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関連する概念動画

Urinary Tract Infection I: Introduction01:26

Urinary Tract Infection I: Introduction

740
Urinary tract infections (UTIs) impact various parts of the urinary system, including the kidneys, ureters, bladder, and urethra. These infections are generally bacterial, with Escherichia coli being the most common causative agent, often originating from the gastrointestinal tract. However, other bacteria, such as Staphylococcus saprophyticus, Klebsiella pneumoniae, and Proteus mirabilis, are also known to cause UTIs. The type, location, and underlying complexity of the UTI guide both...
740
Urinary Tract Infection II: Pathophysiology01:25

Urinary Tract Infection II: Pathophysiology

799
The pathophysiology of urinary tract infections (UTIs) encompasses several progressive stages, beginning with bacterial colonization and culminating in potential systemic complications if untreated. UTIs are primarily initiated by bacteria, such as Escherichia coli, which often originate from the gastrointestinal tract and migrate to the urinary system through the periurethral area. This migration can occur via several routes, including improper hygiene practices, sexual activity, or...
799
Urinary Tract Infection IV: Nursing Management01:17

Urinary Tract Infection IV: Nursing Management

503
In managing urinary tract infections (UTIs) in nursing, a comprehensive assessment is essential. Begin by gathering subjective data, such as the patient’s complaints of dysuria (painful urination), urinary frequency, urgency, suprapubic pain, and any lower abdominal discomfort. This information can be complemented by questions regarding previous UTIs, sexual activity, and personal hygiene practices, which can provide insight into risk factors. Objective assessment should focus on signs...
503
Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care01:30

Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care

330
A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
330
Urinary Tract Calculi I: Introduction01:28

Urinary Tract Calculi I: Introduction

574
Renal calculi, or kidney stones, are solid deposits of minerals and salts formed inside the kidneys. In medical terminology, "calculus" refers to the stone itself, while "lithiasis" describes the process of stone formation. Depending on their location within the urinary system, these stones may be classified as either urolithiasis, when situated within the urinary tract, or nephrolithiasis, when located within the kidneys. Each term signifies the specific impact of the stone.Predisposition...
574
Urinary Tract Calculi V: Nursing Management01:28

Urinary Tract Calculi V: Nursing Management

322
AssessmentSubjective Data: Obtain a detailed health history, including any recent or chronic urinary tract infections, periods of immobilization, previous episodes of renal calculi, and medical conditions such as gout, benign prostatic hyperplasia, or hyperparathyroidism. Review the medication history for drugs that may influence stone formation, including allopurinol, analgesics, loop diuretics, or thiazide diuretics. Document the use of long-term indwelling catheters and any past surgical...
322

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Updated: Feb 13, 2026

An In Vitro Bladder Model of Catheter-Associated Urinary Tract Infection
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ESBLを生成する細菌によって引き起こされる小児性尿路感染症を予測するための機械学習ツール

Chen Hajaj1, Shani Alkoby1, Shai Ashkenazi2,3

  • 1Research Authority, Rabin Medical Center, Petach Tikva, Israel.

The Pediatric infectious disease journal
|February 12, 2026
PubMed
まとめ
この要約は機械生成です。

機械学習モデルは,拡張スペクトルβ-乳糖酶 (ESBL) を生成する細菌によって引き起こされる小児性尿路感染症 (UTIs) を予測することができます. これらのツールは,臨床医が適切な抗生物質選択のための高リスク症例を特定するのに役立ちます.

キーワード:
抗生物質耐性 抗生物質耐性に対する耐性抗生物質の管理について子供 子供 子供 子供 子供 子供機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.尿路感染症 尿路感染症

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関連する実験動画

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科学分野:

  • 小児感染症は,小児の感染症です.
  • 医療情報工学 医療情報工学
  • 医療における機械学習

背景:

  • 拡張スペクトルβ-乳糖酶 (ESBL) を産生する細菌によって引き起こされる小児性尿路感染症 (UTIs) の世界的な流行率が増加しています.
  • ESBL-UTIsは,特殊な抗生物質治療を必要とし,しばしば経験的治療の遅延,ICU入院の増加,罹病率,および長期の入院につながります.
  • ESBL-UTIsを予測することは困難ですが,適切な患者管理をタイムリーにするために不可欠です.

研究 の 目的:

  • ESBLを生成する細菌によって引き起こされる小児性尿路感染症を予測するための機械学習 (ML) モデルを開発および検証する.
  • 小児科医が,ESBL陽性性尿路炎のリスクの高い子どもを特定するのを支援するためです.
  • ESBL-UTIsに対する適切な経験的抗生物質治療の早期開始を可能にする.

主な方法:

  • 2010年1月から2020年8月までの期間において,確認された尿路感染症の小児患者 (1ヶ月から18歳) の電子医療記録の遡及分析.
  • データ抽出には,人口統計,臨床情報,検査結果が含まれていました.
  • 5つのMLモデルを開発し,ESBL陽性細菌感染を予測するために,UTIプレゼンテーションで利用可能な患者データを使用して5つのMLモデルを開発しました.

主要な成果:

  • この研究では,35,830件の小児性尿路感染症の症例を分析した.
  • ESBL陽性尿路感染症と有意に関連した要因には,年齢,性別,社会経済的地位,感染部位,以前の抗生物質使用,以前のESBL-UTI歴,および特定の尿病原体が含まれています.
  • 開発されたMLモデルでは,高い負の予測値 (~0.98) が示され,ESBL陽性尿路感染症の排除において高いパフォーマンスを示した.

結論:

  • 尿路感染症のプレゼンテーションで利用可能なデータを活用した機械学習モデルは,小児におけるESBLを生成する細菌性尿路感染症の確率を評価するのに臨床医を助けることができます.
  • これらのモデルは,経験的な抗生物質の選択のための臨床的意思決定を支援する見込みを示しています.
  • モデルのパフォーマンスを洗練し,臨床結果への影響を評価するために,さらなる見通し研究が必要です.