[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117425-en":3,"doc-seo-117425-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117425,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","PhilHumans - Benchmarking Machine Learning for Personal Health - Research study","Machine learning in healthcare is used to improve patient outcomes while expanding access and affordability. The work argues that strong, widely adopted benchmarks are essential for building intelligent systems, citing how mature ML fields rely on standardized benchmarking tools. It introduces PhilHumans, a holistic benchmark suite for multiple healthcare settings and learning scenarios, including therapy, diet coaching, emergency care, intensive care, obstetric sonography, and tasks such as action anticipation, time-series modeling, insight mining, language modeling, computer vision, reinforcement learning, and program synthesis.","PhilHumans  \nCitation for published version (APA):  \nLiventsev, V. , Kumar, V. , Susaiyah, A. P. S. , Wu, Z. , Rodin, I. , Yaar, A. , Balloccu, S. , Beraziuk, M. , Battiato, S. , Farinella, G. M. , Härmä, A. , Helaoui, R. , Petkovic, M. , Recupero, D. R. , Reiter, E. , Riboni, D. ,& Sterling, R. (2024) . PhilHumans: Benchmarking Machine Learning for Personal Health. (2405 .02770 ed. ) Cornell University-arXiv. arXiv.org No. 2405.02770 [https://doi.org/10.48550/arXiv.2405.02770](https://doi.org/10.48550/arXiv.2405.02770)  \nDocument status and date:  \nPublished: 04/05/2024  \nDOI:  \n10.48550/arXiv.2405.02770  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.umlib.nl/taverne-license](www.umlib.nl/taverne-license)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[repository@maastrichtuniversity.nl](repository@maastrichtuniversity.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 27 Jan. 2025  \narXiv :2405 .02770v2 [ cs .LG] 16 May 2024  \nPhilHumans: Benchmarking Machine Learning for Personal Health  \nVadim Liventsev5 , Vivek Kumar6 , Allmin Pradhap Singh Susaiyah5 , Zixiu Wu6 , Ivan Rodin4 , Asfand Yaar4 , Simone Balloccu3 , Marharyta Beraziuk6 , Sebastiano Battiato4 , Giovanni Maria Farinella4 , Aki Hrm7 , Rim Helaoui7 , Milan Petkovic5 , Diego Reforgiato Recupero6 , Ehud Reiter3 , Daniele Riboni6 , and Raymond Sterling 1  \nAbstract The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other application areas indicates that strong benchmarks are essential for the development of intelligent systems. We present Personal Health Interfaces Leveraging HUman-MAchine Natural interactions (PhilHumans), a holistic suite of benchmarks for machine learning across different Healthcare settings-talk therapy, diet coaching, emergency care, intensive care, obstetric sonography-as well as different learning settings, such as action anticipation, timeseries modeling, insight mining, language modeling, computer vision, reinforcement learning and program synthesis  \n1 Introduction  \nUnderstaffing has been consistently identified as the major challenge facing Healthcare today [7, 1, 2, 21, 55, 82, 97, 87, 124] . Automation tools that make use of Machine Learning (also known as Healthcare 4.0 [126]) have been consistently identified as crucial for reducing the workload of Healthcare professionals and improving the quality of care [5, 34, 44, 46, 78, 86, 94, 136] . ","cbCaijxCnNLmySQx","https://ap.wps.com/l/cbCaijxCnNLmySQx","pdf",3862312,1,26,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Benchmarks\n## 2.1 Tabular perspective","[{\"question\":\"What problem does PhilHumans aim to address in machine learning for healthcare?\",\"answer\":\"It targets the lack of an accepted, standardized benchmarking approach and limited availability of research datasets, which slows progress and makes results hard to compare across studies.\"},{\"question\":\"What kinds of healthcare settings are covered by PhilHumans benchmarks?\",\"answer\":\"The benchmarks span multiple settings such as therapy and coaching, emergency care, intensive care, and obstetric sonography.\"},{\"question\":\"What machine learning challenges and task types are included?\",\"answer\":\"PhilHumans covers tasks including conversational agents, computer vision, time-series prediction, reinforcement learning, and also broader learning settings such as language modeling, insight mining, and program synthesis.\"}]","PhilHumans - 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