{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/medfuse-multi-modal-fusion-with-clinical-time","title":"MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images","arxiv_id":"2207.07027","date":"2022-07-14","proceeding":null,"authors":["Nasir Hayat","Krzysztof J. Geras","Farah E. Shamout"],"abstract":"Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of \"paired\" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion method and introduce new benchmark results for in-hospital mortality prediction and phenotype classification, using clinical time-series data in the MIMIC-IV dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more complex multi-modal fusion strategies, MedFuse provides a performance improvement by a large margin on the fully paired test set. It also remains robust across the partially paired test set containing samples with missing chest X-ray images. We release our code for reproducibility and to enable the evaluation of competing models in the future.","url_abs":"https://arxiv.org/abs/2207.07027v2","url_pdf":"https://arxiv.org/pdf/2207.07027v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"medfuse-multi-modal-fusion-with-clinical-time","repo_url":"https://github.com/nyuad-cai/medfuse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"mortality-prediction","task_name":"Mortality Prediction"},{"task_slug":"phenotype-classification","task_name":"Phenotype classification"},{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/phenotype-classification-on-mimic-cxr-mimic","task":"Phenotype classification","dataset":"MIMIC-CXR, MIMIC-IV","model":"MedFuse (optimal)","rank_in_archive_order":1,"of":1,"metrics":{"AUROC":"0.77"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.07027","atlas_url":"https://app.syntology.ai/?focus=2207.07027","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}