{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T02:19:12Z","timestamp":1773973152631,"version":"3.50.1"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100018555","name":"Science and Technology Program of Guizhou Province","doi-asserted-by":"publisher","award":["ZK2023-297"],"award-info":[{"award-number":["ZK2023-297"]}],"id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072212"],"award-info":[{"award-number":["62072212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017957","name":"Health Commission of Guizhou Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009091","name":"Justus Liebig Universit\u00e4t Gie\u00dfen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009091","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.patcog.2025.112970","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T16:32:48Z","timestamp":1766593968000},"page":"112970","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DeepSelective: Interpretable prognosis prediction via feature selection and compression in EHR data"],"prefix":"10.1016","volume":"174","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6541-4050","authenticated-orcid":false,"given":"Ruochi","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8429-941X","authenticated-orcid":false,"given":"Qian","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8471-4670","authenticated-orcid":false,"given":"Xiaoyang","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-143X","authenticated-orcid":false,"given":"Tian","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9113-0656","authenticated-orcid":false,"given":"Qiong","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4947-5672","authenticated-orcid":false,"given":"Ziqi","family":"Deng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8167-567X","authenticated-orcid":false,"given":"Kewei","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1219-5987","authenticated-orcid":false,"given":"Yueying","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2943-2221","authenticated-orcid":false,"given":"Yusi","family":"Fan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9363-0885","authenticated-orcid":false,"given":"Jiale","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-3777","authenticated-orcid":false,"given":"Lan","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3570-9154","authenticated-orcid":false,"given":"Chang","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8108-6007","authenticated-orcid":false,"given":"Fengfeng","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.patcog.2025.112970_bib0001","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10462-024-10876-2","article-title":"A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction","volume":"57","author":"Nasarudin","year":"2024","journal-title":"Artif. Intell. Rev."},{"issue":"8","key":"10.1016\/j.patcog.2025.112970_bib0002","doi-asserted-by":"crossref","first-page":"931","DOI":"10.59613\/3r1hwy26","article-title":"Analysis of the impact of electronic health record use on the effectiveness of diagnostic and treatment processes","volume":"1","author":"Mulyani","year":"2024","journal-title":"J. Acad. Sci."},{"issue":"6","key":"10.1016\/j.patcog.2025.112970_bib0003","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1001\/jama.2024.24598","article-title":"Recommendations to ensure safety of AI in real-world clinical care","volume":"333","author":"Sittig","year":"2025","journal-title":"JAMA"},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0004","article-title":"Design and development of data-driven AI to reduce the discrepancies in healthcare EHR utilization","volume":"5","author":"Karaferis","year":"2025","journal-title":"J. Clin. Med. Re: AJCMR-184"},{"key":"10.1016\/j.patcog.2025.112970_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111603","article-title":"Brain anatomy prior modeling to forecast clinical progression of cognitive impairment with structural MRI","volume":"165","author":"Zhang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112970_bib0006","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/978-3-319-98779-8_17","article-title":"Advancing clinical research through natural language processing on electronic health records: traditional machine learning meets deep learning","author":"Liu","year":"2019","journal-title":"Clin. Res. Inform."},{"issue":"6","key":"10.1016\/j.patcog.2025.112970_bib0007","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: review, opportunities and challenges","volume":"19","author":"Miotto","year":"2018","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0008","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1111\/biom.12987","article-title":"Automated feature selection of predictors in electronic medical records data","volume":"75","author":"Gronsbell","year":"2019","journal-title":"Biometrics"},{"key":"10.1016\/j.patcog.2025.112970_bib0009","article-title":"Enrank: an ensemble method to detect pulmonary hypertension biomarkers based on feature selection and machine learning models","volume":"12","author":"Liu","year":"2021","journal-title":"Front. Genet."},{"issue":"e2","key":"10.1016\/j.patcog.2025.112970_bib0010","doi-asserted-by":"crossref","first-page":"e206","DOI":"10.1136\/amiajnl-2013-002428","article-title":"Electronic health records-driven phenotyping: challenges, recent advances, and perspectives","volume":"20","author":"Pathak","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.patcog.2025.112970_bib0011","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"825","article-title":"Adacare: explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration","volume":"34","author":"Ma","year":"2020"},{"key":"10.1016\/j.patcog.2025.112970_bib0012","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4567","article-title":"Context-aware health event prediction via transition functions on dynamic disease graphs","volume":"36","author":"Lu","year":"2022"},{"key":"10.1016\/j.patcog.2025.112970_bib0013","series-title":"IJCAI","first-page":"4921","article-title":"VecoCare: visit sequences-clinical notes joint learning for diagnosis prediction in healthcare data","volume":"23","author":"Xu","year":"2023"},{"key":"10.1016\/j.patcog.2025.112970_bib0014","first-page":"54772","article-title":"Instruction tuning large language models to understand electronic health records","volume":"37","author":"Wu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2025.112970_bib0015","doi-asserted-by":"crossref","first-page":"154096","DOI":"10.1109\/ACCESS.2019.2949286","article-title":"Black-box vs. white-box: understanding their advantages and weaknesses from a practical point of view","volume":"7","author":"Loyola-Gonzalez","year":"2019","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0016","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s12911-024-02453-y","article-title":"Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"12","key":"10.1016\/j.patcog.2025.112970_bib0017","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1007\/s10115-022-01756-8","article-title":"Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond","volume":"64","author":"Li","year":"2022","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.patcog.2025.112970_bib0018","series-title":"Proceedings of the 1st Workshop on Deep Learning for Recommender Systems","first-page":"7","article-title":"Wide & deep learning for recommender systems","author":"Cheng","year":"2016"},{"key":"10.1016\/j.patcog.2025.112970_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106202","article-title":"Supervised feature selection through deep neural networks with pairwise connected structure","volume":"204","author":"Huang","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.patcog.2025.112970_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110412","article-title":"ISP-IRLNet: joint optimization of interpretable sampler and implicit regularization learning network for accerlerated MRI","volume":"151","author":"Li","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112970_bib0021","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2025.112970_sbref0022","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Categorical reparameterization with Gumbel-softmax","author":"Jang","year":"2017"},{"issue":"10","key":"10.1016\/j.patcog.2025.112970_bib0023","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.3390\/math11102375","article-title":"The geometry of feature space in deep learning models: a holistic perspective and comprehensive review","volume":"11","author":"Lee","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.patcog.2025.112970_bib0024","series-title":"Technical Report","article-title":"The Origins of Logistic Regression","author":"Cramer","year":"2002"},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0025","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.patcog.2025.112970_bib0026","first-page":"3512","article-title":"Retain: an interpretable predictive model for healthcare using reverse time attention mechanism","volume":"29","author":"Choi","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2025.112970_bib0027","series-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"65","article-title":"Patient subtyping via time-aware LSTM networks","author":"Baytas","year":"2017"},{"key":"10.1016\/j.patcog.2025.112970_bib0028","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Attend and diagnose: clinical time series analysis using attention models","volume":"32","author":"Song","year":"2018"},{"key":"10.1016\/j.patcog.2025.112970_bib0029","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"833","article-title":"Concare: personalized clinical feature embedding via capturing the healthcare context","volume":"34","author":"Ma","year":"2020"},{"key":"10.1016\/j.patcog.2025.112970_bib0030","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"715","article-title":"GRASP: generic framework for health status representation learning based on incorporating knowledge from similar patients","volume":"35","author":"Zhang","year":"2021"},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0031","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/JBHI.2023.3327951","article-title":"GENHPF: general healthcare predictive framework for multi-task multi-source learning","volume":"28","author":"Hur","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.patcog.2025.112970_bib0032","unstructured":"J. Kim, C. Shim, B.S.K. Yang, C. Im, S.Y. Lim, H.-G. Jeong, E. Choi, General-purpose retrieval-enhanced medical prediction model using near-infinite history, (2023). arXiv: 2310.20204."},{"key":"10.1016\/j.patcog.2025.112970_bib0033","series-title":"Computers in Cardiology 1996","first-page":"657","article-title":"A database to support development and evaluation of intelligent intensive care monitoring","author":"Moody","year":"1996"},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0034","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"Harutyunyan","year":"2019","journal-title":"Sci. Data"},{"issue":"3","key":"10.1016\/j.patcog.2025.112970_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103682","article-title":"FairCare: adversarial training of a heterogeneous graph neural network with attention mechanism to learn fair representations of electronic health records","volume":"61","author":"Wang","year":"2024","journal-title":"Inform. Process. Manage."},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0036","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci. Data"},{"issue":"6","key":"10.1016\/j.patcog.2025.112970_bib0037","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","author":"Kraskov","year":"2004","journal-title":"Phys. Rev. E\u2014Stat. Nonlinear Soft Matter Phys."},{"issue":"1","key":"10.1016\/j.patcog.2025.112970_bib0038","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-017-13259-6","article-title":"RIFS: a randomly restarted incremental feature selection algorithm","volume":"7","author":"Ye","year":"2017","journal-title":"Sci. Rep."},{"issue":"1\u20133","key":"10.1016\/j.patcog.2025.112970_bib0039","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320325016334?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320325016334?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T01:36:48Z","timestamp":1773970608000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320325016334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":39,"alternative-id":["S0031320325016334"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2025.112970","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DeepSelective: Interpretable prognosis prediction via feature selection and compression in EHR data","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2025.112970","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112970"}}