{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T21:17:31Z","timestamp":1768598251222,"version":"3.49.0"},"posted":{"date-parts":[[2025,4,9]]},"group-title":"Oncology","reference-count":51,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Acute Myeloid Leukemia (AML) is a complex and heterogeneous disease identified by severe clinical progression, fast cellular proliferation, and often high mortality rates. Incorporating diverse longitudinal information on patients\u2019 medical histories is essential for developing effective disease predictive models applicable to both research and clinical settings. Here, we present a robust methodology for discovering dynamic predictive models to elucidate AML disease progression dynamics from a novel longitudinal multimodal clinical dataset of patients diagnosed with AML. The clinical dataset was analyzed to reveal the main clinical, genetic, and treatment features modulating disease progression. To discover mathematical models\u2014including interactions, parameters, and nodes\u2014predictive of AML progression, we present an explainable machine learning algorithm based on high-performance evolutionary computation. The results demonstrate that the predictive methodology could accurately estimate the clinical dynamics of AML progression in terms of blast percentages for both training and novel patients. This study demonstrates that the developed explainable machine learning approach can successfully predict AML progression by leveraging the heterogeneous and longitudinal dynamics of patients\u2019 clinical data. More importantly, this methodology shows significant potential for application in modeling the progression dynamics of other acute diseases, providing a flexible and adaptable framework for advancing clinical and translational research.<\/jats:p>","DOI":"10.1101\/2025.04.07.25325267","type":"posted-content","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T18:30:13Z","timestamp":1744223413000},"source":"Crossref","is-referenced-by-count":0,"title":["Discovery of Dynamic Models for AML Disease Progression from Longitudinal Multi-Modal Clinical Data Using Explainable Machine Learning"],"prefix":"10.64898","author":[{"given":"Reza","family":"Mousavi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3289-2333","authenticated-orcid":false,"given":"Moaath K.","family":"Mustafa Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4666-6118","authenticated-orcid":false,"given":"Daniel","family":"Lobo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025041707100758000_2025.04.07.25325267v2.1","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1002\/ajh.27625","article-title":"Acute Myeloid Leukemia: 2025 Update on Diagnosis, Risk-Stratification, and Management","volume":"100","year":"2025","journal-title":"American Journal of Hematology"},{"key":"2025041707100758000_2025.04.07.25325267v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cbi.2009.11.025"},{"key":"2025041707100758000_2025.04.07.25325267v2.3","first-page":"328","article-title":"Acute Myeloid Leukemia-Genetic Alterations and Their Clinical Prognosis","volume":"11","year":"2017","journal-title":"Int J Hematol Oncol Stem Cell Res"},{"key":"2025041707100758000_2025.04.07.25325267v2.4","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1406184"},{"key":"2025041707100758000_2025.04.07.25325267v2.5","first-page":"1","article-title":"Current status and research directions in acute myeloid leukemia","volume":"14","year":"2024","journal-title":"Blood Cancer J"},{"key":"2025041707100758000_2025.04.07.25325267v2.6","doi-asserted-by":"crossref","first-page":"e441","DOI":"10.1038\/bcj.2016.50","article-title":"Acute myeloid leukemia: a comprehensive review and 2016 update","volume":"6","year":"2016","journal-title":"Blood Cancer Journal"},{"key":"2025041707100758000_2025.04.07.25325267v2.7","first-page":"e2300229","article-title":"Burden of AML, 1990-2019: Estimates From the Global Burden of Disease Study","volume":"9","year":"2023","journal-title":"JCO Glob Oncol"},{"key":"2025041707100758000_2025.04.07.25325267v2.8","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21763"},{"key":"2025041707100758000_2025.04.07.25325267v2.9","doi-asserted-by":"crossref","first-page":"103798","DOI":"10.1016\/j.isci.2022.103798","article-title":"Machine learning for multi-omics data integration in cancer","volume":"25","year":"2022","journal-title":"iScience"},{"key":"2025041707100758000_2025.04.07.25325267v2.10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s10020-024-01036-x","article-title":"Multi-omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study","volume":"31","year":"2025","journal-title":"Mol Med"},{"key":"2025041707100758000_2025.04.07.25325267v2.11","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1038\/s41598-025-85659-y","article-title":"Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort","volume":"15","year":"2025","journal-title":"Sci Rep"},{"key":"2025041707100758000_2025.04.07.25325267v2.12","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1186\/s12885-024-13320-4","article-title":"Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review","volume":"24","year":"2024","journal-title":"BMC Cancer"},{"key":"2025041707100758000_2025.04.07.25325267v2.13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3390\/bioengineering10020173","article-title":"Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review","volume":"10","year":"2023","journal-title":"Bioengineering"},{"key":"2025041707100758000_2025.04.07.25325267v2.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2023.01.035"},{"key":"2025041707100758000_2025.04.07.25325267v2.15","doi-asserted-by":"crossref","unstructured":"Kurnat-Thoma, E.L. , Nogales, C. and Vasudevan, S . 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