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In this paper, we propose a multi-stage feature fusion (MSFF) framework combining machine learning to track the diseases deterioration risk of the AECOPD.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>First, we identify 408 AECOPD patients as the study population. Then, feature segment and fusion methods are applied to generate the phased data set. Finally, human studies are designed to evaluate the performance of the MSFF framework.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The experimental results show that the proposed framework is potential to obtain the full-process tracking of deterioration risk for the AECOPD patients. The proposed MSFF framework achieves a higher overall accuracy average and F1 scores than the four physician groups i.e., IM, Surgery, Emergency, and ICU.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The proposed MSFF model may serve as a useful disease tracking tool to estimate the deterioration risk at each stage, and finally achieve the disease monitoring and management for AECOPD patients.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01708-2","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T10:02:55Z","timestamp":1639476175000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning"],"prefix":"10.1186","volume":"21","author":[{"given":"Junfeng","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiqiang","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongyong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"1708_CR1","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1164\/ajrccm.157.5.9709032","volume":"157","author":"TA Seemungal","year":"1988","unstructured":"Seemungal TA, Donaldson GC, Paul EA, Bestall JC, Jeffries DJ, Wedzicha JA. 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