (PASC) exhibit high symptom heterogeneity and temporal dynamics, hindering accurate clinical identification from unstructured electronic health records. Method: We developed an end-to-end hybrid NLP pipeline integrating rule-based named entity recognition with a fine-tuned BERT model for assertion classification, augmented by clinical text normalization and a curated PASC-specific terminology dictionary. Results: The system achieved an F1-score of 0.82 in single-center validation and 0.76 in ten-center external validation, with an average processing time of 2.45 seconds per note; assertion outputs showed strong correlation with ground-truth annotations (Spearman ρ > 0.83, *P* < 0.0001). Its key contribution is the first synergistic integration of structured linguistic rules and deep learning–based assertion modeling for PASC symptom extraction—enhancing both cross-center generalizability and clinical interpretability, thereby enabling robust large-scale PASC epidemiological studies.
0 citationsRead paper
Recent publications
Latest Papers
Positioning manuscripts in the scientific landscape with agentic AI
本文提出PASS系统，通过理解论文内容及文献背景预测最佳发表期刊，帮助研究人员解决发表过程中的不确定性问题。
0 citationsRead paper
Extracting Post-Acute Sequelae of SARS-CoV-2 Infection Symptoms from Clinical Notes via Hybrid Natural Language Processing
Post-acute sequelae of SARS-CoV-2 infection (PASC) exhibit high symptom heterogeneity and temporal dynamics, hindering accurate clinical identification from unstructured electronic health records. Method: We developed an end-to-end hybrid NLP pipeline integrating rule-based named entity recognition with a fine-tuned BERT model for assertion classification, augmented by clinical text normalization and a curated PASC-specific terminology dictionary. Results: The system achieved an F1-score of 0.82 in single-center validation and 0.76 in ten-center external validation, with an average processing time of 2.45 seconds per note; assertion outputs showed strong correlation with ground-truth annotations (Spearman ρ > 0.83, *P* < 0.0001). Its key contribution is the first synergistic integration of structured linguistic rules and deep learning–based assertion modeling for PASC symptom extraction—enhancing both cross-center generalizability and clinical interpretability, thereby enabling robust large-scale PASC epidemiological studies.
0 citationsRead paper