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Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis
- Source :
- Applied Sciences, Vol 11, Iss 1289, p 1289 (2021), Applied Sciences, Volume 11, Issue 3
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Electronic Medical Records (EMRs) can be used to create alerts for clinicians to identify patients at risk and to provide useful information for clinical decision-making support. In this study, we proposed a novel approach for predicting Amyotrophic Lateral Sclerosis (ALS) based on comorbidities and associated indicators using EMRs. The medical histories of ALS patients were analyzed and compared with those of subjects without ALS, and the associated comorbidities were selected as features for constructing the machine learning and prediction model. We proposed a novel weighted Jaccard index (WJI) that incorporates four different machine learning techniques to construct prediction systems. Alternative prediction models were constructed based on two different levels of comorbidity: single disease codes and clustered disease codes. With an accuracy of 83.7%, sensitivity of 78.8%, specificity of 85.7%, and area under the receiver operating characteristic curve (AUC) value of 0.907 for the single disease code level, the proposed WJI outperformed the traditional Jaccard index (JI) and scoring methods. Incorporating the proposed WJI into EMRs enabled the construction of a prediction system for analyzing the risk of suffering a specific disease based on comorbidity combinatorial patterns, which could provide a fast, low-cost, and noninvasive evaluation approach for early diagnosis of a specific disease.
- Subjects :
- Jaccard index
amyotrophic lateral sclerosis (ALS)
Computer science
Pattern analysis
Disease
Machine learning
computer.software_genre
lcsh:Technology
lcsh:Chemistry
03 medical and health sciences
0302 clinical medicine
medicine
General Materials Science
030212 general & internal medicine
Amyotrophic lateral sclerosis
Instrumentation
lcsh:QH301-705.5
electronic medical record (EMR)
Fluid Flow and Transfer Processes
Receiver operating characteristic
business.industry
lcsh:T
Process Chemistry and Technology
Medical record
General Engineering
medicine.disease
Comorbidity
lcsh:QC1-999
Computer Science Applications
weighted Jaccard index (WJI)
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
computer
030217 neurology & neurosurgery
Predictive modelling
lcsh:Physics
disease prediction
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 11
- Issue :
- 1289
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....d2f1e1c78fc18b5f81025ee138e57eb9