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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.7K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.7K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Long-term Depression01:05

Long-term Depression

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Review and Preview01:10

Review and Preview

8.4K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Review and Preview01:13

Review and Preview

11.6K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Updated: Feb 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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標準ラベルを用いた機械学習によるテキストベースのうつ病推定：システマティックレビューとメタアナリシス

Shengming Zhang1, Chaohai Zhang1, Jiaxin Zhang1,2

  • 1School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, Guangdong, China.

Journal of medical Internet research
|February 11, 2026
PubMed
まとめ
この要約は機械生成です。

標準ラベルを用いたテキストベースのうつ病推定モデルは、強力な予測性能を示すことがわかった。埋め込み特徴量、深層学習、臨床医の診断は精度を大幅に向上させ、メンタルヘルススクリーニングにおける信頼性の高いデータと報告の重要性を強調している。

キーワード:
TRIPOD透明性のある多変量予測モデルの報告（Individual Prognosis Or Diagnosis）うつ病自然言語処理標準ラベルテキスト

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科学分野:

  • 自然言語処理
  • 機械学習
  • メンタルヘルス

背景:

  • うつ病は日常生活に著しい影響を与え、自殺行動につながる可能性がある。
  • テキストベースのうつ病推定は、早期のメンタルヘルススクリーニングのための実行可能なアプローチを提供する。
  • 既存のレビューでは、弱いラベルが使用されることが多く、モデルの信頼性と実用的な応用が制限されていた。

研究 の 目的:

  • 標準ラベルを用いたテキストベースのうつ病モデルの予測性能を評価する。
  • テキストリソース、表現、モデルアーキテクチャ、アノテーションソース、報告品質を含むパフォーマンスの異質性に影響を与える要因を特定する。

主な方法:

  • 2014年から2025年までの4つの主要なデータベース（PubMed、Scopus、IEEE Xplore、Web of Science）を対象としたPRISMA 2020ガイドラインに従った体系的な文献検索。
  • 参加者が生成したテキストと検証済みのうつ病ラベル（臨床診断またはスケール）を使用して機械学習モデルを開発した研究を含めた。
  • プールされた効果量（r）を計算するためにランダム効果メタアナリシスを実施し、サブグループ/メタ回帰分析を実行した。

主要な成果:

  • 11の研究から15のモデルを分析し、大きな全体的な効果量（r=0.605）を明らかにした。
  • 埋め込みベースのテキスト表現（r=0.741）と深層学習アーキテクチャ（r=0.731）は、それぞれ従来の特徴量と浅いモデルを大幅に上回った。
  • 臨床医の診断を使用したモデル（r=0.688）は、自己報告スケールを使用したモデル（r=0.500）よりも高いパフォーマンスを示した。
  • 透明性のある報告はパフォーマンスと正の相関があった（β=0.085）。

結論:

  • 標準ラベルを持つテキストベースのうつ病推定モデルは、堅牢な予測能力を示す。
  • 埋め込み特徴量、深層学習アーキテクチャ、および臨床医の診断は、モデルパフォーマンス向上の重要な推進要因である。
  • うつ病スクリーニングモデルの信頼性と実用的な有用性を高めるためには、標準ラベル、特徴量表現、および透明性のある報告の重要な役割を強調する。