Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Patient Monitoring and Management shows how machine learning watches over patients continuously and flags problems before they escalate. You learn to work with vital-sign, wearable and monitoring data, and build models for early-warning and deterioration detection, alarm management, and supporting care of chronic and high-risk patients. The course connects these to real settings — from ICU to remote home monitoring — and to the alarm-fatigue, safety and reliability challenges that clinical monitoring demands. You finish able to reason about an AI patient-monitoring solution. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in patient monitoring and management — continuous monitoring, early-warning and deterioration detection, and AI-supported care management.
📋 Course Objectives
1. Work with vital-sign and monitoring data.
2. Build early-warning and deterioration models.
3. Reduce false alarms and alarm fatigue.
4. Support chronic and remote patient management.
5. Address safety and reliability in monitoring.
👥 Who Should Enroll?
• Clinical and critical-care professionals
• Health-tech and monitoring teams
• Biomedical data scientists
• Students of clinical technology
🚀 Key Learning Outcomes
• An understanding of AI in patient monitoring.
• An early-warning modelling perspective.
• A safety-focused clinical approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Physiological Data Acquisition
• ECG, SpO2, respiration and continuous vitals: sampling, resolution and artefact
• Motion artefact, lead-off and the alarm burden this creates
• Wearable versus bedside data quality, and what each can support clinically
Time-Series Preparation
• Filtering, resampling and handling irregular and missing observations
• Feature extraction, including heart-rate variability and waveform morphology
• Windowing and label alignment for event prediction
Deterioration and Early Warning
• Early warning scores as the baseline any model must beat
• Sepsis and deterioration prediction, and the published failures worth studying
• Lead time versus precision, and the ward capacity to respond
Reducing Fatigue Rather Than Adding to It
• Alarm fatigue as the dominant clinical failure mode of monitoring systems
• Alarm suppression, escalation logic and the safety case for silencing anything
• Human factors in the design of a bedside or remote monitoring display
Deployment Beyond the Ward
• Remote monitoring and hospital-at-home programme design
• Connectivity, device management and data governance outside the hospital
• Evaluating whether monitoring changed outcomes rather than just generating data
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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