Building predictive models for MERS-CoV infections using data mining techniques

Al-Turaiki, Isra; Alshahrani, Mona; Almutairi, Tahani Mazyad

doi:10.1016/j.jiph.2016.09.007

Cited by 69 publications

(71 citation statements)

References 5 publications

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“…Older age (65 years or over) was a major predictor of longer recovery delay in our sample. This was noted by Al‐Turaiki et al, as well . In other recent studies, being of older age was a factor for worse clinical outcomes such as infection severity and death in MERS‐CoV patients.…”

Section: Discussionmentioning

confidence: 55%

“…As per the authors’ knowledge, two studies have so far addressed clinical improvement on laboratory‐confirmed MERS‐CoV patients . The first study, Shalhoub et al, was based on a case report in which their observations may not be generalized to a wider MERS‐CoV population.…”

Section: Introductionmentioning

confidence: 99%

“…The first study, Shalhoub et al, was based on a case report in which their observations may not be generalized to a wider MERS‐CoV population. The second study, Al‐Turaiki et al, utilized publicly available data from the Saudi Ministry of Health. The major shortcomings in their study were several potential confounding factors such as underlying medical conditions and a primary or secondary mode of MERS‐CoV transmission that had not been included in the analysis.…”

Section: Introductionmentioning

confidence: 99%

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Factors associated with recovery delay in a sample of patients diagnosed by MERS‐CoV rRT‐PCR: A Saudi Arabian multicenter retrospective study

Ahmed

Al‐Jahdali

Alaqeel

et al. 2018

Influenza Resp Viruses

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BackgroundResearch evidence exists that poor prognosis is common in Middle East respiratory syndrome coronavirus (MERS‐CoV) patients.ObjectivesThis study estimates recovery delay intervals and identifies associated factors in a sample of Saudi Arabian patients admitted for suspected MERS‐CoV and diagnosed by rRT‐PCR assay.MethodsA multicenter retrospective study was conducted on 829 patients admitted between September 2012 and June 2016 and diagnosed by rRT‐PCR procedures to have MERS‐CoV and non‐MERS‐CoV infection in which 396 achieved recovery. Detailed medical charts were reviewed for each patient who achieved recovery. Time intervals in days were calculated from presentation to the initial rRT‐PCR diagnosis (diagnosis delay) and from the initial rRT‐PCR diagnosis to recovery (recovery delay).ResultsThe median recovery delay in our sample was 5 days. According to the multivariate negative binomial model, elderly (age ≥ 65), MERS‐CoV infection, ICU admission, and abnormal radiology findings were associated with longer recovery delay (adjusted relative risk (aRR): 1.741, 2.138, 2.048, and 1.473, respectively). Camel contact and the presence of respiratory symptoms at presentation were associated with a shorter recovery delay (expedited recovery) (aRR: 0.267 and 0.537, respectively). Diagnosis delay is a positive predictor for recovery delay (r = .421; P = .001).ConclusionsThe study evidence supports that longer recovery delay was seen in patients of older age, MERS‐CoV infection, ICU admission, and abnormal radiology findings. Shorter recovery delay was found in patients who had camel contact and respiratory symptoms at presentation. These findings may help us understand clinical decision making on directing hospital resources toward prompt screening, monitoring, and implementing clinical recovery and treatment strategies.

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Section: Discussionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factors associated with recovery delay in a sample of patients diagnosed by MERS‐CoV rRT‐PCR: A Saudi Arabian multicenter retrospective study

Ahmed

Al‐Jahdali

Alaqeel

et al. 2018

Influenza Resp Viruses

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“…The dataset consisted of 227 healthy newborns. Also in another study, Naive Bayes classifier and J48 decision tree algorithm were used for building predictive models for MERS-CoV infections Al-Turaiki et al (2016). The dataset used consists of 1082 records.…”

Section: Main Textmentioning

confidence: 99%

A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran

Mohammadzadeh

Hoseini

Derafshi

2017

Procedia Computer Science

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Nowadays Health care industry has a significant growth in using data mining techniques to discover hidden information for effective decision making. Huge amount of healthcare data is suitable to mine hidden patterns and knowledge. In this paper we traced behavior of patients during the period of 3 years in three clinics of a big public sector hospital and tried to detect special groups and their tendencies by RFML model as a customer life time value (CLV). The main goal was to detect 'potential for loyal' customers for strengthen relationships and 'potential to churn' customers for recovery of the efficiency of customer retention campaigns and reduce the costs associated with churn. This strategy helps hospital administrators to increase profit and reduce costs of customers' loss. At first, K-means clustering algorithm was applied for identification of target customers and groups and then, decision tree classifier as churn prediction was used. We compared performance of three clinics based on the number of loyal and churn customers. Our results showed that Pediatric Hematology clinic had a better performance than that of other clinics, because of more number of loyal customers.

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“…Recently, various types of data mining methods have been applied by a number of researchers [6,7], using real MERS-CoV datasets based on several types of machine learning classifiers. MERS is a complex disease caused by MERS-CoV that spreads easily and has a high death rate; approximately 40% of patients diagnosed with MERS have died [1].…”

Section: Introductionmentioning

confidence: 99%

Selecting Accurate Classifier Models for a MERS-CoV Dataset

Almoammar¹,

Alhenaki²,

Kurdi³

2018

Advances in Intelligent Systems and Computing

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The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a viral respiratory disease that is spreading worldwide necessitating to have an accurate diagnosis system that accurately predicts infections. As data mining classifiers can greatly assist in enhancing the prediction accuracy of diseases in general. In this paper, classifier model performance for two classification types:(1) binary and (2) multi-class were tested on a MERS-CoV dataset that consists of all reported cases in Saudi Arabia between 2013 and 2017. A cross-validation model was applied to measure the accuracy of the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (k-NN) classifiers. Experimental results demonstrate that SVM and Decision Tree classifiers achieved the highest accuracy of 86.44% for binary classification based on healthcare personnel class. On the other hand, for multiclass classification based on city class, the decision tree classifier had the highest accuracy among the remaining classifiers; although it did not reach a satisfactory accuracy level (42.80%). This work is intended to be a part of a MERS-CoV prediction system to enhance the diagnosis of MERS-CoV disease.

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Building predictive models for MERS-CoV infections using data mining techniques

Abstract: We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.

Cited by 69 publications

References 5 publications

Factors associated with recovery delay in a sample of patients diagnosed by MERS‐CoV rRT‐PCR: A Saudi Arabian multicenter retrospective study

Factors associated with recovery delay in a sample of patients diagnosed by MERS‐CoV rRT‐PCR: A Saudi Arabian multicenter retrospective study

A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran

Selecting Accurate Classifier Models for a MERS-CoV Dataset

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