− HEALTH There is a growing literature on the prediction of risk of deterioration in hospital settings, including by l…− SCIENCE Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity o…+ HEALTH Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed gene…− HEALTH Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissectio…− HEALTH Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but out…− HEALTH Background Large language model (LLM)-generated hospital courses are increasingly integrated into electronic…− HEALTH Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporati…+ HEALTH Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (…
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TRV-2026-0978Version 1 · Certified

Written 2026-09-04 06:04:52 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-0978
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-04T06:04:52.044392Z
status: published
lens: p_space
sector: health
headline: Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis
dek: Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction. Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20…
gain_title: (none)
problem_title: Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).
problem_evidence: (none)
quick_read: Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction.

Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).
limitation: 
tag: Evidence-backed problem
key_points: Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. | Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. | Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).
rundown: Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction.

Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).
sources:
- peer_reviewed | Expert Opinion on Biological Therapy | https://doi.org/10.1080/14712598.2026.2727098 | 2026-09-03
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