AIによる意思決定における不確実性の最小化と定量化:医学における応用
Samuel D Curtis1,2,3,4,5, Sambit Panda6,7, Adam Li8
1Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205.
まとめ
多次元情報化一般化仮説テスト (MIGHT) は,AI予測の不確実性を正確に定量化し,特定のエラータイプを制御します. このAI戦略は 典型的なアプローチとは異なり 現実世界のデータ分析に 信頼できます
科学分野:
- 人工知能
- 生物医学データ分析
- 統計学的な学習
背景:
- AIはデータ分析に不可欠ですが,特定のエラータイプ (例えばスクリーニングにおける偽陽性) を制御することは困難です.
- AIの予測における不確実性を定量化することは,特にエラー制御においては,理論的および実用的な困難を伴う.
研究 の 目的:
- AI予測の不確実性を正確に定量化するための新しい戦略を開発する.
- AI主導の分析における特定のエラータイプに対する信頼性の高い制御を確保する.
- バイオメディカルスクリーニングなどの重要なアプリケーションで AIベースの発見を信頼するという課題に取り組むこと.
主な方法:
- 非パラメトリックなアンサンブル法である多次元情報型一般化仮説テスト (MIGHT) を導入した.
- MIGHT フレームワーク内の統合されたカノンカル・クロス・バリデーションとパラメタリック・カリブレーション
- MIGHTの性能はシミュレーションと液体生検データ (ccfDNA) に適用することで検証された.
主要な成果:
- MIGHTは理論上の保証で不確実性と信頼性を正確に定量化し,典型的なAIアプローチを上回ります.
- MIGHTは,SVM,ランダムフォレスト,およびトランスフォーマーと比較して,ccfDNAデータに対する有意に低い変動係数としばしば高い感度を示した.
- 変数セットを組み合わせることで,騒音の増加による感度が低下することが判明し,最適な変数選択の重要性を強調した.
結論:
- MIGHTは,特に特定のエラータイプを制御することが重要な場合に,AIベースのデータ分析のための信頼できる方法を提供します.
- この研究は,現実世界のデータの信頼できる解釈のための理論的保証で不確実性と信頼性を定量化する必要性を強調しています.
- MIGHTは,液体生検におけるバイオマーカー発見と検証のための堅実なソリューションを提供し,がん検出の未解決問題に取り組んでいます.
関連する概念動画
Issues And Trends In Healthcare Delivery System
5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K
Uncertainty: Confidence Intervals
4.7K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.7K
Uncertainty: Overview
977
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
977
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
174
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
174
Uncertainty in Measurement: Accuracy and Precision
79.5K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
79.5K
Decision Making: P-value Method
5.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.7K


