The paper mentions LSTM model is used to process

The gap

Evidence profile

Sourced from the inline gaps and open questions of the source papers, classified as methodology gap, spanning 3 journals. Those papers have been cited 1 times in total.

Supporting evidence — 3 representative gaps

• Explainable Machine Learning Framework for Predicting Hospital Length of Stay to Enhance Healthcare Resource Management (2026) · Bonfring International Journal of Industrial Engineering and Management Science · doi

  The paper mentions LSTM model is used to process sequential data in healthcare but does not provide detailed results, comparison, or performance metrics for the LSTM model compared to other approaches.

  methodology gapinline gapsevidence 5/5
  Keywords: lstm model mentions used process sequential healthcare provide detailed comparison performance metrics compared approaches
• An LSTM-Based Time-Series Framework for Early Detection of Prostatitis Using Longitudinal Clinical Indicators (2026) · International Journal of Development Mathematics (IJDM) · doi

  The study lacks comparison with other temporal modeling approaches beyond the mentioned prior studies, leaving open the question of how this LSTM approach compares to other deep learning architectures for time-series medical data.

  methodology gapopen questionsevidence 5/5
  Keywords: lacks comparison temporal modeling approaches beyond mentioned prior leaving open question lstm approach compares deep
• Performance Analysis of a Hybrid Deep Learning Framework Integrating CNN, RNN, LSTM, and ResNet50 for Lung Disease Recurrence Prediction Using Chest X-Ray Images and Post-Recovery Clinical Data (2026) · Engineering, Technology & Applied Science Research · cited 1× · doi

  The LSTM model for temporal pattern modeling of lung disease recurrence operates on unspecified clinical features and visit sequences; the specific longitudinal variables (e.g., symptom progression markers, biomarker trends, treatment adherence), optimal sequence length, and temporal granularity (daily, weekly, monthly) that maximize LSTM predictive performance require systematic investigation.

  methodology gapinline gapsevidence 5/5
  Keywords: LSTM temporal patterns clinical data longitudinal modeling recurrence prediction sequence modeling

Questions about this gap