Cédric Beaulac, Jeffrey S Rosenthal, Qinglin Pei, Debra Friedman, Suzanne Wolden, David Hodgson
{"title":"An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group.","authors":"Cédric Beaulac, Jeffrey S Rosenthal, Qinglin Pei, Debra Friedman, Suzanne Wolden, David Hodgson","doi":"10.1080/08839514.2020.1815151","DOIUrl":"10.1080/08839514.2020.1815151","url":null,"abstract":"<p><p>In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence.</p>","PeriodicalId":8260,"journal":{"name":"Applied Artificial Intelligence","volume":"34 14","pages":"1100-1114"},"PeriodicalIF":2.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963212/pdf/nihms-1637283.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25488529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chandrima Sarkar, Sarah Cooley, Jaideep Srivastava
{"title":"Robust Feature Selection Technique using Rank Aggregation.","authors":"Chandrima Sarkar, Sarah Cooley, Jaideep Srivastava","doi":"10.1080/08839514.2014.883903","DOIUrl":"https://doi.org/10.1080/08839514.2014.883903","url":null,"abstract":"<p><p>Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases while classifiers exploit different statistical properties of data for evaluation. In numerous situations this can put researchers into dilemma as to which feature selection method and a classifiers to choose from a vast range of choices. In this paper, we propose a technique that aggregates the consensus properties of various feature selection methods to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable towards achieving similar and ideally higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the Robustness Index (RI). We perform an extensive empirical evaluation of our technique on eight data sets with different dimensions including Arrythmia, Lung Cancer, Madelon, mfeat-fourier, internet-ads, Leukemia-3c and Embryonal Tumor and a real world data set namely Acute Myeloid Leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that compared to other techniques our algorithm improves the classification accuracy by approximately 3-4% (in data set with less than 500 features) and by more than 5% (in data set with more than 500 features), across a wide range of classifiers.</p>","PeriodicalId":8260,"journal":{"name":"Applied Artificial Intelligence","volume":"28 3","pages":"243-257"},"PeriodicalIF":2.8,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08839514.2014.883903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32349731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Maintaining Engagement in Long-term Interventions with Relational Agents.","authors":"Timothy Bickmore, Daniel Schulman, Langxuan Yin","doi":"10.1080/08839514.2010.492259","DOIUrl":"https://doi.org/10.1080/08839514.2010.492259","url":null,"abstract":"<p><p>We discuss issues in designing virtual humans for applications which require long-term voluntary use, and the problem of maintaining engagement with users over time. Concepts and theories related to engagement from a variety of disciplines are reviewed. We describe a platform for conducting studies into long-term interactions between humans and virtual agents, and present the results of two longitudinal randomized controlled experiments in which the effect of manipulations of agent behavior on user engagement was assessed.</p>","PeriodicalId":8260,"journal":{"name":"Applied Artificial Intelligence","volume":"24 6","pages":"648-666"},"PeriodicalIF":2.8,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/08839514.2010.492259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29669802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}