{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:34:56Z","timestamp":1792805696559,"version":"4.5.0"},"reference-count":81,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Georgia Institute of Technology President\u2019s Undergraduate Research Award","award":["R21CA232249"],"award-info":[{"award-number":["R21CA232249"]}],"id":[{"id":"https:\/\/ror.org\/01zkghx44","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Georgia Institute of Technology President\u2019s Undergraduate Research Award","award":["1944247"],"award-info":[{"award-number":["1944247"]}],"id":[{"id":"https:\/\/ror.org\/01zkghx44","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Georgia Institute of Technology President\u2019s Undergraduate Research Award","award":["253558"],"award-info":[{"award-number":["253558"]}],"id":[{"id":"https:\/\/ror.org\/01zkghx44","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R21CA232249"],"award-info":[{"award-number":["R21CA232249"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1944247"],"award-info":[{"award-number":["1944247"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["253558"],"award-info":[{"award-number":["253558"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Aflac Cancer and Blood Disorders Center, Children\u2019s Healthcare of Atlanta","award":["R21CA232249"],"award-info":[{"award-number":["R21CA232249"]}],"id":[{"id":"https:\/\/ror.org\/050fhx250","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Aflac Cancer and Blood Disorders Center, Children\u2019s Healthcare of Atlanta","award":["1944247"],"award-info":[{"award-number":["1944247"]}],"id":[{"id":"https:\/\/ror.org\/050fhx250","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Aflac Cancer and Blood Disorders Center, Children\u2019s Healthcare of Atlanta","award":["253558"],"award-info":[{"award-number":["253558"]}],"id":[{"id":"https:\/\/ror.org\/050fhx250","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation CAREER award","doi-asserted-by":"publisher","award":["R21CA232249"],"award-info":[{"award-number":["R21CA232249"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation CAREER award","doi-asserted-by":"publisher","award":["1944247"],"award-info":[{"award-number":["1944247"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation CAREER award","doi-asserted-by":"publisher","award":["253558"],"award-info":[{"award-number":["253558"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","award":["R21CA232249"],"award-info":[{"award-number":["R21CA232249"]}],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","award":["1944247"],"award-info":[{"award-number":["1944247"]}],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","award":["253558"],"award-info":[{"award-number":["253558"]}],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either \u201chigh risk\u201d or \u201clow risk\u201d in pediatric ALL (n = 580) and AML (n = 132) with accuracy of \u223c79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.<\/jats:p>","DOI":"10.3390\/jcm13061788","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T09:14:33Z","timestamp":1710926073000},"page":"1788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2969-7019","authenticated-orcid":false,"given":"Irfan","family":"Al-Hussaini","sequence":"first","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8070-9778","authenticated-orcid":false,"given":"Brandon","family":"White","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armon","family":"Varmeziar","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nidhi","family":"Mehra","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milagro","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judy","family":"Lee","sequence":"additional","affiliation":[{"name":"Aflac Cancer and Blood Disorders Center, Children\u2019s Healthcare of Atlanta, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas P.","family":"DeGroote","sequence":"additional","affiliation":[{"name":"Aflac Cancer and Blood Disorders Center, Children\u2019s Healthcare of Atlanta, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamara P.","family":"Miller","sequence":"additional","affiliation":[{"name":"Aflac Cancer and Blood Disorders Center, Children\u2019s Healthcare of Atlanta, Atlanta, GA 30322, USA"},{"name":"Department of Pediatrics, Division of Pediatric Hematology\/Oncology, Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5472-6355","authenticated-orcid":false,"given":"Cassie S.","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Machine Learning Center at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3390\/jcm4010127","article-title":"Pediatric AML: From biology to clinical management","volume":"4","author":"Zwaan","year":"2015","journal-title":"J. Clin. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e28673","DOI":"10.1002\/pbc.28673","article-title":"Systemic viral infection in children receiving chemotherapy for acute leukemia","volume":"67","author":"Bochennek","year":"2020","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1093\/annonc\/mdw557","article-title":"Infection-related complications during treatment for childhood acute lymphoblastic leukemia","volume":"28","author":"Inaba","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_4","first-page":"31","article-title":"Lymphocyte functions of child patients with ALL (acute lymphoblastic leukemia) in remission","volume":"14","author":"Suzuki","year":"1984","journal-title":"Jpn. J. Clin. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/08880010151114741","article-title":"Immunoglobulin class and subclass concentrations after treatment of childhood leukemia","volume":"18","author":"Kristinsson","year":"2001","journal-title":"Pediatr. Hematol. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1002\/pbc.23258","article-title":"Impact of treatment reduction for childhood acute lymphoblastic leukemia on serum immunoglobulins and antibodies against vaccine-preventable diseases","volume":"58","author":"Bierings","year":"2012","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1080\/00365540310016600","article-title":"Impact of conventional chemotherapy on levels of antibodies against vaccine-preventable diseases in children treated for cancer","volume":"35","author":"Reinhardt","year":"2003","journal-title":"Scand. J. Infect. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1097\/MPH.0000000000000548","article-title":"Early post-therapy hospitalizations among survivors of childhood leukemia and lymphoma","volume":"38","author":"Smitherman","year":"2016","journal-title":"J. Pediatr. Hematol."},{"key":"ref_9","first-page":"1056","article-title":"Infection-related mortality in children with acute lymphoblastic leukemia: An analysis of infectious deaths on UKALL2003","volume":"124","author":"Bate","year":"2014","journal-title":"Blood J. Am. Soc. Hematol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e26952","DOI":"10.1002\/pbc.26952","article-title":"Effectiveness of antibacterial prophylaxis during induction chemotherapy in children with acute lymphoblastic leukemia","volume":"65","author":"Sulis","year":"2018","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1002\/pbc.22423","article-title":"Infections during induction therapy for children with acute lymphoblastic leukemia. the role of sulfamethoxazole-trimethoprim (SMX-TMP) prophylaxis","volume":"55","author":"Rungoe","year":"2010","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s11912-022-01192-5","article-title":"Neutropenia and Infection Prophylaxis in Childhood Cancer","volume":"24","author":"Villeneuve","year":"2022","journal-title":"Curr. Oncol. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1001\/jamaoncol.2017.5449","article-title":"Not just digital pathology, intelligent digital pathology","volume":"4","author":"Acs","year":"2018","journal-title":"JAMA Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","article-title":"How machine learning will transform biomedicine","volume":"181","author":"Goecks","year":"2020","journal-title":"Cell"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1007\/s11517-020-02245-2","article-title":"Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach","volume":"58","author":"Mahmood","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1111\/ijlh.13089","article-title":"Machine learning applications in the diagnosis of leukemia: Current trends and future directions","volume":"41","author":"Salah","year":"2019","journal-title":"Int. J. Lab. Hematol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1177\/0954411920938567","article-title":"Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning","volume":"234","author":"Fathi","year":"2020","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100780","DOI":"10.1016\/j.isci.2019.100780","article-title":"Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics","volume":"23","author":"Perrakis","year":"2020","journal-title":"Iscience"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100399","DOI":"10.1016\/j.imu.2020.100399","article-title":"Treatment outcome classification of pediatric Acute Lymphoblastic Leukemia patients with clinical and medical data using machine learning: A case study at MAHAK hospital","volume":"20","author":"Kashef","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1176935119835544","DOI":"10.1177\/1176935119835544","article-title":"Predicting complete remission of acute myeloid leukemia: Machine learning applied to gene expression","volume":"18","author":"Gal","year":"2019","journal-title":"Cancer Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s41467-017-02465-5","article-title":"A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia","volume":"9","author":"Lee","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1016\/j.csbj.2023.03.046","article-title":"Machine learning in metastatic cancer research: Potentials, possibilities, and prospects","volume":"21","author":"Petinrin","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1200\/CCI.21.00102","article-title":"Applications of artificial intelligence in pediatric oncology: A systematic review","volume":"5","author":"Ramesh","year":"2021","journal-title":"JCO Clin. Cancer Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13023-017-0755-5","article-title":"An overview of the impact of rare disease characteristics on research methodology","volume":"13","author":"Whicher","year":"2018","journal-title":"Orphanet J. Rare Dis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10994-017-5640-x","article-title":"Learning constraints in spreadsheets and tabular data","volume":"106","author":"Kolb","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","article-title":"The false hope of current approaches to explainable artificial intelligence in health care","volume":"3","author":"Ghassemi","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e1379","DOI":"10.1002\/widm.1379","article-title":"Interpretability of machine learning-based prediction models in healthcare","volume":"10","author":"Stiglic","year":"2020","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_28","unstructured":"Al-Hussaini, I., and Mitchell, C.S. (2023). Performance and utility trade-off in interpretable sleep staging. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Arik, S.\u00d6., and Pfister, T. (2021, January 2\u20139). Tabnet: Attentive interpretable tabular learning. Proceedings of the AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"ref_30","first-page":"721","article-title":"SLEEPER: Interpretable Sleep staging via Prototypes from Expert Rules","volume":"106","author":"Xiao","year":"2019","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1145\/3359786","article-title":"Techniques for Interpretable Machine Learning","volume":"63","author":"Du","year":"2019","journal-title":"Commun. ACM"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2522","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_33","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lundberg, S.M., Nair, B., Vavilala, M.S., Horibe, M., Eisses, M.J., Adams, T., Liston, D.E., Low, D.K.W., Newman, S.F., and Kim, J. (2018). Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng., 2.","DOI":"10.1101\/206540"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_36","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012003","DOI":"10.1088\/1742-6596\/1230\/1\/012003","article-title":"Improvement of K-Means Performance Using a Combination of Principal Component Analysis and Rapid Centroid Estimation","volume":"1230","author":"Sapriadi","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Prakash, J., Wang, V., Quinn, R.E., and Mitchell, C.S. (2021). Unsupervised Machine Learning to Identify Separable Clinical Alzheimer\u2019s Disease Sub-Populations. Brain Sci., 11.","DOI":"10.3390\/brainsci11080977"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, A., Onyeze, C., Kartchner, D., Allegri, S., Nakajima An, D., McCoy, K., Davalbhakta, E., and Mitchell, C.S. (2022). Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6010027"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"McCoy, K., Gudapati, S., He, L., Horlander, E., Kartchner, D., Kulkarni, S., Mehra, N., Prakash, J., Thenot, H., and Vanga, S.V. (2021). Biomedical Text Link Prediction for Drug Discovery: A Case Study with COVID-19. Pharmaceutics, 13.","DOI":"10.3390\/pharmaceutics13060794"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kartchner, D., McCoy, K., Dubey, J., Zhang, D., Zheng, K., Umrani, R., Kim, J.J., and Mitchell, C.S. (2023). Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19. Biology, 12.","DOI":"10.3390\/biology12091269"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mehra, N., Varmeziar, A., Chen, X., Kronick, O., Fisher, R., Kota, V., and Mitchell, C.S. (2022). Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia. Cancers, 14.","DOI":"10.3390\/cancers14194686"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tandra, G., Yoone, A., Mathew, R., Wang, M., Hales, C.M., and Mitchell, C.S. (2023). Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson\u2019s Disease. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms241512339"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e678","DOI":"10.1016\/S2352-3026(22)00168-5","article-title":"Rates of laboratory adverse events by course in paediatric leukaemia ascertained with automated electronic health record extraction: A retrospective cohort study from the Children\u2019s Oncology Group","volume":"9","author":"Miller","year":"2022","journal-title":"Lancet Haematol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1080\/10428194.2020.1852471","article-title":"Quantifying the difference in risk of adverse events by induction treatment regimen in pediatric acute lymphoblastic leukemia","volume":"62","author":"West","year":"2021","journal-title":"Leuk. Lymphoma"},{"key":"ref_46","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv."},{"key":"ref_47","unstructured":"Dorogush, A.V., Gulin, A., Gusev, G., Kazeev, N., Prokhorenkova, L.O., and Vorobev, A. (2017). Fighting biases with dynamic boosting. arXiv."},{"key":"ref_48","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_49","unstructured":"Meng, Q., Ke, G., Wang, T., Chen, W., Ye, Q., Ma, Z.M., and Liu, T.Y. (2016, January 5\u201310). A communication-efficient parallel algorithm for decision tree. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_50","unstructured":"Zhang, H., Si, S., and Hsieh, C.J. (2017). GPU-acceleration for Large-scale Tree Boosting. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA. KDD\u201916.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_52","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_54","unstructured":"Rasmussen, C.E. (2003, January 4\u201316). Gaussian processes in machine learning. Proceedings of the Summer School on Machine Learning, T\u00fcbingen, Germany."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_56","unstructured":"Agrawal, R., and Srikant, R. (1994, January 12\u201315). Fast algorithms for mining association rules. Proceedings of the 20th International Conference Very Large Data Bases, VLDB, Santiago, Chile."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1023\/B:DAMI.0000005258.31418.83","article-title":"Mining frequent patterns without candidate generation: A frequent-pattern tree approach","volume":"8","author":"Han","year":"2004","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1080\/10428194.2019.1573369","article-title":"Incidence of infections after therapy completion in children with acute lymphoblastic leukemia or acute myeloid leukemia: A systematic review of the literature","volume":"60","author":"Hwee","year":"2019","journal-title":"Leuk. Lymphoma"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Al-Hussaini, I., and Mitchell, C.S. (2023). SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables. Bioengineering, 10.","DOI":"10.3390\/bioengineering10080918"},{"key":"ref_60","first-page":"e46837","article-title":"Assessment of the Prevalence of Infections in Pediatric Patients With Acute Lymphoblastic Leukemia","volume":"15","author":"Nair","year":"2023","journal-title":"Cureus"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hagberg, A., Swart, P., and S Chult, D. (2008). Exploring Network Structure, Dynamics, and Function Using NetworkX, Technical Report.","DOI":"10.25080\/TCWV9851"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sedler, A.R., and Mitchell, C.S. (2019). SemNet: Using local features to navigate the biomedical concept graph. Front. Bioeng. Biotechnol., 7.","DOI":"10.3389\/fbioe.2019.00156"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e154","DOI":"10.1097\/MPH.0000000000002619","article-title":"Hyperglycemia and Other Glycemic Measures Throughout Therapy for Pediatric Acute Lymphoblastic Leukemia and Lymphoma","volume":"45","author":"Demedis","year":"2023","journal-title":"J. Pediatr. Hematol.\/Oncol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e29937","DOI":"10.1002\/pbc.29937","article-title":"Improving infectious adverse event reporting for children and adolescents enrolled in clinical trials for acute lymphoblastic leukemia: A report from the Children\u2019s Oncology Group","volume":"69","author":"Elgarten","year":"2022","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5581\/1516-8484.20130013","article-title":"Hyperglycemia increases the complicated infection and mortality rates during induction therapy in adult acute leukemia patients","volume":"35","author":"Matias","year":"2013","journal-title":"Rev. Bras. Hematol. Hemoter."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ejon.2014.08.005","article-title":"Prevalence and impact of hyperglycemia on hospitalized leukemia patients","volume":"19","author":"Storey","year":"2015","journal-title":"Eur. J. Oncol. Nurs."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1097\/MPH.0000000000002494","article-title":"Drug Induced Diabetes Mellitus in Pediatric Acute Lymphoblastic Leukemia: Approach to Diagnosis and Management","volume":"44","author":"Handattu","year":"2022","journal-title":"J. Pediatr. Hematol. Oncol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1038\/ejcn.2013.146","article-title":"Zinc supplementation in children and adolescents with acute leukemia","volume":"67","author":"Consolo","year":"2013","journal-title":"Eur. J. Clin. Nutr."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"627662","DOI":"10.3389\/fimmu.2020.627662","article-title":"The Clinical Significance of Iron Overload and Iron Metabolism in Myelodysplastic Syndrome and Acute Myeloid Leukemia","volume":"11","author":"Weber","year":"2020","journal-title":"Front. Immunol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e27701","DOI":"10.1002\/pbc.27701","article-title":"Use of granulocyte colony-stimulating factor and risk of relapse in pediatric patients treated for acute myeloid leukemia according to NOPHO-AML 2004 and DB AML-01","volume":"66","author":"Asdahl","year":"2019","journal-title":"Pediatr. Blood Cancer"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/978-1-4419-7073-2_19","article-title":"The hematopoietic growth factors in acute leukemia: A European perspective","volume":"157","author":"Heuser","year":"2011","journal-title":"Cancer Treat. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1610096","DOI":"10.3389\/pore.2022.1610096","article-title":"Co-detection of VEGF-A and its regulator, microRNA-181a, may indicate central nervous system involvement in pediatric leukemia","volume":"28","author":"Egyed","year":"2022","journal-title":"Pathol. Oncol. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13075-021-02692-8","article-title":"Hematological malignancies in systemic lupus erythematosus: Clinical characteristics, risk factors, and prognosis-a case-control study","volume":"24","author":"Zhang","year":"2022","journal-title":"Arthritis Res. Ther."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1002\/gepi.22242","article-title":"Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes","volume":"43","author":"Din","year":"2019","journal-title":"Genet. Epidemiol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.3899\/jrheum.170179","article-title":"Malignancy in pediatric-onset systemic lupus erythematosus","volume":"44","author":"Bernatsky","year":"2017","journal-title":"J. Rheumatol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1001\/jamaoncol.2023.2163","article-title":"Management of down syndrome-associated leukemias: A review","volume":"9","author":"Verma","year":"2023","journal-title":"JAMA Oncol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"83767","DOI":"10.18632\/oncotarget.11765","article-title":"Impact of age on the survival of pediatric leukemia: An analysis of 15083 children in the SEER database","volume":"7","author":"Wang","year":"2016","journal-title":"Oncotarget"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Al-Hussaini, I., and Mitchell, C.S. (2023, January 4\u201310). Towards Interpretable Seizure Detection Using Wearables. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10097091"},{"key":"ref_79","unstructured":"Frangi, A., de Bruijne, M., Wassermann, D., and Navab, N. (2023, January 18\u201323). sEBM: Scaling Event Based Models to Predict Disease Progression via Implicit Biomarker Selection and Clustering. Proceedings of the Information Processing in Medical Imaging, San Carlos de Bariloche, Argentina."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ebbehoj, A., Thunbo, M.\u00d8., Andersen, O.E., Glindtvad, M.V., and Hulman, A. (2022). Transfer learning for non-image data in clinical research: A scoping review. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000014"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kartchner, D., Al-Hussaini, I., Turner, H., Deng, J., Lohiya, S., Bathala, P., and Mitchell, C. (2023, January 23\u201327). BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA. SIGIR\u201923.","DOI":"10.1145\/3539618.3591897"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/6\/1788\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:16:41Z","timestamp":1760105801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/6\/1788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,20]]},"references-count":81,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["jcm13061788"],"URL":"https:\/\/doi.org\/10.3390\/jcm13061788","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,20]]}}}