Integrated methodologies (machine learning, data analysis, natural language) and their use in health/language applications and assistive technologies for people with disabilities

综合方法（机器学习、数据分析、自然语言）及其在健康/语言应用和残疾人辅助技术中的应用

• 批准号: 203981-2011
• 负责人: Cercone, Nick
• 金额: $ 1.75万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570016
• 关键词: Integrated; methodologies; machine; learning; data

Two broad areas of investigation encompass my primary research interests: (1) machine learning and associated technologies for health and language applications; and (2) assistive technologies for people with disabilities. A judicious mixture of theory and practice has produced theoretical and methodological progress resulting in advanced prototype software implementations. Examples of this theory/practice admixture include finding best evidence for evidence-based medicine, verification of independently accumulated evidence using multiply sectioned Bayesian networks, and hybrid systems design using machine learning tasks such as classification and prediction. Another example uses head driven phase structured grammar approaches for speech prosody, language translation, text mining and n-gram analysis to generalize language research to a wider class of problems, e.g., author attribution, classifying dementia levels in patients with mild cognitive impairment from patient-caregiver transcripts, detecting malicious code in email, and designing software to assist students with disabilities. Machine learning and associated technologies for health & language applications: Our research effectively exploits solutions to a major problem for Canadian health organizations: finding best evidence for evidence based best practice recommendations. A long-term goal is to develop a decision support system for evidence based management and best practice recommendations by: (1) building prototypes for adaptive information extraction, text & data mining from documents on which to base best practices; & (2) employing multiply sectioned Bayesian networks to infer a probabilistic interpretation to validate evidence for recommendations. HYRIS generalizes our rough set-based hybrid intelligent system for survival analysis. Assistive technologies for people with disabilities help them accomplish tasks that they cannot accomplish otherwise or could not do easily. Our research specializes on technology for persons with vision impairment, with hearing impairment and with learning disabilities and mild cognitive impairment.

两个广泛的研究领域涵盖了我的主要研究兴趣：（1）机器学习和 健康和语言应用的相关技术;以及（2）残疾人辅助技术 残疾。理论和实践的明智结合带来了理论和方法上的进步 从而导致高级原型软件实现。 这种理论/实践混合的例子包括为循证医学寻找最佳证据， 验证独立积累的证据，使用多节贝叶斯网络，和混合 系统设计使用机器学习任务，如分类和预测。另一个例子使用head 用于语音韵律、语言翻译、文本挖掘和n-gram的驱动阶段结构化语法方法 分析将语言研究推广到更广泛的问题类别，例如，作者归属，分类 轻度认知障碍患者的痴呆水平，来自患者-护理人员转录本，检测 电子邮件中的恶意代码，以及设计帮助残疾学生的软件。 机器学习和相关技术用于健康和语言应用：我们的研究有效地利用了加拿大卫生组织的主要问题的解决方案：为基于证据的最佳实践建议找到最佳证据。一个长期的目标是开发一个决策支持系统的证据为基础的管理和最佳实践建议：（1）建立原型的自适应信息提取，文本和数据挖掘的文件上的最佳实践;（2）采用多节贝叶斯网络推断概率解释，以验证证据的建议。HYRIS概括了我们的基于粗糙集的混合智能系统的生存分析。 残疾人辅助技术帮助他们完成他们无法完成或无法轻松完成的任务。我们的研究专门针对视力障碍、听力障碍、学习障碍和轻度认知障碍的人。