COMPUTATIONAL THINKING-Novel Machine Learning Approaches for Automati

在生物医学领域，临床医生和研究人员现在面临着信息管理、创新和决策方面的巨大挑战，因为在这个时代，知识增长异常迅速，分布在大量数据库中，规模远远超过个人所能掌握的范围。计算机惊人的速度、记忆能力和符号处理能力，如果得到适当的利用，可以补充人类的认知能力，从而能够有效地使用与解决临床和科学问题有关的所有知识。 为了给计算和认知领域的研究注入活力，近年来出现了一些新的概念：计算智能、机器学习、智能放大系统、灵活能力、人机协作和计算思维。作为医学高级研究项目倡议的一部分，NLM正在资助计算思维的新方法，以评估使用创新的计算方法来提高临床医生和生物医学科学家解决一个或多个重要认知任务的能力的可行性，并为患者、家庭和公众带来医疗保健的改善。

In biomedicine, clinicians and researchers now face formidable challenges in information management, innovation, and decision-making in an era which is seeing extraordinarily rapid growth of knowledge, distributed among a host of databases, and on a scale far larger than can be mastered by an individual. The remarkable speed, memory capacity, and symbol-manipulating power of computers, if properly harnessed, can complement human cognitive strengths so as to enable efficient use of all of the knowledge relevant to solution of clinical and scientific problems. To invigorate research in the arena of computation and cognition, a number of fresh concepts have arisen in recent years: computational intelligence, machine learning, intelligence amplifying systems, flexible competence, human-computer collaboration, and computational thinking. As part of its ¿Medical Advanced Research Projects Initiative,¿ the NLM is funding novel approaches to computational thinking, in order to evaluate the feasibility of using innovative computational approaches to enhance the ability of clinicians and biomedical scientists to solve one or more significant cognitive tasks and bring improvements in medical care to patient, families and the public.