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関連する概念動画

Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

428
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
428
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

430
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
430
Clinical Trials: Overview01:11

Clinical Trials: Overview

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Clinical Trials01:16

Clinical Trials

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
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Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

507
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
507
Subviral Agents01:29

Subviral Agents

589
Subviral agents are infectious entities that resemble viruses but lack one or more viral components, such as a capsid or essential replication machinery. These agents include viroids, prions, and satellites, each possessing distinct structural and functional characteristics that influence their mode of infection and replication.Viroids are the simplest subviral agents, consisting of circular, single-stranded RNA molecules without a protein coat. They exclusively infect plants, relying entirely...
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Updated: Feb 14, 2026

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EC2Seq2Sql: LLMエージェントとの患者試験マッチング

Liu Yang1, Yongzhong Han1, Liang Liu2

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.

PloS one
|February 12, 2026
PubMed
まとめ

EC2Seq2Sqlは,臨床試験の適格性基準を,電子医療記録 (EHR) データに対するSQLクエリに自動的に変換します. このフレームワークは,自然言語の基準と構造化されたEHRスキーマを橋渡しすることで,臨床試験のための患者のスクリーニングを強化します.

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Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
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科学分野:

  • バイオメディカル・インフォマティクス
  • 医療における人工知能
  • クリニック・トライアル・リクルートメント

背景:

  • 臨床試験の募集は,自然言語の適格性基準と構造化された電子医療記録 (EHR) データの不一致のために課題に直面しています.
  • 臨床試験の適格性を決める手動の患者スクリーニングは,時間がかかり,誤りやすいものです.

研究 の 目的:

  • EC2Seq2Sqlの開発と評価は,ナラティブ臨床試験の適格性基準を実行可能なSQLクエリに変換するための自動化されたフレームワークです.
  • EHRデータを用いた臨床試験の患者識別の効率と正確性を向上させるため.

主な方法:

  • 2段階のフレームワーク: 1) BARTベースの意味解析器で,テキスト基準を構造化パターンに変換する. 2) LLMベースのエージェントの接地パターンをデータベースのスキーマに組み込み,SQLクエリを生成する.
  • ClinicalTrials.govのデータセットと現実世界の肝細胞癌 EHRコホートに関する評価.

主要な成果:

  • BARTパーサはROUGE_L 0.8067とBLEU 0.8427.7を達成しました.
  • SQL生成段階では,0.84の正確なマッチ精度と0.91の実行精度を達成しました.
  • 現実のコホートに関するクエリは,0.88の臨床マッチの精度を達成し,効果的な患者リトレイが実証されました.

結論:

  • EC2Seq2Sqlは,EHRベースの患者のスクリーニングのために,適格性基準をSQLに変換することを効果的に自動化します.
  • このフレームワークは手動スクリーニングの努力を減らし,コホート識別のための再現可能な方法を提供します.
  • 大規模なマルチセンター展開にはさらなる検証が必要である.