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

使用机器学习和多模态实时数据优化住院患者病情恶化的监测、预测和表型分析

• 批准号: 10735863
• 负责人: Karina W. Davidson
• 金额: $ 81.48万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-16 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735863
• 关键词: Address; Admission activity; Adult; Algorithms; COVID-19 patient; Caring; Cessation of life; Clinical; Clinical Data; Cluster Analysis; Communities; Country; Data; Data Set; Deterioration; Devices; Diagnosis; Diagnostic; Electronic Health Record; Floor; Health; Health system; Heart Arrest; Hospitalization; Hospitals; Hour; Inpatients; Intensive Care Units; Intervention; Intubation; Life; Link; Machine Learning; Manuals; Measurement; Measures; Medical; Modeling; Monitor; Morbidity - disease rate; Natural Language Processing; Neural Network Simulation; Nurses; Operative Surgical Procedures; Outcome; Patient Admission; Patient Monitoring; Patient-Focused Outcomes; Patients; Phenotype; Prospective cohort; Publishing; Recommendation; Recording of previous events; Recurrence; Research; Resources; Risk; Sleep; Specific qualifier value; Staff Work Load; Symptoms; Target Populations; Time; Training; Validation; Work; Workload; base; clinical predictors; cohort; cost; deep learning model; design; diagnostic value; high risk; improved; innovative technologies; large datasets; learning strategy; machine learning algorithm; machine learning prediction; model design; monitoring device; mortality; multimodality; patient population; personalized care; prediction algorithm; predictive modeling; predictive tools; preventable death; prospective; recurrent neural network; response; support tools; targeted treatment; tool; treatment strategy; unsupervised learning; vector; ward; wearable device; wearable monitor

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.

有效的患者对医学手术病房的监测至关重要，因为多达5％的住院成人 患者恶化，需要转移到重症监护病房（ICU）或快速反应团队的干预措施 （RRT）。目前，每4-6小时，即使是最稳定的患者，对所有患者进行生命体征测量。 对于稳定的患者，这种监测通常是不必要的，而对于高危患者，生命体征监测 每4-6个小时通常不够。为了满足这一需求，我们将利用最大，最多样化的人之一 该国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，旨在优化患者监测。我们将使用 连续监测（CM）设备，以提前识别可能恶化并指定临床的患者 恶化的根本原因可以及时干预。我们已经应用并发布了类似的ML 其他人群的方法，包括：1）深度复发的神经网络（RNN），以避免不必要 隔夜生命； 2）使用连续监视数据预测临床警报的深度学习模型最多可预测4个 提前几个小时； 3）在医学笔记和无监督的聚类上进行自然语言处理 患者。我们的方法涉及从目标人群中收集前瞻性的CM数据 住院的患者以及发展和验证模型，包括回顾性和前瞻性。我们的 方法将使我们能够：确定在医疗手术病房中录取的稳定患者以优化 生命体征监测。我们将使用来自240万住院的EHR数据培训RNN模型，以预测 在接下来的8小时内测量生命体征，稳定的患者，并能够消除下一个生命力 测量。我们回顾性地将使用交叉递交验证和前瞻性验证该模型 在5家不同的医院中静静地验证它。基于连续的 监视数据和临床硬性结果。我们将从目标人群中收集前瞻性数据 2,000名住院患者，他们在我们最大的医院的医疗手术地板上被接纳 警告评分高于5。CM补丁将在入院后开始收集数据。我们将共同使用 临床硬结局（死亡，插管，心脏骤停，计划外的ICU转移，RRT），以训练两个深 学习模型可预测长达4小时及24小时前的恶化。定义早期和晚期 住院的患者恶化的表型底物。使用56K的临床数据恶化 AIM 1的患者（EHR变量和提取的症状）前4小时24小时 恶化，我们将执行无监督的聚类分析，以识别与表型相关的独特集群 恶化。我们将将衍生的表型组与临床结果和治疗相关联，以告知更多 有针对性的治疗和干预策略。我们旨在开发新工具以使患者需求与 资源，并向住院的患者提供更有效，有效，个性化和主动的护理。