Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Local Anesthetics: Pharmacokinetics01:13

Local Anesthetics: Pharmacokinetics

850
The potency and duration of action of local anesthetics (LAs) are determined by their pharmacokinetics. Pharmacokinetics describes how LAs are absorbed, distributed, metabolized, and eliminated from the body. When administered to the vascular tissues, LAs are quickly absorbed and enter the systemic circulation, reducing their localized effects. Adding vasoconstrictors such as epinephrine to LAs reduces their absorption into the systemic circulation, making them clinically effective. The...
850
Local Anesthetics: Chemistry and Structure-Activity Relationship01:27

Local Anesthetics: Chemistry and Structure-Activity Relationship

5.4K
Local anesthetics (LAs) are drugs that induce a temporary loss of sensation in a limited body area, preventing pain. Cocaine was the first local anesthetic discovered in the late 19th century. Cocaine is a benzoic acid ester obtained from the leaves of coca shrubs and was often used for its psychotropic effects. Cocaine was first isolated in 1860 by Albert Niemann. Sigmund Freud studied the physiological actions of cocaine. Carl Koller later introduced it into clinical practice in 1884 as a...
5.4K
Drug Nomenclature01:17

Drug Nomenclature

2.3K
During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
2.3K
Local Anesthetics: Mechanism of Action01:23

Local Anesthetics: Mechanism of Action

2.5K
Local anesthetics (LAs) block sensory and motor impulses by inhibiting the sodium channels on the nerve cell membranes. This induces temporary loss of sensation, relieving pain in a specific body area.
Local anesthetics are amphiphilic molecules consisting of a hydrophobic aromatic part linked to a hydrophilic group by an ester or amide linkage. They are weak bases and are usually available as salts, which increases their solubility and stability. Once administered, LAs exist in the body either...
2.5K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.1K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.1K
Local Anesthetics: Common Agents and Their Applications01:23

Local Anesthetics: Common Agents and Their Applications

548
Local anesthetics (LAs) are commonly used for various applications in medical and dental procedures. Some of the common agents used are cocaine, lidocaine, and bupivacaine.
Cocaine is an ester of benzoic acid and methylecgogine. It is used to anesthetize and vasoconstrict locally. Currently, it is used primarily for topical applications. It is beneficial for surgeries on the upper respiratory tract, providing anesthesia and shrinking the mucosa. Cocaine in the form of cocaine hydrochloride is...
548

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

QutRNA2: robust tRNA modification discovery from Nanopore direct tRNA sequencing.

NAR genomics and bioinformatics·2026
Same author

Circtools 2.0: a comprehensive framework for enhanced circular RNA bioinformatics.

BMC bioinformatics·2026
Same author

The SURROGATOR Framework for Context-Aware Surrogation of Privacy Sensitive Information in Medical Text.

Studies in health technology and informatics·2026
Same author

RBM20 isoform regulation by independent transcription start sites adapts alternative splicing in development and disease.

Nature communications·2026
Same author

CAMK2D causes heart failure in mice with RBM20 cardiomyopathy.

Nature cardiovascular research·2026
Same author

Enhancing KLF15 activity in cardiomyocytes: a novel approach to prevent pathological reprogramming and fibrosis via nuclease-deficient dCas9VPR.

Signal transduction and targeted therapy·2026

関連する実験動画

Updated: Sep 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

薬の情報抽出は,ローカルな大型言語モデルを用いて行われます.

Phillip Richter-Pechanski1, Marvin Seiferling2, Christina Kiriakou3

  • 1Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120 Heidelberg, DE, Germany; Department of Internal Medicine III, University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, DE, Germany; German Center for Cardiovascular Research (DZHK) - Partner site Heidelberg/Mannheim, Im Neuenheimer Feld 669, 69120 Heidelberg, DE, Germany; Informatics for Life, Im Neuenheimer Feld 669, 69120 Heidelberg, DE, Germany; Department of Computational Linguistics, Heidelberg University, Im Neuenheimer Feld 325, 69120 Heidelberg, DE, Germany.

Journal of biomedical informatics
|August 23, 2025
PubMed
まとめ

精密調整されたローカル・ラッグ・ランゲージ・モデル (LLM) は,臨床テキストから最先端の薬物抽出を実現し,正確性と透明性を向上させます. これらのモデルは現実の医療環境で 効率的で信頼性の高いソリューションを提供します

キーワード:
クリニカルNLP微調整する解釈可能性大規模な言語モデルラマ薬の情報抽出

さらに関連する動画

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

575
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

634

関連する実験動画

Last Updated: Sep 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

575
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

634

科学分野:

  • 医療情報学
  • 自然言語処理
  • 人工知能

背景:

  • 構造化されていない臨床テキストから薬剤情報を抽出することは不可欠ですが,手作業とエラーのために困難です.
  • 現在の自動化方法は,専門知識の要求,時間制限,ITインフラストラクチャ,透明性などの制約に直面しています.
  • ゲネラティブ・ラッグ・ランゲージ・モデル (LLM) とパラメータ効率的な微調整は,有望な解決策を提供します.

研究 の 目的:

  • 自動化されたエンドツーエンドの医薬品情報抽出のためのローカルLLMを評価する.
  • 英語とドイツ語の臨床データセットの両方で微調整されたLLMのパフォーマンスを評価する.
  • 予測の透明性を高めるために,説明可能なテクニックを使用します.

主な方法:

  • 地元のLLMで命名されたエンティティ認識と関係抽出を使用しました.
  • 形式制限の指示と評価のための自動フィードバックパイプラインを使用します.
  • トークンレベルのシャプリー値を適用して,トークンの貢献を視覚化および定量化します.

主要な成果:

  • 精密調整されたラマモデルは,英語のデータで新しい最先端の結果を出し,F1スコアを最大10pp改善しました. 薬剤による副作用と6 pp. 薬の理由から
  • Llamaはドイツのデータセットで新しいベンチマークを確立し,従来の方法を最大16pp上回りました. マイクロ平均F1スコア
  • OpenBioLLMは 構造化された出力の限界と 幻覚を経験した.

結論:

  • 薬の情報を抽出するための 最先端の方法を超えています
  • これらのモデルは,英語とドイツ語の両方で有効な臨床環境で,限られたリソースで高性能を提供します.
  • シェープリー値は予測の透明性を高め,臨床意思決定を支援します.