A multi-label classification system for anomaly classification in electrocardiogram

Li, Chenyang; Sun, Le; Peng, Dandan; Subramani, Sudha; Nicolas, Shangwe Charmant

doi:10.1007/s13755-022-00192-w

Cited by 7 publications

(6 citation statements)

References 41 publications

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“…Therefore, it can identify PIMs more accurately when prescriptions have unknown independent variables. In addition, traditional classification algorithms consider learning problems that contain only one label, i.e., each example is associated with one single nominal target variable characterizing its property [ 19 ]. Due to the presence of multiple target variables in prescriptions, problem transformation methods should be used to transform the multilabel classification problem into several single-label classification problems.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the information on prescriptions involved multilabel classification problems. Therefore, problem transformation methods should be used to map the multilabel learning task into one or more single-label learning tasks, which resulted in improved model performance [ 19 ]. In this study, we aim to apply problem transformation models to identify correlations in prescription information and attempt to use several machine learning algorithms to find an optimal model for the warning of PIMs in geriatric outpatients.…”

Section: Introductionmentioning

confidence: 99%

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Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

Tian

Jin

et al. 2023

JCM

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Due to multiple comorbid illnesses, polypharmacy, and age-related changes in pharmacokinetics and pharmacodynamics in older adults, the prevalence of potentially inappropriate medications (PIMs) is high, which affects the quality of life of older adults. Building an effective warning model is necessary for the early identification of PIMs to prevent harm caused by medication in geriatric patients. The purpose of this study was to develop a machine learning-based model for the warning of PIMs in older Chinese outpatients. This retrospective study was conducted among geriatric outpatients in nine tertiary hospitals in Chengdu from January 2018 to December 2018. The Beers criteria 2019 were used to assess PIMs in geriatric outpatients. Three problem transformation methods were used to tackle the multilabel classification problem in prescriptions. After the division of patient prescriptions into the training and test sets (8:2), we adopted six widely used classification algorithms to conduct the classification task and assessed the discriminative performance by the accuracy, precision, recall, F1 scores, subset accuracy (ss Acc), and Hamming loss (hm) of each model. The results showed that among 11,741 older patient prescriptions, 5816 PIMs were identified in 4038 (34.39%) patient prescriptions. A total of 41 types of PIMs were identified in these prescriptions. The three-problem transformation methods included label power set (LP), classifier chains (CC), and binary relevance (BR). Six classification algorithms were used to establish the warning models, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), CatBoost, Deep Forest (DF), and TabNet. The CC + CatBoost model had the highest accuracy value (97.83%), recall value (89.34%), F1 value (90.69%), and ss Acc value (97.79%) with a good precision value (92.18%) and the lowest hm value (0.0006). Therefore, the CC + CatBoost model was selected to predict the occurrence of PIM in geriatric Chinese patients. This study’s novelty establishes a warning model for PIMs in geriatric patients by using machine learning. With the popularity of electronic patient record systems, sophisticated computer algorithms can be implemented at the bedside to improve medication use safety in geriatric patients in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

Tian

Jin

et al. 2023

JCM

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show abstract

“…As discussed in the introduction section, SLC was chosen because of the following; It is easier to implement than MLC because SLC avoids the ambiguity that can arise in multi-label scenarios, where an instance can belong to multiple classes simultaneously [ 51 ]. It is fast and hence, reduced computational complexity, hence fewer computational resources and memory [ 52 ] . It has fewer odds than MLC in producing false positives [ 53 ] .…”

Section: Methodsmentioning

confidence: 99%

“…It is fast and hence, reduced computational complexity, hence fewer computational resources and memory [ 52 ] .…”

Section: Methodsmentioning

confidence: 99%

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Okagbue,

Ijezie,

Ugwoke

et al. 2023

Heliyon

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“…From the multifault location diagnosis problem, the multilabel classification problem can be transformed into multiple single-label classification problems through a conversion strategy. For example, the ventilation system RVMFL diagnosis problem can be divided into multiple single-fault location diagnosis problems, but this undoubtedly increases the computational complexity 16 , 17 . The multilabel classification problem can also be solved by applying multilabel classification support and adaptation algorithms, such as DT, MLP, ranking support vector machine (Rank-SVM), and AdaBoost.MH, ML–KNN 18 – 22 .…”

Section: Introductionmentioning

confidence: 99%

A supervised diagnostic experiment of resistance variable multifault locations in a mine ventilation system

Wang

Liu

Deng

et al. 2023

Sci Rep

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The diagnosis of resistance variable multifault location (RVMFL) in a mine ventilation system is an essential function of the mine intelligent ventilation system, which is of great significance to mine-safe production. In this paper, a supervised machine learning model based on a decision tree (DT), multilayer perceptron (MLP), and ranking support vector machine (Rank-SVM) is proposed for RVMFL diagnosis in a mine ventilation system. The feasibility of the method and the predictive performance and generalization ability of the model were verified using a tenfold cross-validation of a multifault sample set of a 10-branch T-shaped angle-joint ventilation network and a 54-branch experimental ventilation network. The reliability of the model was further verified by diagnosing the RVMFL of the experimental ventilation system. The results show that the three models, DT, MLP, and Rank-SVM, can be used for the diagnosis of RVMFL in mine ventilation systems, and the prediction performance and generalization ability of the MLP and DT models perform better than the Rank-SVM model. In the diagnosis of multifault locations of the experimental ventilation system, the diagnostic accuracy of the MLP model reached 100% and that of the DT model was 44.44%. The results confirm the MLP model outperforms the three models and can meet engineering needs.

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A multi-label classification system for anomaly classification in electrocardiogram

Cited by 7 publications

References 41 publications

Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

A supervised diagnostic experiment of resistance variable multifault locations in a mine ventilation system

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