Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management

Appendicitis-II: Diagnostic Studies and Management

Diagnosing and managing appendicitis requires a structured and comprehensive approach that spans from initial assessment to postoperative care. Here is an overview of the process:
Diagnosing Appendicitis
It requires a multifaceted approach, starting with a detailed physical examination to pinpoint the location and nature of the pain and identify any associated symptoms. Laboratory tests play a crucial role. A complete Blood Count (CBC) typically reveals leukocytosis (an increased number of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

UbiDash: A UPS proteomic atlas for tissue-aware degrader design.

Cell death and differentiation·2026

Same author

From Primary Melanoma to Metastatic Evolution: AI-Powered Pathology Integrated with Functional Analysis and Clinical Metadata Improving Treatment Prediction.

Cancers·2026

Same author

Future Directions: Artificial Intelligence and Digital Tools in Bladder Cancer Care.

The Urologic clinics of North America·2026

Same author

Focal Cryo-Ablation for Treatment of Intermediate Favorable Risk (Grade Group 2) Prostate Cancer.

Urology·2026

Same author

Blinded by the Smoke: Are We Ignoring a Key Modifier of Response in NMIBC Trials?

The Journal of urology·2026

Same author

Shared Decision Making for Prostate Cancer Screening is a System Responsibility.

The Journal of urology·2026

Search research articles

Related Experiment Video

Updated: Sep 6, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Machine learning decision support model for radical cystectomy discharge planning.

Calvin C Zhao¹, Marc A Bjurlin², James S Wysock¹

¹New York University Grossman School of Medicine, Department of Urology, New York, NY.

Urologic Oncology

|June 24, 2022

Summary

A machine learning model accurately predicts the need for higher-level care after radical cystectomy (RC), improving discharge planning for bladder cancer patients. This AI tool supports timely clinical decisions for better patient outcomes.

Keywords:

Bladder cancer Machine learning Radical cystectomy

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Sep 6, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Urology and Oncology
Artificial Intelligence in Healthcare
Health Services Research

Background:

Discharge planning after radical cystectomy (RC) for bladder cancer is complex.
Predicting the need for higher-level care post-RC is crucial for patient management.
Current clinical indices may not fully capture discharge disposition needs.

Purpose of the Study:

To develop and validate a machine learning model for predicting non-home discharge after RC.
To improve the timeliness and appropriateness of discharge planning for RC patients.
To identify key predischarge factors influencing discharge location.

Main Methods:

Utilized the ACS-NSQIP database (2014-2019) for patients undergoing elective RC.
Trained a gradient boosted decision tree model on predischarge variables.
Employed threshold-moving for calibration and evaluated performance using ROC and precision-recall curves.

Main Results:

The model achieved an AUC of 0.80 and average precision of 0.33.
Post-calibration, the model demonstrated a recall of 0.757 and precision of 0.211.
Key predictors included septic shock, prolonged ventilator use, surgical site infection, and unplanned intubation.

Conclusions:

The machine learning model effectively identifies patients requiring higher-level care post-RC.
This predictive tool outperforms existing clinical indices and prior research.
Machine learning offers a promising approach to enhance clinical decision-making in discharge planning.