基于变压器的时间到事件预测慢性病恶化的恶化
Moshe Zisser1, Dvir Aran2,3
1Faculty of Data and Decision Sciences, Technion-Israel Institute of Technology, Haifa, 3200003, Israel.
Journal of the American Medical Informatics Association : JAMIA
|February 13, 2024
概括
我们开发了STRAFE,一种用于电子健康记录的深度学习生存分析模型. STRAFE准确预测慢性病 (CKD) 患者的疾病进展,改善风险识别和患者护理.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 纵向健康记录为疾病预测提供了宝贵的见解.
- 传统的风险预测模型经常使用固定的时间方法,这可能不适合临床决策.
- 时间到事件的预测对于预测患者旅程中的临床事件至关重要.
研究的目的:
- 介绍STRAFE,一种基于变压器的新型架构,用于电子健康记录的生存分析.
- 提高在纵向健康数据中预测时间到事件结果的准确性和通用性.
- 改进对高风险患者的识别,以便进行主动干预.
主要方法:
- 开发了一个基于变压器的深度学习架构 (STRAFE) 用于生存分析.
- 使用来自OMOP-CDM格式化访问序列的SNOMED-CT代码作为输入.
- 在48个月内计算事件发生概率,并根据大型慢性病 (CKD) 数据集进行评估.
主要成果:
- 与预测CKD进展的现有时间到事件算法相比,STRAFE表现出更高的性能,平均绝对误差 (MAE) 更低.
- 与二进制结果预测模型相比,实现了接受器运行曲线下面面积 (AUC) 的改善.
- 通过一种新的可视化方法,STRAFE预测将识别高风险患者的积极预测值提高了三倍.
结论:
- 时间到事件预测模型最适合临床应用.
- 深度学习的STRAFE模型超过了传统和固定时间预测算法,这可能是因为它能够处理受审查的数据.
- 使用STRAFE准确的风险分层可以显著改善患者的治疗结果,降低医疗保健成本,并优化资源配置.
更多相关视频
08:505/6th Nephrectomy in Combination with High Salt Diet and Nitric Oxide Synthase Inhibition to Induce Chronic Kidney Disease in the Lewis Rat
Published on: July 3, 2013
23.6K
04:05Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
Published on: June 30, 2023
1.8K
相关概念视频
01:27Dialysis
310
Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
310
01:17Factors Affecting Renal Clearance: Renal Impairment
95
Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
95
01:11Renal Failure: Dose Adjustments
89
In patients with renal impairment, drugs undergo significant changes in their pharmacokinetics, which require dosage adjustments to ensure safe and effective therapy.
Reduced renal clearance and elimination rate are common outcomes of renal impairment. These alterations lead to a prolonged elimination half-life and an altered apparent volume of distribution for drugs. As a result, dosage adjustments are typically necessary to maintain optimal drug levels in the body.
However, dosage adjustments...
Reduced renal clearance and elimination rate are common outcomes of renal impairment. These alterations lead to a prolonged elimination half-life and an altered apparent volume of distribution for drugs. As a result, dosage adjustments are typically necessary to maintain optimal drug levels in the body.
However, dosage adjustments...
89
01:24Kaplan-Meier Approach
138
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
138
01:18Introduction To Survival Analysis
237
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
237
01:09Censoring Survival Data
92
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
92