Real-World Data and Machine Learning to Predict Cardiac Amyloidosis
Elena García-García1, Gracia María González-Romero1, Encarna M Martín-Pérez2
1Fundación San Juan de Dios, Centro CC de la Salud San Rafael, Universidad Nebrija, 28036 Madrid, Spain.
International Journal of Environmental Research and Public Health
|January 26, 2021
Summary
This study introduces a statistical learning algorithm to detect cardiac amyloidosis, also known as "stiff heart syndrome." The algorithm effectively identifies this rare heart condition using clinical records, aiding in early diagnosis.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Cardiac amyloidosis, or "stiff heart syndrome," is a rare restrictive cardiomyopathy characterized by amyloid deposits in the heart muscle.
- Delayed diagnosis is common due to the condition's rarity and diagnostic challenges, often leading to a poor prognosis.
Purpose of the Study:
- To analyze the characteristics of cardiac amyloidosis.
- To propose and validate a statistical learning algorithm for detecting the disease using clinical records.
Main Methods:
- Utilized hospitalization clinical records (medical and nursing) for algorithm training and learning.
- Treated patient admission and discharge data as vectors, addressing high dimensionality and class imbalance due to low disease prevalence.
Main Results:
- Demonstrated the feasibility of learning from limited clinical data for rare diseases like cardiac amyloidosis.
- The algorithm successfully detected the disease when applied to general and heart failure patient data, identifying disease episodes through data vector profiling.
Conclusions:
- The developed statistical learning algorithm shows potential for screening specific populations to improve early detection of cardiac amyloidosis.
- This predictive technique can aid in identifying patients with this challenging heart condition.
