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Utilizing clinical data from 107 confirmed cases, we developed several predictive models and identified the extreme gradient boosting (XGBoost) algorithm as the most effective, achieving an area under the curve (AUC) of 0.950. Further analysis was performed based on the 15 robust features selected and performance-optimized through the Boruta algorithm, integrating\n                      <jats:underline>Sh<\/jats:underline>\n                      apley\n                      <jats:underline>a<\/jats:underline>\n                      dditive\n                      <jats:underline>e<\/jats:underline>\n                      x\n                      <jats:underline>p<\/jats:underline>\n                      lanations (SHAP) plots to identify key variables such as IL-6, TNF-\u03b1, PLR, and AST. While Mendelian randomization did not reveal definitive causal pathways across all examined factors, notable associations were observed in specific metabolic-inflammatory pathways. These findings offer a foundation for enhanced predictive accuracy and mechanistic understanding of SFTS mortality.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne viral disease with high mortality, posing a major threat to public health in East Asia. This study develops a machine learning (ML) model to quickly predict SFTS patients\u2019 mortality risk using routine clinical tests, helping doctors stratify risks early. Moreover, by exploring causal pathways between bodily indicators and disease outcomes, it provides new clues for understanding why SFTS becomes severe, which could guide better treatments in the future.<\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/msphere.00831-25","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:00:21Z","timestamp":1781614821000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Integrative analysis of mortality risk in SFTS using machine learning and genetic approaches"],"prefix":"10.1128","volume":"11","author":[{"given":"Helin","family":"Zha","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03t1yn780","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianzi","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03t1yn780","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyang","family":"Sun","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01r4q9n85","id-type":"ROR","asserted-by":"publisher"}],"name":"Faculty of Innovation Engineering, Macau University of Science and Technology","place":["Taipa, Macau"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3795-1534","authenticated-orcid":false,"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03t1yn780","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3109-6152","authenticated-orcid":false,"given":"Yajing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation, Hefei Third People's Hospital","place":["Hefei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44231-023-00035-6"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/microorganisms11092309"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/23744235.2024.2370965"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.3760\/cma.j.cn112338-20210529-00439"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12985-023-02033-y"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s12276-021-00610-1"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/v13071213"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmi.2015.03.001"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.3892\/ijmm.2022.5174"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmi.2025.02.004"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/v15040940"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00705-024-05968-x"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268820002290"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2023.1307960"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2024.1458670"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiac.2018.07.009"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0024-5"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","unstructured":"Fascia M. 2024. 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