[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117230-en":3,"doc-seo-117230-105":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},117230,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","PhilHumans - Benchmarking Machine Learning for Personal Health","PhilHumans presents a holistic benchmark suite for machine learning in personal health, aiming to improve patient outcomes and expand healthcare accessibility and affordability. It responds to the lack of accepted, widely available benchmarking standards by defining tasks that include datasets and evaluation procedures across therapy, diet coaching, emergency care, intensive care, and obstetric sonography. The suite also covers diverse learning and modeling settings such as action anticipation, time-series modeling, insight mining, language modeling, computer vision, reinforcement learning, and program synthesis.","arXiv :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] . In turn, the shortage of standard benchmarks has been consistently identified as a central roadblock for machine learning in Healthcare [27, 31, 49, 52, 59, 76, 81, 95, 110] .  \nWhether it’s ImageNet [32] in Computer Vision or GLUE [128] in natural language processing, benchmarks are a core research tool in mature applications of machine learning, enabling quantitative analysis of learning methodologies to guide and orient their development. Machine learning for Healthcare, an emergent field  \n1R2M Solution, Spain, 2Joint Research Centre, Italy, 3University of Aberdeen, Scotland 4University of Catania, Italy, 5TU Eindhoven, the Netherlands (Corresponding author’s e-mail: [v.liventsev@tue.nl](v.liventsev@tue.nl)), 6 University of Cagliari, Italy, 7 Philips Research, Eindhoven, the Netherlands. The affiliations listed reflect those at the time the work was completed and may not be current.  \n2 Alonso et al.  \nwith unique challenges in availability of research datasets [6, 48, 92, 139] lacksan accepted benchmarking standard: recent literature reviews [93, 126] of the field cover a variety of studies that each use their own (often non-public) benchmark.  \nTo address this, we propose a suite of tasks that each include a dataset and an evaluation procedure and cover a wide variety of both healthcare settings (therapy and coaching, emergency care, intensive care, sonography) and machine learning challenges (conversational agents, computer vision, time series prediction, reinforcement learning) . We also provide a preliminary evaluation of widely used machine learning approaches on these tasks.  \n2 Benchmarks  \n2.1 Tabular perspective  \nOur first two benchmarks evaluate methods for machine learning on low-dimensional tabular data. This perspective restricts the types of data made available to learning algorithms, excluding for example, radiological data. At the same time, it is anything but contrived: many real-world tasks, such as risk assessment for negative outcomesin intensive care based on the patient’s vital signs, are within the tabular domain [102] .  \n2.1.1 MIMIC-IV-Ext-SEQ  \nReinforcement Learning in Healthcare is typically concerned with narrow selfcontained tasks such as sepsis prediction or anesthesia control. However, previous research has demonstrated the potential of generalist models [100](the prime example being Large Language Models [18]) to outperform tas","cbCaicDdOZJdOEaK","https://ap.wps.com/l/cbCaicDdOZJdOEaK","pdf",3824633,1,25,"English","en",105,"# Introduction\n# Benchmarks\n## Tabular perspective\n## MIMIC-IV-Ext-SEQ\n## Auto-ALS","[{\"question\":\"Why are benchmarks important for machine learning in healthcare?\",\"answer\":\"Benchmarks enable quantitative comparison of learning methods and help guide system development, similar to widely used benchmarks like ImageNet and GLUE in other ML areas.\"},{\"question\":\"What does the PhilHumans benchmark suite provide?\",\"answer\":\"PhilHumans provides a set of benchmark tasks, each with an accompanying dataset and an evaluation procedure, spanning multiple healthcare settings and ML challenges.\"},{\"question\":\"Which types of learning tasks and settings are covered?\",\"answer\":\"The benchmarks include areas such as action anticipation, time-series modeling, insight mining, language modeling, computer vision, reinforcement learning, and program synthesis.\"}]","PhilHumans - 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