A Deep Learning system for Automated Quality Control of Medical Questionnaire data in Large-Scale Cohort Studies

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ID: 331840
2026
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Abstract
Abstract Recent advances in natural language processing combined with deep learning have transformed disease prediction, diagnosis, and treatment, yet their application in automated quality control of medical questionnaire data remains largely unexplored. Here, we present an end-to-end system tailored for large-scale cohort interview data. A fine-tuned Whisper model was developed for automatic audio-to-text transcription and optimized for recognizing domain-specific medical terminology and regional dialects, achieving an accuracy of over 91%. Using the DeepSeek framework, we jointly analyzed transcribed texts, interviewer records, and questionnaire content from 522 medical interviews to identify quality issues, achieving the area under the receiver operating characteristic curve (AUC) values of 0.893, 0.953, and 0.973 for omitted question stems, response–record inconsistencies, and insufficient follow-up, respectively. The system achieved high agreement with the senior specialist reference (F1 = 0.843, Kappa = 0.835) and significantly outperformed the junior specialist group (P < 0.01). Evaluation on an independent validation dataset (n = 96), which was collected using a structurally distinct brain health questionnaire, further demonstrated robust performance and generalizability (F1 = 0.831, Kappa = 0.821). Collectively, this framework enhances the efficiency and reliability of medical questionnaire data processing and offers a practical artificial intelligence-driven solution for quality control in global health data, especially in low-resource dialect settings.
Reference Key
openalex_W7221018771 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Minmin Cao, Yanan Duan, Dong Lang, Pinqi Fang, Zhouyu Guan, Xinming Xu, Qingqing Zheng, Di Cheng, Fusong Jiang, Hua Zhao, Keqing Dong, Shijun Wang, Bin Sheng, Huating Li, Weiping Jia
Journal journal of molecular cell biology
Year 2026
DOI
10.1093/jmcb/mjag038
URL
https://doi.org/10.1093/jmcb/mjag038
Keywords Keywords not found

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