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Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F

使用机器学习分类器预测严重急性术后疼痛 F

基本信息

批准号：
8901203
负责人：
Patrick J Tighe
金额：
$ 11.77万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-05 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8901203
关键词：
Acute Acute Pain Algorithms Analgesics Anesthetics Award Bayesian Analysis Beck depression inventory Biological Neural Networks Biology Caring Chronic Classification Classification Scheme Client satisfaction Clinical Clinical Decision Support Systems Complex Computerized Medical Record Conduction Anesthesia Data Decision Trees Demographic Factors Development Diagnostic Procedure Elements Environment Evaluation Exhibits Foundations Funding Future Intervention Lead Learning Logistic Regressions Machine Learning Measures Medical Education Mentorship Methodology Methods Multicenter Studies Nature Operative Surgical Procedures Outcome Pain Pain intensity Pain management Pain-Free Patients Performance Perioperative Population Positioning Attribute Postoperative Pain Postoperative Period Procedures Psychology Psychometrics Recovery Reporting Research Personnel Risk Role Surgeon Techniques Testing Text Time Update base biopsychosocial cancer surgery clinical practice cost cost effective didactic education experience flexibility improved mathematical algorithm novel novel diagnostics prospective theories

项目摘要

DESCRIPTION (provided by applicant): Up to 40% of patients undergoing surgery report moderate to severe pain in the postoperative period. The development of a clinical decision support system to allow preoperative intervention for this subset of patients may have a profound impact on their recovery, and potentially their long-term outcome. To accurately forecast severe postoperative pain, we propose the use of machine learning classifiers (MLC's), which are classification algorithms employing a range of novel search and classification methodologies that continually update their performance as new information becomes available. This award will permit the applicant to complete a rigorous didactic curriculum emphasizing classification theory, algorithm evaluation, and development of clinical decision support systems. The nature of these studies place them far outside the realm of traditional medical education. By protecting time for continued mentorship from experts in pain biology and psychology, machine learning, and clinical regional anesthesia, the candidate is well-positioned to become an independently-funded researcher in the field of perioperative pain prediction. In Specific Aim 1 of this study, we will test the hypothesis that Machine Learning Classifiers can accurately predict severe post-operative pain in patients undergoing cancer surgery. This portion of the study will retrospectively test MLC's ability to predict severe pain on post-operatie day 1. An array of MLC's will be tested amongst each other, both with and without the implementation of text analytics. Additionally, all MLC's will be compared against more traditional multiple variable regression techniques such as logistic regression. In Specific Aim 2, we will test the hypothesis that the addition of prospectively obtained attributes and instances will permit continued improvement in MLC performance. This prospective portion of the study will examine the role of prospectively-obtained psychometric attributes, as well as the ability of MLC's to learn and adapt their accuracy during continued refinements to surgical and anesthetic care.

描述（由申请人提供）：高达40%的接受手术的患者报告术后出现中度至重度疼痛。临床决策支持系统的发展，使术前干预这一子集的患者可能会有深远的影响，他们的恢复，并可能他们的长期结果。为了准确预测严重的术后疼痛，我们建议使用机器学习分类器（MLC），这是一种分类算法，采用一系列新颖的搜索和分类方法，随着新信息的出现，不断更新其性能。该奖项将允许申请人完成严格的教学课程，强调分类理论，算法评估和临床决策支持系统的开发。这些研究的性质使它们远远超出了传统医学教育的范围。通过保护疼痛生物学和心理学，机器学习和临床区域麻醉专家的持续指导时间，候选人有能力成为围手术期疼痛预测领域的独立资助研究人员。在本研究的具体目标1中，我们将测试机器学习分类器可以准确预测接受癌症手术的患者术后严重疼痛的假设。本部分研究将回顾性测试MLC预测术后第1天重度疼痛的能力。一系列MLC将在相互之间进行测试，无论是否实施文本分析。此外，将所有MLC与更传统的多变量回归技术（如逻辑回归）进行比较。在具体目标2中，我们将检验这样的假设，即增加预期获得的属性和实例将允许MLC性能的持续改进。本研究的前瞻性部分将检查前瞻性获得的心理测量属性的作用，以及MLC在手术和麻醉护理的持续改进过程中学习和适应其准确性的能力。