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相关概念视频

Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Data Validation01:15

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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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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生物医学数据宣言:一个轻量级的数据文档映射,以提高AI/ML的透明度.

Daniel Bottomly1, Christopher G Suciu1,2, Benjamin Cordier1

  • 1Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.

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生物医学机器学习 (ML) 数据文档需要改进. 我们开发了生物医学数据宣言,一个模块化模板,以减少发电机负担并提高ML应用程序的透明度.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 生物医学机器学习 (ML) 模型需要强大的数据集文档来进行临床决策.
  • 目前的文档方法是繁重的,并将数据/模型问责制混为一谈,特别是在非ML数据集.
  • 机器学习算法的公平性取决于用户对数据来源和质量等问题的认识.

研究的目的:

  • 为弥补当前ML数据文档化实践中的差距.
  • 开发生物医学数据集的实用文档框架.
  • 提高ML应用中的透明度和偏差缓解.

主要方法:

  • 通过在四个关键模板中映射元素来导出共识文档字段.
  • 调查生物医学利益相关者 (临床医生,实验室科学家,数据管理人员,计算人员) 关于现场重要性.
  • 开发了生物医学数据宣言,这是一个模块化模板,具有个人特定的现场呈现.

主要成果:

  • 在生物医学利益相关者之间确定了角色依赖的优先级差异.
  • 生物医学数据宣言通过提供量身定制的信息来减少发电机负担.
  • 确保最终用户获得与角色相关的数据信息,以便更好地应用ML.

结论:

  • 生物医学数据宣言提高了公共/受控存储库中的数据集的透明度.
  • 通过更好地理解数据,提高机器学习应用程序中的偏差缓解.
  • 促进生物医学研究中负责任的数据共享和利用.