Authors:
Landon Brand
;
Aditya Patel
;
Izzatbir Singh
and
Clayton Brand
Affiliation:
Stasis Labs, United States
Keyword(s):
CNN, Machine Learning, Mimic, Mortality Prediction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Machine Learning in Healthcare shows great promise, but is often difficult to implement due to difficulties in collecting data. We used a 1-dimensional convolutional neural network(CNN) on limited data to show a practical application of deep learning in healthcare. We used only vital signs data that can be collected from low cost, readily available hardware designed for non-critical care settings, and a dynamic model that updates as more data is collected over time. Our data is derived from the MIMIC dataset. We use 320 patients for testing and 2,990 for training the model. The CNN model predicted mortalities with up to a 76.3% accuracy, and outperformed both recurrent neural network and multi-layer perceptron models. To our knowledge, the proposed methodology is the first of its kind to predict mortality risk scores based on only heart rate, respiratory rate, and blood pressure, three easily collectible data.