Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

Lu, Hsueh-Yi; Huang, Chen-Yuan; Su, Chwen-Tzeng; Lin, Chen‐Chiang

doi:10.1371/journal.pone.0094917

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…The sensitivity and specificity of the Naïve Bayesian model were 100 and 97.5 % (as showed in Table 6 ) which has given a positive LR (LR+) of 40 and a negative LR (LR−) of 0. The sensitivity and specificity of the Logistic Regression model were 96.7 and 91.67 % (as showed in Table 6 ) which has given a positive LR (LR+) of 11 and a negative LR (LR−) of 0.04 (Table 7 ) (Deeks and Altman 2004 ; Lang and Secic 1997 ; Lu et al 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Intelligent framework for diagnosis of frozen shoulder using cross sectional survey and case studies

et al. 2016

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ObjectivesFrozen shoulder is a disease in which shoulder becomes stiff. Accurate diagnosis of frozen shoulder is helpful in providing economical and effective treatment for patients. This research provides the classification of unstructured data using data mining techniques. Prediction results are validated by K-fold cross-validation method. It also provides accurate diagnosis of frozen shoulder using Naïve Bayesian and Random Forest models. At the end results are presented by performance measure techniques.MethodsIn this research, 145 respondents (patients) with a severe finding of frozen shoulder are included. They are selected on premise of (clinical) assessment confirmed after by MRI. This data is taken from the department of Orthopedics (Pakistan Institute of Medical Sciences Islamabad and Railway Hospital Rawalpindi) between September 2014 to November 2015. Frozen shoulder is categorized on the basis of MRI result. The predictor variables are taken from patient survey and patient reports, which consisted of 35+ variables. The outcome variable is coded into numeric system of “intact” and “no-intact”. The outcome variable is assigned into numeric code, 1 for “intact” and 0 for “no-intact”. “Intact” group is used as an indication that tissue is damaged badly and “no-intact” is classified as normal. Distribution of result is 110 patients for “Intact” group and 35 patients for “No-Intact” group (false positive rate was 24 %). In this research we have utilized two methods i.e. Naive Bayes and Random Forest. A statistics regression model (Logistic regression) to categorize frozen shoulder finding into “intact” and “no-intact” classes. In the end, we validated our results by Bayesian theorem. This gives a rough estimate about the probability of frozen shoulder.ResultsIn this research, our anticipated and predictive procedures gave better outcome as compared to statistical techniques. The specificity and sensitivity ratio of predicting a frozen shoulder are better in the Naïve Bayes as compared to Random Forest. In end the likelihood ratio results are used with Bayesian theorem for final evaluation of the results, from this we conclude predictive model is valid model for classification of frozen shoulder.ConclusionsWe have used three predictive models in our study to classify frozen shoulder. Then we validated our predictive results by Bayesian theorem to give a rough estimate about the probability of occurrence of disease or not. This enhances the clinical decision making regarding frozen shoulder.

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Section: Discussionmentioning

confidence: 99%

Intelligent framework for diagnosis of frozen shoulder using cross sectional survey and case studies

et al. 2016

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“…Predicting rotator cuff tear by data mining of clinical assessments has been reported 16 ; however, data on informatics‐based algorithms for rotator cuff tear and normal rotator cuff are lacking. Informatics‐based algorithms extend beyond the simple use of ICD‐9 or ICD‐10 billing codes to ascertain cases and controls.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Electronic Medical Record Algorithm to Identify Phenotypes of Rotator Cuff Tear

Gao

Fan

Ayers

et al. 2020

PM&R

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Background A lack of studies with large sample sizes of patients with rotator cuff tears is a barrier to performing clinical and genomic research. Objective To develop and validate an electronic medical record (EMR)–based algorithm to identify individuals with and without rotator cuff tear. Design We used a deidentified version of the EMR of more than 2 million subjects. A screening algorithm was applied to classify subjects into likely rotator cuff tear and likely normal rotator cuff groups. From these subjects, 500 likely rotator cuff tear and 500 likely normal rotator cuff were randomly chosen for algorithm development. Chart review of all 1000 subjects confirmed the true phenotype of rotator cuff tear or normal rotator cuff based on magnetic resonance imaging and operative report. An algorithm was then developed based on logistic regression and validation of the algorithm was performed. Results The variables significantly predicting rotator cuff tear included the number of times a Current Procedural Terminology code related to rotator cuff procedures was used (odds ratio [OR] = 3.3; 95% confidence interval [CI]: 1.6‐6.8 for ≥3 vs 0), the number of times a term related to rotator cuff lesions occurred in radiology reports (OR = 2.2; 95% CI: 1.2‐4.1 for ≥1 vs 0), and the number of times a term related to rotator cuff lesions occurred in physician notes (OR = 4.5; 95% CI: 2.2‐9.1 for 1 or 2 times vs 0). This phenotyping algorithm had a specificity of 0.89 (95% CI: 0.79‐0.95) for rotator cuff tear, area under the curve (AUC) of 0.842, and diagnostic likelihood ratios (DLRs), DLR+ and DLR− of 5.94 (95% CI: 3.07‐11.48) and 0.363 (95% CI: 0.291‐0.453). Conclusion Our informatics algorithm enables identification of cohorts of individuals with and without rotator cuff tear from an EMR‐based data set with moderate accuracy.

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“…The authors proposed the final model that the Bayesian classifier with the fuzzy discretisation of numerical attributes was accurate, and for this reason, as a promising machine learning tool that can help physicians in their decision-making process. Lu et al 22 used Bayesian theory, likelihood ratios, and predictive data mining to improve the precision in the diagnosis of rotator cuff tears. The authors concluded that this model could be a useful tool in classification and the diagnosis of rotator cuff tears.…”

Section: The Use Of Other Modelsmentioning

confidence: 99%

Artificial intelligence and data processing in injury diagnosis and prevention in competitive sports: A literature review

Poulios¹,

Serlis²,

Groumpos³

et al. 2021

MOJOR

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Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

Cited by 10 publications

References 47 publications

Intelligent framework for diagnosis of frozen shoulder using cross sectional survey and case studies

Intelligent framework for diagnosis of frozen shoulder using cross sectional survey and case studies

Development and Validation of an Electronic Medical Record Algorithm to Identify Phenotypes of Rotator Cuff Tear

Artificial intelligence and data processing in injury diagnosis and prevention in competitive sports: A literature review

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