Optimization of monitoring, prediction and phenotyping of deterioration of inhospital patients using machine learning and multimodal real time data

对内科-外科病房进行有效的患者监测至关重要，因为高达5%的住院成人 患者病情恶化，需要转移到重症监护室（ICU）或快速反应小组的干预 （RRT）。目前，每4-6小时对所有患者进行一次生命体征测量，即使是最稳定的患者。 对于稳定的患者，这种监测通常是不必要的，而对于高风险患者，生命体征监测 每隔4-6小时通常是不够的。为了满足这一需求，我们将利用世界上最大、最多样化的 临床数据集，使用来自240万住院患者的电子健康记录（EHR）数据， 生成机器学习（ML）预测模型，旨在优化患者监护。我们将使用 持续监测（CM）设备，以提前识别可能恶化的患者，并指定临床 恶化的根本原因，以便能够及时干预。我们已经应用并发布了类似的ML 其他队列的方法，包括：1）深度递归神经网络（RNN），以避免不必要的 2）深度学习模型，使用连续监测数据预测临床警报，最高可达4 提前几个小时; 3）对医疗笔记进行自然语言处理， 患者我们的方法包括从2000名目标人群中前瞻性地收集CM数据 住院患者，并开发和验证模型，回顾性和前瞻性。我们 这种方法将使我们能够：确定在内外科病房住院的稳定患者，以优化 生命体征监测。我们将使用来自240万次住院的EHR数据训练RNN模型，以预测 测量生命体征，患者在接下来的8小时内保持稳定，并能够消除下一个生命体征 测量.我们将回顾性地验证模型，使用交叉隶属验证，并前瞻性地， 在五家不同的医院默默验证基于连续性，开发临床恶化算法 监测数据和临床硬结果。我们将从以下目标人群中收集前瞻性数据： 2,000名住院病人，他们在我们最大的医院的内科-外科楼层住院， 警告分数高于5。CM补丁将在入院时开始收集数据。我们将使用组合 临床硬结局（死亡、插管、心脏骤停、计划外ICU转移、RRT），以训练两个深度- 学习模型来预测4小时和24小时之前的恶化。定义早和晚 住院患者恶化的表型基质。利用56 K恶化的临床资料， 目标1中的患者（EHR变量和提取的表现症状），治疗前4小时和24小时 恶化，我们将进行无监督聚类分析，以确定与表型相关的独特聚类， 恶化我们将把衍生的表型组与临床结果和治疗联系起来， 有针对性的治疗和干预策略。我们的目标是开发新的工具，以满足患者的需求， 资源，并为住院患者提供更高效，有效，个性化和主动的护理。

Efficient patient monitoring on the medical-surgical wards is crucial, because up to 5% of hospitalized adult patients deteriorate, requiring transfer to the intensive care unit (ICU) or intervention of a rapid response team (RRT). Currently, vital sign measurement is performed on all patients every 4-6 hours, even the most stable. For stable patients, this monitoring is often unnecessary, whereas for higher-risk patients, vital sign monitoring every 4-6 hours is often not adequate. To address this need, we will leverage one of the largest, most diverse clinical datasets in the country, using electronic health record (EHR) data from 2.4M hospitalized patients to generate machine learning (ML) predictive models, designed to optimize patient monitoring. We will use continuous monitoring (CM) devices to identify in advance patients likely to deteriorate and specify the clinical underlying reasons of deterioration to enable timely interventions. We have applied and published similar ML approaches on other cohorts, including: 1) deep recurrent neural networks (RNNs) to avoid unnecessary overnight vitals; 2) deep learning models that use continuous monitoring data to predict clinical alerts up to 4 hours ahead of time; and 3) natural language processing on medical notes and unsupervised clustering of patients. Our approach involves collecting prospectively CM data from a targeted population of 2,000 hospitalized patients, and developing and validating models, both retrospectively and prospectively. Our approach will allow us to: Identify stable patients admitted on the medical-surgical wards to optimize vital signs monitoring. We will train a RNN model using EHR data from 2.4M hospitalizations, to predict, after vital signs are measured, stable patients for the next 8 hours, and enable eliminating the next vitals measurement. We retrospectively will validate the model, using cross-affiliation validation, and prospectively, silently validate it in 5 different hospitals. Develop a clinical deterioration algorithm, based on continuous monitoring data and clinical hard outcomes. We will collect prospective data from a targeted population of 2,000 inpatients, who are admitted on medical-surgical floors in our largest hospital, with a modified early warning score higher than 5. The CM patches will start collecting data upon admission. We will use combined clinical hard outcomes (death, intubation, cardiac arrest, unplanned ICU transfer, RRTs) to train two deep- learning models to predict deterioration up to 4 hours and up to 24 hours before. Define the early and late phenotypic substrates of hospitalized patient deterioration. Using the clinical data of 56K deteriorated patients from Aim 1 (EHR variables and extracted presenting symptoms) 4 hour and 24 hours prior to deterioration, we will perform unsupervised cluster analysis to identify unique clusters linked to phenotypes of deterioration. We will associate derived phenotype groups to clinical outcomes and treatments, to inform more targeted treatment and intervention strategies. We aim to develop new tools to align patient needs with resources, and deliver more efficient, effective, personalized, and proactive care to hospitalized patients.