دانلود مقاله ایجاد تیم تحقیقاتی برای هوش مصنوعی و قلب و عروق
چکیده
مقدمه
مطالب و روش ها
نتایج
بحث
منابع
Abstract
Introduction
Materials and methods
Results
Discussion
References
چکیده
هدف مطالعه
یک تیم بین‌رشته‌ای چند نهادی برای ایجاد یک گروه تحقیقاتی با تمرکز بر استفاده از هوش مصنوعی و انفورماتیک برای بیماران قلبی-انکولوژی ایجاد شد. قلب و عروق یک رشته پزشکی نوظهور است که به پیشگیری، غربالگری و مدیریت اثرات نامطلوب قلبی عروقی درمان‌های سرطان/سرطان اختصاص دارد. بیماری های قلبی عروقی عامل اصلی مرگ و میر در بازماندگان سرطان است. خطر قلبی عروقی در این بیماران بیشتر از جمعیت عادی است. با این حال، پیش‌بینی و پیشگیری از حوادث نامطلوب قلبی عروقی در افراد با سابقه درمان سرطان/سرطان چالش برانگیز است. بنابراین، ایجاد یک تیم بین رشته‌ای برای ایجاد کمک‌های تصمیم‌گیری بالینی طبقه‌بندی خطر قلبی عروقی برای ادغام در پرونده‌های سلامت الکترونیکی برای بیماران انکولوژی بسیار مهم در نظر گرفته شد.
طراحی/تنظیم/شرکت کنندگان
اعضای اصلی تیم از کالج پزشکی ویسکانسین (MCW)، دانشگاه ویسکانسین-میلواکی (UWM) و دانشکده مهندسی میلواکی (MSOE) و اعضای اضافی از کلینیک کلیولند، کلینیک مایو و سایر موسسات به نیروهای خود ملحق شده اند تا بالاترین سطح را اعمال کنند. - محاسبات عملکرد در قلب و عروق
نتایج
این تیم متشکل از پزشکان و محققان از زمینه های مکمل و هم افزایی مرتبط با این کار است. این تیم یک گروه اپیدمیولوژیک از حدود 5000 بازمانده سرطان ساخته است که به عنوان پایگاه داده ای برای پروژه های هوش مصنوعی چند نهادی بین رشته ای عمل می کند.
نتیجه
درس های آموخته شده از ایجاد این تیم و همچنین یافته های اولیه از گروه اپیدمیولوژی ارائه شده است. موانع برای تشکیل یک تیم بین رشته ای چند نهادی برای تحقیقات انفورماتیک سلامت در قلب و عروق شکسته شده است. پایگاه داده ای از بازماندگان سرطان با همکاری تیم ایجاد شده است و بینش اولیه را در مورد پیامدهای قلبی عروقی و بیماری های همراه در این جمعیت ارائه می دهد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Study objective
A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial.
Design/setting/participants
Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology.
Results
The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects.
Conclusion
Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.
Introduction
A team may be defined as a set of individuals with complementary skills who are committed to a common goal, set of performance objectives, and approach for which they hold one another accountable [1]. Although organizational specialists proposed this definition to describe workgroups in business, it is equally applicable to interdisciplinary healthcare teams [2], including research teams. In fact, an American Heart Association Scientific Statement advocates for an interdisciplinary team approach to maximize scientific discoveries [3]. To apply this in research practice, how can an interdisciplinary multi-institutional team be established that encompasses a variety of expertise and includes diverse individuals at various stages of training and career, to chart a path for the application of artificial intelligence in cardio-oncology? How can team science be applied in the setting of informatics and a learning health system to predict and optimize the best care and management for cancer survivors at risk for cardiovascular events? A learning healthcare system applies scientific knowledge during clinical care while also eliciting insights from that care in order to spur innovation in the delivery of optimal healthcare and inspire new research areas [4]. What should an interdisciplinary team look like in this setting? Who should be on the team? How should the team be structured and why? How should team members across disciplines communicate to foster and optimize collaboration? Should there be one comprehensive team, or smaller teamlets? What is the value of operating across disciplines within and across institutions? Is there value in diversity of perspectives, leveraging variety in training backgrounds and current practice methods and patterns, sharing experiences across institutions, pooling versus comparing patient cohorts, testing and validating algorithms, or exchanging ideas that may work across institutions versus focusing on only one? While the answers to some of these questions may seem self-evident, success in building interdisciplinary teams to develop and execute projects designed to promote equity, improve risk stratification, prevent adverse cardiovascular events, and improve health outcomes for cancer survivors will depend upon deep and unflinching consideration of such questions and taking the sometimes difficult or non-obvious actions required to clear the hurdles they suggest.
Results and analyses
3.1. Disciplines of interdisciplinary team constituents
The following types of required expertise were determined for this interdisciplinary teamwork: cardio-oncology, health informatics, artificial intelligence (including machine learning and natural language processing), health/clinical/biomedical informatics, research-related information technology, biostatistics, shared decision-making, clinical decision support, community engagement, and health disparities. Consequently, individuals at MC W, then U WM and MSOE, then Mayo Clinic in Rochester MN, Cleveland Clinic in Cleveland OH, and University of Ottawa in Canada with these expertise were recruited to participate in the team (Figs. 1 and 2).