Identification of Patients with Low Life Expectancy

 描述（由申请人提供）：越来越多的人认识到，最佳治疗对每个患者都不一样-这取决于个体患者的情况。决定最佳临床管理的一个重要因素是患者的预期寿命，这决定了医疗决策必须操作的时间范围。例如，虽然增加额外的糖尿病药物可能会使一个40岁的人在20年后免于失明或发生肾衰竭，但它不会给一个80岁的转移性恶性肿瘤患者带来任何好处，预计他只能活几个月。 因此，当我们衡量护理质量、通过临床决策支持向临床医生建议治疗方案或比较不同的治疗策略时，我们必须考虑患者的预期寿命。然而，目前没有可用的方法可以以足够的精度做到这一点。 最常用的技术来评估病人的死亡率风险主要利用管理数据和其他结构化的数据字段在电子医疗记录。这种方法省去了大量的信息，这些信息只能在叙述性文档中获得，例如提供者笔记、放射学报告、在这个项目中，我们提出了两个新的技术-人工智能技术动态逻辑和自然语言处理（NLP）的应用，以测试假设，可以利用叙述性电子文档中的信息来显著提高识别低预期寿命患者的准确性。 动态逻辑允许规避组合复杂性的挑战，这种复杂性限制了变量及其组合的数量，这些变量及其组合可以被大多数当前使用的分析方法视为结果的预测因子。动态逻辑利用有限数量的迭代近似，将具有多个预测变量的问题的复杂性从指数降低到近似线性。利用动态逻辑将使我们能够大大增加模型的丰富性，以识别预期寿命低的患者，并最终提高其准确性。 通常仅在叙述性文件中发现的患者功能状态等信息对于提高识别高死亡率风险的虚弱患者的准确性至关重要。现代NLP技术可以有效地识别医学文本中的关键概念，但到目前为止，分析方法只允许在预测模型中考虑少数预先选择的概念。将NLP与动态逻辑相结合，将使我们能够大大扩展可以包含在预期寿命预测模型中的叙事文本中的概念数量，这可能会大大提高准确性。 在拟议的翻译多学科项目中，我们的团队将包括人工智能、自然语言处理、电子病历数据分析和老年医学方面的专家，他们将测试动态逻辑和NLP的结合是否可以改善对高死亡风险患者的识别。

 DESCRIPTION (provided by applicant): It is increasingly recognized that optimal treatment is not the same for every patient - it depends on the individual patient's circumstances. One important factor that determines the optimal clinical management is the patient's life expectancy, which determines the temporal horizon within which medical decisions have to operate. For example, while adding an extra diabetes medication may save a 40-year-old individual from going blind or developing kidney failure 20 years later, it will not bring any benefits to an 80-yer old with a metastatic malignancy who is expected to live only a few months. Consequently, it is critical that when we measure quality of care, suggest treatment options to clinicians through clinical decision support or compare different treatment strategies, we take into account the patient's life expectancy. However, currently there are no methods available that can do this with sufficient accuracy. Most commonly used techniques to assess a patient's mortality risk draw primarily on administrative data and other structured data fields in electronic medical records. This approach leaves out a large amount of information that is only available in narrative documents such as provider notes, radiology reports, etc. In this project we propose to test the hypothesis that application of two novel technologies - artificial intelligence technique Dynamic Logic and natural language processing (NLP) - could leverage the information in narrative electronic documents to significantly improve the accuracy of identification of patients with low life expectancy. Dynamic Logic allows to circumvent the challenge of combinatorial complexity that limits the number of variables and their combinations that can be considered as predictors of an outcome by most currently used analytical methods. Dynamic Logic makes use of a limited number of iterative approximations to reduce the complexity of a problem with multiple predictor variables from exponential to approximately linear. Utilization of Dynamic Logic will allow us to greatly increase the richness of the models for identification of patients with low life expectancy and ultimately improve their accuracy. Information such as the patient's functional status that is usually only found in narrative documents may be critical to improving accuracy of identifying frail patients at high mortality risk. Modern NLP techniques can effectively identify key concepts in medical text but until now analytical methods allowed consideration of only a few of pre-selected concepts in prediction models. Combining NLP with Dynamic Logic will allow us to greatly expand the number of concepts from narrative text that could be included in the life expectancy prediction model, likely leading to a considerable improvement in accuracy. In the proposed translational multidisciplinary project our team that will include experts on artificial intelligence, natural language processing, analysis of data in electronic medical records, and geriatrics will test whether a combination of Dynamic Logic and NLP improves identification of patients at high risk of death.