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Automated machine learning for endemic active tuberculosis prediction from multiplex serological data
- Source :
- Scientific reports, vol 11, iss 1, Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021), Scientific Reports
- Publication Year :
- 2021
- Publisher :
- eScholarship, University of California, 2021.
-
Abstract
- Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models. These models were then further tested for generalizability on out-of-sample secondary and tertiary datasets. Out of 31 antigens evaluated, a 23-antigen model was the most robust on both the secondary dataset (TB vs healthy) and the tertiary dataset (TB vs COPD) with sensitivity of 90.5% and respective specificities of 100.0% and 74.6%. MILO represents a user-friendly, end-to-end solution for automated generation and deployment of optimized models, ideal for applications where rapid clinical implementation is critical such as emerging infectious diseases.
- Subjects :
- Adult
Male
Computer science
Science
Feature selection
Bioengineering
Machine learning
computer.software_genre
Article
Model validation
Machine Learning
Young Adult
Rare Diseases
Theoretical
Models
Humans
Tuberculosis
Multiplex
Generalizability theory
Lung
Retrospective Studies
screening and diagnosis
Multidisciplinary
business.industry
Diagnostic markers
Models, Theoretical
Active tuberculosis
4.1 Discovery and preclinical testing of markers and technologies
Detection
Infectious Diseases
Emerging Infectious Diseases
Good Health and Well Being
Medicine
Female
Artificial intelligence
business
Infection
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Scientific reports, vol 11, iss 1, Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021), Scientific Reports
- Accession number :
- edsair.doi.dedup.....9f137f568e6585c99557d2b9a75d724d