[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116965-en":3,"doc-seo-116965-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116965,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Avoiding Machine Learning Becoming Pseudoscience in Biomedical Research - Journal Article","Machine learning promises more accurate,less biased predictions than humans alone, yet it often extrapolates from existing datasets and can reproduce prejudices embedded in historical data. As algorithmic outputs can shape ethical and moral dilemmas, they may reinforce errors and remain vulnerable to targeted influences. In biomedical research, limited and unjust data practices, overfitting, and oracle-like behavior can cause machine learning to be perceived as pseudoscience. This article discusses demarcation criteria using Popper and Kuhn perspectives and truth theories across three study cases, outlining conditions to prevent pseudoscientific outcomes.","BSI_2  \nby Ira Puspa  \nSubmission date:01-Apr-202309:26AM (UTC+0700)  \nSubmission ID:2052595091  \nFile name:12787-44211-1-PB.pdf(392.75K)  \nWord count:8195  \nCharacter count:45965  \n\n| JURNAL INFORMATIKA,Vol.10 No.1 April 2023,Halaman 1-12  \u003Cbr>ISSN:2355-6579IE-ISSN:2528-2247  \u003Cbr>DOI:https://doi.org/10.31294/inf.v101.12787  \u003Cbr>Avoiding Machine Learning Becoming Pseudoscience in  \u003Cbr>Biomedical Research  \u003Cbr>Meredita Susanty¹,Ira Puspasari²,Nilam Fitriah³,Dimitri Mahayana⁴,Tati ErawatiLatifah  \u003Cbr>Rajab⁵,Hasballah Zakaria⁶,Agung Wahyu Setiawan⁷,Rukman Hertadi⁸  \u003Cbr>36  \u003Cbr>'Universitas Pertamina  \u003Cbr>JI Teuku Nyak Arief Simprug Kebayoran Lama DKI Jakarta,Indonesia  \u003Cbr>28  \u003Cbr>2Universitas Dinamika  \u003Cbr>JI.Raya Kedung Baruk No.98,Kedung Baruk Rungkut Surabaya Jawa Timur,Indonesia  \u003Cbr>33  \u003Cbr>3;4,5,6,7.8 Institut Teknoloqi Bandung  \u003Cbr>JI.Ganesa No.10,Lb.Siliwangi Coblong,Bandung Jawa Barat,Indonesia  \u003Cbr>e-mail:'meredita.sus41y@universitaspertamina.ac.id,233221050@std.stei.itb.ac.id  \u003Cbr>333220307@std.steiitb.ac.id,4dimitrimahayanastei@gmail.com,5tati@stei.itb.ac.id,  \u003Cbr>fahala@gmail.com,⁷awsetiawan@itb.ac.id,Brukman@chem.itb.ac.id  \u003Cbr>Article Information  \u003Cbr>Submitted:20-04-2022 Revised:07-10-2022 Accepted:15-02-2023  \u003Cbr>Abstract  \u003Cbr>The use of machine learning harbours the promise of more accurate,unbiased future predictions than  \u003Cbr>human beings on their own can ever be capable of.However,because existing data sets are always  \u003Cbr>utilized,these calculations are extrapolations of lhe past and serve to reproduce prejudices embedded  \u003Cbr>in the data In turn,machine learning prediction result raises ethical and moral dilemmas.As mirors of  \u003Cbr>society,algorithms show the status quo,reinforce errors,and are subject to targeted influences-for  \u003Cbr>good and the bad.This phenomenon makes machine learning viewed as pseudoscience.Besides the  \u003Cbr>limitations,injustices,and oracle-like nature of these technologies,there are also questions about the  \u003Cbr>nature of the opportunities and possibilities they offer.This article aims to discuss whether machine  \u003Cbr>learning in biomedical research falls into pseudoscience based on Popper and Kuhn's perspective and  \u003Cbr>four theories of truth using three study cases.The discussion result explains several conditions that  \u003Cbr>must be fulfiled so that machine leaming in biomedical does not fall into pseudoscience.  \u003Cbr>Keywords:deep learning;philosophy;biomedical  \u003Cbr>53  \u003Cbr>1.Introduction  \u003Cbr>relationship in the generation of a model.The  \u003Cbr>Machine learning is the study of  \u003Cbr>training examples are drawn from an unknown  \u003Cbr>probability distribution.The leamer must develop  \u003Cbr>algorithms that improve their performance at  \u003Cbr>a general model of this space that wil allow it to  \u003Cbr>some tasks from experience(Mitchell,1997).  \u003Cbr>make sufficiently accurate predictions in new  \u003Cbr>While traditional programming uses data and  \u003Cbr>cases.Because training sets are lim-ited and the  \u003Cbr>programs to produce an output,machine  \u003Cbr>learning uses data and output to produce a  \u003Cbr>future is uncertain,learning theory rarely  \u003Cbr>guarantees algorithm performance.Probabilistic  \u003Cbr>program.The main goal of a learner is to  \u003Cbr>generalize their experiences.In this context,  \u003Cbr>performance bounds are pretty common.The  \u003Cbr>generalization refers to a learning machine's  \u003Cbr>decomposition of bias and variance is one  \u003Cbr>technique to measure generalization error.  \u003Cbr>ability to accurately execute new,previously  \u003Cbr>While machine learning shows less  \u003Cbr>unseen examples/tasks after observing a  \u003Cbr>human bias,other sorts of biases emerge.Due  \u003Cbr>learning data set (Bishop,2006).Observation of  \u003Cbr>to the model's large capacity,machine learning  \u003Cbr>existing data is performed iteratively to generate  \u003Cbr>a predictive model.  \u003Cbr>algorithms are capable of forming unrealistic  \u003Cbr>Allowing the computer to \"decide\"what is  \u003Cbr>relationships among variables.When the  \u003Cbr>relevant within the parameters ","cbCailFcuxFqS1Fo","https://ap.wps.com/l/cbCailFcuxFqS1Fo","pdf",3241942,1,21,"English","en",105,"# Introduction\n## Machine learning overview and generalization\n## Bias, variance, and overfitting\n## Pseudoscience demarcation in biomedical research","[{\"question\":\"Why can machine learning predictions become associated with pseudoscience in biomedical research?\",\"answer\":\"Because model results are extrapolations from existing datasets, they can reproduce biases and confirm social inequities, while remaining vulnerable to errors reinforced by the algorithms’ status quo behavior.\"},{\"question\":\"How do dataset limitations contribute to problematic outcomes such as overfitting?\",\"answer\":\"Limited measurements and insufficient validation can lead models to memorize training data and form unrealistic relationships, causing overfitting and reduced reliability on unseen cases.\"},{\"question\":\"What approach does the article use to assess whether biomedical machine learning is pseudoscience?\",\"answer\":\"It discusses demarcation based on Popper and Kuhn perspectives and applies four theories of truth, using three study cases to derive conditions that must be satisfied to avoid pseudoscience.\"}]","Avoiding Machine Learning Becoming Pseudoscience in Biomedical Research - Journal Article | 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can machine learning predictions become associated with pseudoscience in biomedical research?","Question",{"text":75,"@type":76},"Because model results are extrapolations from existing datasets, they can reproduce biases and confirm social inequities, while remaining vulnerable to errors reinforced by the algorithms’ status quo behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do dataset limitations contribute to problematic outcomes such as overfitting?",{"text":80,"@type":76},"Limited measurements and insufficient validation can lead models to memorize training data and form unrealistic relationships, causing overfitting and reduced reliability on unseen cases.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach does the article use to assess whether biomedical machine learning is pseudoscience?",{"text":84,"@type":76},"It discusses demarcation based on Popper and Kuhn perspectives and applies four theories of truth, using three study cases to derive 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