机器学习方法用于预测病毒失败的比较:使用电子健康记录数据的案例研究.
Allan Kimaina1,2,3, Jonathan Dick4,3, Allison DeLong2
1Moi University, Eldoret, Kenya.
概括
机器学习模型可以在计划测量之前预测人类免疫缺陷病毒 (HIV) 病毒失败. 这使得更早的干预措施可以改善资源有限的环境中的患者结果.
科学领域:
- 机器学习 机器学习
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
背景情况:
- 人类免疫缺陷病毒 (HIV) 病毒衰竭发生在抗逆转录病毒疗法 (ART) 无法抑制病毒载量低于1000副本/毫升时.
- 世卫组织建议对艾滋病毒患者进行6个月一次的病毒载荷监测,并每年进行一次,因疑似病毒失败而有偏差.
- 及时检测病毒失败对于启动必要干预至关重要,临床预测模型为早期检测提供了潜力.
研究的目的:
- 为了比较统计机器学习方法预测HIV病毒失败的预测准确度.
- 利用来自肯尼亚大型艾滋病毒护理计划的电子健康记录 (EHR) 数据.
- 在ART启动后的第一和第二次测量时预测病毒失败.
主要方法:
- 在超过10,000个患者记录中训练和交叉验证了10个统计机器学习模型 (参数,非参数,整体,贝叶斯).
- 用临床记录中的50个临床医生选择的变量作为输入.
- 使用十倍交叉验证计算预测准确度,测量灵敏度,特异性和AUC.
主要成果:
- 在第一次和第二次测量时,病毒失效率约为20%.
- 组合方法一般优于其他方法,在第一次测量时的特异性>90%和灵敏度为50-60%.
- 第二次测量的预测准确性得到改善,灵敏度>80%;超级学习器,梯度增强和BART是表现最好的.
- 顶级方法实现了75-85%的积极预测值和20%的失败率的负预测值>95%.
结论:
- 机器学习技术在预定监测之前有望识别患有艾滋病毒失败风险的患者.
- 预后病毒学评估可以指导早期的,有针对性的干预措施,如耐药性监测,坚持咨询或治疗转换.
- 建议进行外部验证以确认这些发现并支持临床实施.
相关概念视频
Steps in Outbreak Investigation
155
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
155
Statistical Methods for Analyzing Epidemiological Data
432
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
432
Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Kaplan-Meier Approach
197
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,...
197
Classification of Illness
7.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.6K
Methods of Documentation VII: EMR
869
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
869


