Machine Learning Driven Healthcare Monitoring System
For a UK-based medical service provider to improve clinical efficiency and patient care
About
Creation of a system that offers real-time human activity recognition within the clinical systems to provide better diagnosis & treatment for critical care patients
Capability
Analytics
Industry
Healthcare
Country
UK
Key Features
The ML-based healthcare monitoring system offers actionable insights to help collate the recorded data (historical & current health data sets) from various connected devices along with the patient’s vitals.
Data collection
Readings are collected from various sources, including historical records, prescription, wearable patient sensors, and even equipment such as arterial blood gas (ABS) analyzer, echocardiography, and more
Improved efficiency
The simplified process helps convert varied data sets across different device protocols in a unified format for further analysis, reporting, diagnosis and, sometimes, treatment
Continuous monitoring
The system classifies the activities performed & recorded by different patients in a continuous sequence – while they are sleeping, sitting, being fed, walking, and more
Centralized health management
The AI-powered patient monitoring system offers real-time insights and alerts about health & performance, ensuring instant data interaction with actionable outputs
Challenges
- No system in place to understand diverse device criticality and functionality
- Manual & time-consuming to do analysis of historical & current data of the patients
- No way to understand, diagnose and project next steps in patient-care in faster way
- Need to offer real-time assistance for critical care patients
Solution
We created ML-based data models for the connected devices to enable activity recognition through a secure & reliable patient health monitoring system. The model enables the medical care provider to record & analyze large data sets of patient health in real-time to make informed decisions.
Data set & deployment
The data set was divided into observation cycle windows of one second with a 50% overlap and further deployed into the AI system at scale on users’ devices
Smart data model
The model extracts the data for the mapping & analysis of the patient’s health status through connected devices, including a heart rate sensor, VO2, ECG, and more
Unified dashboard
The customized interface helps incorporate metrics beyond traditional activity identification models, and creates dashboard based on various parameters such as gender, age, illness, treatment group, and more
Predictive analysis
It enables doctors to easily track & identify patterns of multiple patient activities and define an accurate, pre-emptive diagnosis of ailments
Technologies Used
Project Snapshots
5x
increase in operational efficiency
63%
accurate diagnosis through ML models
Real-time
data collection & patient-intelligence
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