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相关概念视频

Data Collection I01:30

Data Collection I

6.3K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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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:
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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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,...
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Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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选择性预测用于提取非结构化临床数据.

Akshay Swaminathan1,2, Ivan Lopez1,2, William Wang3,4

  • 1Stanford University School of Medicine, Stanford, CA, United States.

Journal of the American Medical Informatics Association : JAMIA
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

选择性预测模型通过允许模型避免预测来改善非结构化的临床数据抽象. 与传统方法相比,这种方法提高了准确性和效率.

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科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 目前用于从非结构化临床数据中提取信息的方法,如手动抽象和结构化代理变量,通常是低效和不精确的.
  • 需要可扩展和准确的解决方案来处理越来越多的临床文本数据.

研究的目的:

  • 评估选择性预测模型在提高非结构化临床数据抽象的准确性和效率方面的有效性.
  • 将选择性分类器的性能与非选择性模型和结构化代理变量进行比较.

主要方法:

  • 训练有素的选择性分类器 (逻辑回归,随机森林,支持矢量机) 来提取五个变量:抑郁症,质母细胞瘤 (GBM),直肠腺癌 (DRA),腹腔切除 (APR) 和前腰切除 (LAR).
  • 改变了与虚假阳性,虚假阴性和弃权预测相关的成本,以衡量总错误分类成本.
  • 评估绩效指标,包括灵敏度,特异性,正预测值 (PPV) 和负预测值 (NPV).

主要成果:

  • 选择性分类器显示出显著的弃权率,抑郁症的比例从0%到97%,GBM和结直肠癌模型的比例从5%到43%.
  • 对于质母细胞瘤 (GBM) 提取,选择性分类器在43%的笔记中弃权,导致提高灵敏度 (0.94至0.96),特异性 (0.79至0.96),PPV (0.89至0.98),和NPV (0.88至0.91),与非选择性分类器和结构化代理变量相比.
  • 选择性预测模型在某些情况下将错误分类总成本降低了高达58%.

结论:

  • 选择性分类器在从非结构化临床笔记中提取数据时,超过了非选择性分类器和结构化代理变量.
  • 选择性预测是一个有价值的策略,当避免错误的预测比对每一个实例做出预测更为关键时.
  • 这种方法为更准确,更有效的临床数据抽象提供了一个有希望的途径.