Rahman, Md. KhalilurAshraf, Muaz IbneIslam, S.M. SihatSiraz, Zasia Farzin2026-06-032026-06-0320252025ID 21101177ID 21301107ID 21201379http://hdl.handle.net/10361/28282Cataloged from PDF version of project report.Includes bibliographical references (pages 56-58).This project report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.Intensive care units (ICUs) and their high mortality rate often require predictive tools that could help to determine at-risk patients in time and direct the interventions. The given project presents a deep learning system that merges Convolutional Neural Networks (CNN) with the Long Short Term Memory (LSTM) networks to better predict the risk of mortality at an early stage among critical patients in ICUs. Using a significant portion of the patient data such as the vital signs and laboratory results, the model can carry out constant risk analysis and it could prove superior to the conventional scoring systems. Health suggestion module is also incorporated to make suggestions on clinical interventions to be made on high risk patients thus assisting healthcare providers with their decision-making. Finally, the suggested direction will enhance the patient outcomes as it will allow delivering proactive medical care and, thus, distributing ICU resources more reasonably.58 pagesenBRAC University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.ICU mortality predictionDeep learningMachine learningClinical decision supportTime-series dataReinforcement learningMortality.Deep learning (Machine learning).Time-series analysis--Data processing.Reinforcement learning.Project Report