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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

358
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
358

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

Daily symptom monitoring is sustainable over months: retention, not compliance, is the primary barrier to long-duration digital tracking.

medRxiv : the preprint server for health sciences·2026
Same author

Computational signatures of uncertainty are reflected in motor cortex excitatory neurochemistry.

Nature communications·2025
Same author

Predictive modelling of clinically significant depressive symptoms after coronary artery bypass graft surgery: protocol for a multicentre observational study in two Swiss hospitals (the PsyCor study).

BMJ open·2025
Same author

Altered Decision-Making Across Acute and Chronic Pain States: A Bias Towards Context-Dependent Valuations.

bioRxiv : the preprint server for biology·2025
Same author

The interoceptive origin of reinforcement learning.

Trends in cognitive sciences·2025
Same author

The neuroscience of mental illness: Building toward the future.

Cell·2024
Same journal

Erratum for the Research Article "Detecting supramolecular organic nanoparticles during heat wave".

Science (New York, N.Y.)·2026
Same journal

Local signals, systemic decline.

Science (New York, N.Y.)·2026
Same journal

The mechanics of liver regeneration.

Science (New York, N.Y.)·2026
Same journal

Computing in a memory with physics.

Science (New York, N.Y.)·2026
Same journal

Retraction.

Science (New York, N.Y.)·2026
Same journal

Making time.

Science (New York, N.Y.)·2026
関連記事をすべて見る

関連する実験動画

Updated: Jul 5, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

11.9K

精密医療における実用的な課題

Frederike H Petzschner1

  • 1Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA.

Science (New York, N.Y.)
|January 11, 2024
PubMed
まとめ
この要約は機械生成です。

機械学習モデルは 個人が治療にどう反応するかを 正確に予測するのに苦労します パーソナライズされた医療の進歩には これらの課題を克服することが重要です

さらに関連する動画

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

24.4K
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

10.6K

関連する実験動画

Last Updated: Jul 5, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

11.9K
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

24.4K
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

10.6K

科学分野:

  • 計算生物学
  • ゲノミクス
  • バイオ統計学

背景:

  • 個々の治療反応を予測することは 精密医療の鍵です
  • 機械学習 (ML) は大きな可能性を秘めているが,大きな障害に直面している.

研究 の 目的:

  • 個々の治療反応を予測するために機械学習を適用する主な障害を特定し,分析する.
  • この分野における将来の研究開発の分野を強調する.

主な方法:

  • 治療応答の予測における機械学習アプリケーションに関する現在の文献のレビュー.
  • データの異質性,モデルの解釈性,検証などの共通の課題の分析

主要な成果:

  • 重要な課題は,限られた高品質で多様なデータセットです.
  • モデルの一般化と解釈は依然として重要な障壁です.
  • 倫理的な考慮と規制上の障害も進歩を妨げています.

結論:

  • データの不足に対処し,モデルの透明性を向上させることが重要です.
  • 臨床使用のための強固で解釈可能なMLモデルを開発するには,さらなる研究が必要です.
  • これらの障害を克服することで 個別化された治療戦略の採用が加速されます