2016/12/05 by Cheng Zhang, Zhang, Cheng, Hedvig Kjellström +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Topic Modeling #cs.LG
paper · pdf · doi:10.48550/arxiv.1612.01356
NIPS 2016 Workshop on Machine Learning for Health
arxiv created 2016/12/05 · openalex publication_date 2016/12/05 · arxiv updated 2016/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we extend a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.