Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis
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...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/3/1289 |
id |
doaj-14658619be7d4ce89c580178c531fcc1 |
---|---|
record_format |
Article |
spelling |
doaj-14658619be7d4ce89c580178c531fcc12021-02-01T00:03:09ZengMDPI AGApplied Sciences2076-34172021-01-01111289128910.3390/app11031289Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral SclerosisChia-Hui Huang0Bak-Sau Yip1David Taniar2Chi-Shin Hwang3Tun-Wen Pai4Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Neurology, National Taiwan University Hospital Hsin Chu Branch, Hsin Chu 30059, TaiwanDepartment of Software Systems and Cybersecurity, Monash University, Victoria 3800, AustraliaDepartment of Neurology, ShuTien Hospital, Taipei 10662, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, TaiwanElectronic 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.https://www.mdpi.com/2076-3417/11/3/1289electronic medical record (EMR)disease predictionamyotrophic lateral sclerosis (ALS)weighted Jaccard index (WJI) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chia-Hui Huang Bak-Sau Yip David Taniar Chi-Shin Hwang Tun-Wen Pai |
spellingShingle |
Chia-Hui Huang Bak-Sau Yip David Taniar Chi-Shin Hwang Tun-Wen Pai Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis Applied Sciences electronic medical record (EMR) disease prediction amyotrophic lateral sclerosis (ALS) weighted Jaccard index (WJI) |
author_facet |
Chia-Hui Huang Bak-Sau Yip David Taniar Chi-Shin Hwang Tun-Wen Pai |
author_sort |
Chia-Hui Huang |
title |
Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis |
title_short |
Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis |
title_full |
Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis |
title_fullStr |
Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis |
title_full_unstemmed |
Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis |
title_sort |
comorbidity pattern analysis for predicting amyotrophic lateral sclerosis |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-01-01 |
description |
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. |
topic |
electronic medical record (EMR) disease prediction amyotrophic lateral sclerosis (ALS) weighted Jaccard index (WJI) |
url |
https://www.mdpi.com/2076-3417/11/3/1289 |
work_keys_str_mv |
AT chiahuihuang comorbiditypatternanalysisforpredictingamyotrophiclateralsclerosis AT baksauyip comorbiditypatternanalysisforpredictingamyotrophiclateralsclerosis AT davidtaniar comorbiditypatternanalysisforpredictingamyotrophiclateralsclerosis AT chishinhwang comorbiditypatternanalysisforpredictingamyotrophiclateralsclerosis AT tunwenpai comorbiditypatternanalysisforpredictingamyotrophiclateralsclerosis |
_version_ |
1724315939897344000 |