Scholarlink

<p>The phenomenon of neonatal mortality is a significant issue in health care, which necessitates the use of advanced analytical tools for early risk prediction. This work utilized the Medical Information Mart (MIMIC) Pediatric Intensive Care (PIC) database to develop a framework for identifying key biomarkers associated with mortality risk. The combination of many clinical data sources comprising more than two thousand Neonatal Intensive Care Unit (NICU) Neonatal patient admissions (both alive and dead) were extracted. Biomarkers with an abnormal value were taken into consideration for further analysis. The patterns were analyzed using Long Short-Term Memory (LSTM) autoencoders that converted the biomarker data of both deceased and live neonates into meaningful representations. Similar biomarker patterns were clustered to gain insights into the mortality risk factors. The suggested framework is found to be superior to the existing neonatal scoring systems with a sensitivity of 88.1%, specificity of 89.5%, and Area Under Receiver Operating Characteristic Curve (AUCROC) of 0.92. These results indicate the possibility of employing the biomarker patterns as predictors of neonatal mortality and the basis of a decision-support model.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning-based neonatal outcome prediction: an LSTM autoencoder framework for pattern analysis and risk assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

查看全文
  • Safiya Parvin A.,
  • Saleena B.

摘要

The phenomenon of neonatal mortality is a significant issue in health care, which necessitates the use of advanced analytical tools for early risk prediction. This work utilized the Medical Information Mart (MIMIC) Pediatric Intensive Care (PIC) database to develop a framework for identifying key biomarkers associated with mortality risk. The combination of many clinical data sources comprising more than two thousand Neonatal Intensive Care Unit (NICU) Neonatal patient admissions (both alive and dead) were extracted. Biomarkers with an abnormal value were taken into consideration for further analysis. The patterns were analyzed using Long Short-Term Memory (LSTM) autoencoders that converted the biomarker data of both deceased and live neonates into meaningful representations. Similar biomarker patterns were clustered to gain insights into the mortality risk factors. The suggested framework is found to be superior to the existing neonatal scoring systems with a sensitivity of 88.1%, specificity of 89.5%, and Area Under Receiver Operating Characteristic Curve (AUCROC) of 0.92. These results indicate the possibility of employing the biomarker patterns as predictors of neonatal mortality and the basis of a decision-support model.