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During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
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Patients with hypertrophic cardiomyopathy (HCM) and left ventricular outflow tract (LVOT) obstruction who remain symptomatic despite optimal medical therapy may undergo a septal myectomy (Morrow procedure). This procedure involves excising a portion of the hypertrophied septum below the aortic valve using a heart-lung machine to improve blood flow through the LVOT. Effective preoperative and postoperative nursing management ensures successful patient outcomes, minimizes complications, and...
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Related Experiment Video

Updated: Sep 7, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.

Abbas M Hassan1, Andrea Biaggi-Ondina1, Aashish Rajesh2

  • 1Department of Plastic and Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

The American Surgeon
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

Patient-reported outcomes (PROs) provide valuable patient insights. Artificial intelligence (AI) and machine learning (ML) can predict PROs, enhancing surgical care and patient decision-making.

Keywords:
artificial intelligencedeep learningmachine learningpatient-reported outcomessurgery

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Area of Science:

  • Surgical outcomes research
  • Health informatics
  • Artificial intelligence in medicine

Background:

  • Patient-reported outcomes (PROs) are crucial for assessing treatment effectiveness, recovery, and satisfaction from the patient's viewpoint.
  • PROs facilitate a patient-centric approach, shifting focus from disease metrics to individual patient experiences and shared decision-making.
  • Integrating AI and ML offers a powerful method to analyze PROs and personalize patient care.

Approach:

  • This review examines the application of AI and ML models for predicting PROs in surgical contexts.
  • It covers common predictive algorithms and modeling techniques relevant to surgical outcome prediction.
  • The review discusses current uses and inherent limitations of AI/ML in predicting surgical PROs.

Key Points:

  • AI and ML can accurately predict patient-reported outcomes, improving personalized surgical care.
  • Predictive models enhance shared decision-making by incorporating patient perspectives.
  • Understanding AI/ML applications and limitations is vital for advancing surgical patient care.

Conclusions:

  • AI and ML hold significant potential to revolutionize the prediction of patient-reported outcomes in surgery.
  • These technologies can lead to more tailored treatments and improved patient satisfaction.
  • Further research and development are needed to fully leverage AI/ML for enhanced surgical patient journeys.