[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117373-en":3,"doc-seo-117373-105":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},117373,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","M3H - Multimodal Multitask Machine Learning for Healthcare","Developing an integrated many-to-many framework using multimodal data is essential for unifying healthcare applications from diagnoses to operations. M3H presents an explainable multimodal multitask learning framework that consolidates tabular, time-series, language, and vision inputs for supervised binary/multiclass classification, regression, and unsupervised clustering. It balances self-exploitation of source tasks and cross-exploration across tasks, while providing TIM-score explainability for task learning interdependencies. Across 40 diagnoses, three operation forecasts, and one phenotyping task, M3H improves on single-task baselines by 11.6% on average, with modular, production-ready design.","M3H: Multimodal Multitask Machine Learning for Healthcare  \nAbstract  \nDeveloping an integrated many-to-many framework leveraging multimodal data for multiple tasks is crucial to unifying healthcare applications ranging from diagnoses to operations. In resource-constrained hospital environments, a scalable and unified machine learning framework that improves previous forecast performances could improve hospital operations and save costs. We introduce M3H, an explainable Multimodal Multitask Machine Learning for Healthcare framework that consolidates learning from tabular, time-series, language, and vision data for supervised binary/multiclass classification, regression, and unsupervised clustering. It features a novel attention mechanism balancing self-exploitation (learning source-task), and cross-exploration (learning cross-tasks), and offers explainability through a proposed TIM score, shedding light on the dynamics of task learning interdependencies. M3H encompasses an unprecedented range of medical tasks and machine learning problem classes and consistently outperforms traditional single-task models by on average 11.6% across 40 disease diagnoses from 16 medical departments, three hospital operation forecasts, and one patient phenotyping task. The modular design of the framework ensures its generalizability in data processing, task definition, and rapid model prototyping, making it production ready for both clinical and operational healthcare settings, especially those in constrained environments.  \n1. Introduction  \n1.1 Background  \nThe integration of Artificial Intelligence (AI) and Machine Learning (ML) has seen unprecedented promise to advance healthcare services and to fundamentally improve our understanding of medicine (Topol 2019, Yu 2018) . Leveraging the increasingly accessible patient digital records, multimodal learning incorporates multiple modalities and sources of data input to provide holistic views of patient profiles (Soenksen 2022,  \nHuang 2020, Acosta 2022, Baltrusaitis 2019, Ahmed 2020) . However, beyond the integration of diverse inputs, a combination of outcomes is often further necessary to characterize patients comprehensively. Multitask learning, which leads to performance breakthroughs in large language models such as GPT-2 (Radford 2019), and computer vision (Ren 2015, He 2017, Reed 2022), is a natural extension under this premise to simultaneously learn multiple medical tasks to improve model performance across cardiology (Torres-Soto 2020), psychiatry and psychology (Tseng 2020, Lee 2021), oncology (Fu 2021), radiology (Jin 2021) and other healthcare domains (Eyuboglu 2021, Wang 2023, Tang 2023) . Specifically, in contrast with multiclass learning of mutually exclusive targets, multitask learning can simultaneously process multiple tasks and thus provide better performance due to the sharing of common knowledge. Importantly, multimodal multitasking emulates existing collaborative efforts in clinical settings, where physicians and administrators across multiple departments often integrate diverse sources of information to jointly navigate multiple complex medical decisions simultaneously. However, it remains challenging to develop an integrative multimodal multitask machine learning framework that is consistently applicable across distinct healthcare domains and machine learning problem classes while maintaining efficiency in handling increasingly large healthcare datasets (Ahmed 2023) .  \n1.2 Contributions  \nM3H addresses several challenges, including the difficulty of integration across multiple distinct machine learning problem classes into a single framework and the lack of explainability metrics to measure how and why combining certain tasks improves performance. In particular, the M3H framework complements and extends previous literature on important key topics and provides new perspectives on the following:  \n1. M3H represents the first integrated healthcare system to bridge multi-diseas","cbCaif3kD2iQP38x","https://ap.wps.com/l/cbCaif3kD2iQP38x","pdf",3441569,1,30,"English","en",105,"# Introduction\n## Background\n## Contributions","[{\"question\":\"What is the core idea behind M3H in healthcare machine learning?\",\"answer\":\"M3H builds an integrated many-to-many multimodal multitask framework that learns from tabular, time-series, language, and vision data to support multiple healthcare tasks together.\"},{\"question\":\"Which tasks and prediction types does M3H handle?\",\"answer\":\"M3H supports supervised binary/multiclass classification, regression, and unsupervised clustering, covering a broad set of medical problem classes.\"},{\"question\":\"How does M3H provide explainability for multitask learning?\",\"answer\":\"M3H introduces the TIM score, an incremental explainable metric that quantifies performance benefits from jointly training additional tasks and helps reveal interdependencies between tasks.\"}]","M3H - 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