Harvard data science review最新文献

{"title":"Expanding the Data Science Toolkit: Mixed Methods for Improving Causal Inference.","authors":"Noor Qaragholi, Elizabeth A Stuart","doi":"10.1162/99608f92.2eb6655b","DOIUrl":"10.1162/99608f92.2eb6655b","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"7 4","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12674192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Univariate-Guided Sparse Regression.","authors":"Sourav Chatterjee, Trevor Hastie, Robert Tibshirani","doi":"10.1162/99608f92.c79ff6db","DOIUrl":"10.1162/99608f92.c79ff6db","url":null,"abstract":"<p><p>In this article, we introduce \"uniLasso,\" a novel statistical method for regression. This two-stage approach preserves the signs of the univariate coefficients and leverages their magnitude. Both of these properties are attractive for stability and interpretation of the model. Through comprehensive simulations and applications to real-world data sets, we demonstrate that uniLasso outperforms lasso in various settings, particularly in terms of sparsity and model interpretability. We prove asymptotic support recovery and mean-squared error consistency under a set of conditions different from the well-known irrepresentability conditions for the lasso. Extensions to generalized linear models (GLMs) and Cox regression are also discussed.</p>","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"7 3","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistics and AI - A Fireside Conversation.","authors":"Xihong Lin, Tianxi Cai, David Donoho, Haoda Fu, Tracy Ke, Jiashun Jin, Xiao-Li Meng, Annie Qu, Chengchun Shi, Peter Song, Qiang Sun, Wenyi Wang, Hulin Wu, Bin Yu, Heping Zhang, Tian Zheng, Harrison Zhou, Jin Zhou, Hongtu Zhu, Ji Zhu","doi":"10.1162/99608f92.c066fe9c","DOIUrl":"10.1162/99608f92.c066fe9c","url":null,"abstract":"<p><p>A three-hour webinar titled \"Statistics and AI - A Fireside Conversation\" was held on Sunday, March 17th, 2024, attracting an online audience of approximately 1,000 attendees. The event featured three sessions aimed at engaging the statistical community on key challenges and opportunities in the AI era: addressing statistical challenges and opportunities (Panel I), evolving the publication process (Panel II), and advancing next-generation statistical pipelines and resources (Panel III). Panel I examined issues such as dwindling talent, shifting funding landscapes, and AI's rapid rise, highlighting the need for statistical rigor, interdisciplinary collaboration, and innovative approaches to shape the future of AI. Panel II emphasized the importance of streamlining the publication process, fostering impactful research, and prioritizing workflows and data quality. Panel III focused on modernizing statistical education by integrating AI and deep learning, promoting interdisciplinary collaboration, and maintaining foundational principles such as uncertainty and reproducibility. These discussions collectively outlined a strategic roadmap for ensuring the relevance and advancement of statistics in the age of AI.</p><p><strong>Organizers in alphabetical order: </strong>Xihong Lin (Harvard University), Tracey Ke (Harvard University), Tian Zheng (Columbia University), Jing Zhou (University of California at Los Angeles), and Hongtu Zhu (University of North Carolina at Chapel Hill).</p><p><strong>Moderators: </strong>Xihong Lin (Harvard University), Heping Zhang (Yale University), and Peter Song (University of Michigan).</p><p><strong>Panelists in alphabetical order: </strong>Tianxi Cai (Harvard University), Haoda Fu (Amgen), Tracy Ke (Harvard University), Jiashun Jin (Carnegie Mellon University), Xiao-Li Meng (Harvard University), Annie Qu (University of California at Irvine), Chengchun Shi (London School of Economics and Political Science), Qiang Sun (University of Toronto and MBZUAI), Wenyi Wang (MD Anderson Cancer Center), Hulin Wu (University of Texas at Houston), Bin Yu (University of California at Berkeley), Harrison Zhou (Yale University), Hongtu Zhu (University of North Carolina at Chapel Hill), and Ji Zhu (University of Michigan).</p><p><strong>Discussant: </strong>David Donoho (Stanford University).</p>","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"7 2","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13271656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.","authors":"Yuming Sun, Stephen Salerno, Ziyang Pan, Eileen Yang, Chinakorn Sujimongkol, Jiyeon Song, Xinan Wang, Peisong Han, Donglin Zeng, Jian Kang, David C Christiani, Yi Li","doi":"10.1162/99608f92.9d86a749","DOIUrl":"10.1162/99608f92.9d86a749","url":null,"abstract":"<p><p>Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts.</p>","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11225107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rejoinder: Building a Paradigm That Allows for the Possibility of Non-Ignorable Nonresponse","authors":"Michael A. Bailey","doi":"10.1162/99608f92.4187b1b4","DOIUrl":"https://doi.org/10.1162/99608f92.4187b1b4","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":" 117","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Close to Refuge: Integrating AI and Human Insights for Intervention and Prevention: A Conversation With Seema Iyer","authors":"Seema Iyer, Xiao-Li Meng, Liberty Vittert","doi":"10.1162/99608f92.1a58d824","DOIUrl":"https://doi.org/10.1162/99608f92.1a58d824","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"15 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Intelligence and Rationality of AI and Humans: A Conversation With Steven Pinker","authors":"Steven Pinker, Xiao-Li Meng, Liberty Vittert","doi":"10.1162/99608f92.c37d3572","DOIUrl":"https://doi.org/10.1162/99608f92.c37d3572","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"151 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crisis? What Crisis? Sociology’s Slow Progress Toward Scientific Transparency","authors":"Kim A. Weeden","doi":"10.1162/99608f92.151c41e3","DOIUrl":"https://doi.org/10.1162/99608f92.151c41e3","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"122 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136317015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demonstrations of the Potential of AI-based Political Issue Polling","authors":"Nathan E. Sanders, Alex Ulinich, Bruce Schneier","doi":"10.1162/99608f92.1d3cf75d","DOIUrl":"https://doi.org/10.1162/99608f92.1d3cf75d","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"146 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding and Fostering Regional Artificial Intelligence Ecosystems: A Case Study in Maine","authors":"Hamit Hamutcu, Usama Fayyad, Michael Pollastri","doi":"10.1162/99608f92.dc2a1d1b","DOIUrl":"https://doi.org/10.1162/99608f92.dc2a1d1b","url":null,"abstract":"","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136316714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}