Development and validation of a risk predictive model for student harmful drinking—A longitudinal data linkage study

Ngo, Duc Anh; Rege, Saumitra V.; Ait-Daoud, Nassima; Holstege, Christopher P.

doi:10.1016/j.drugalcdep.2019.01.016

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…Some clinical records with ICD-10 labels, such as DZ03 "Medical observation and evaluation for suspected diseases and conditions" have a high impact on our final predictive model but may have no informative value for medical staff. In the literature, risk factors such as gender and age have been discovered in many studies [ 14 , 55 ]. However, no studies looked for clinical and risk factors for AUD from EHRs.…”

Section: Resultsmentioning

confidence: 99%

Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods

Ebrahimi

Wiil

Naemi

et al. 2022

BMC Med Inform Decis Mak

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Background High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to be effective in feature reduction as well as in identifying risk factors related to prediction of clinical disorders. This paper examines the prediction of patients with alcohol use disorder (AUD) using machine learning (ML) and attempts to identify risk factors related to the diagnosis of AUD. Methods A FS framework consisting of two operational levels, base selectors and ensemble selectors. The first level consists of five FS methods: three filter methods, one wrapper method, and one embedded method. Base selector outputs are aggregated to develop four ensemble FS methods. The outputs of FS method were then fed into three ML algorithms: support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to compare and identify the best feature subset for the prediction of AUD from EHRs. Results In terms of feature reduction, the embedded FS method could significantly reduce the number of features from 361 to 131. In terms of classification performance, RF based on 272 features selected by our proposed ensemble method (Union FS) with the highest accuracy in predicting patients with AUD, 96%, outperformed all other models in terms of AUROC, AUPRC, Precision, Recall, and F1-Score. Considering the limitations of embedded and wrapper methods, the best overall performance was achieved by our proposed Union Filter FS, which reduced the number of features to 223 and improved Precision, Recall, and F1-Score in RF from 0.77, 0.65, and 0.71 to 0.87, 0.81, and 0.84, respectively. Our findings indicate that, besides gender, age, and length of stay at the hospital, diagnosis related to digestive organs, bones, muscles and connective tissue, and the nervous systems are important clinical factors related to the prediction of patients with AUD. Conclusion Our proposed FS method could improve the classification performance significantly. It could identify clinical factors related to prediction of AUD from EHRs, thereby effectively helping clinical staff to identify and treat AUD patients and improving medical knowledge of the AUD condition. Moreover, the diversity of features among female and male patients as well as gender disparity were investigated using FS methods and ML techniques.

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Section: Resultsmentioning

confidence: 99%

Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods

Ebrahimi

Wiil

Naemi

et al. 2022

BMC Med Inform Decis Mak

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“…Previous studies have shown that ML algorithms such as artificial neural networks [ 24 – 26 ], logistic regression (LR) [ 25 , 27 – 29 ], support vector machines (SVMs) [ 24 – 26 , 29 – 32 ], random forests (RFs) [ 24 , 25 , 28 , 29 , 31 ], elastic nets [ 24 , 31 ], k-nearest neighbour (KNN) [ 25 ], decision trees (DTs) [ 25 , 28 ], and naive Bayes [ 28 ]…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have shown that ML algorithms such as artificial neural networks [ 24 – 26 ], logistic regression (LR) [ 25 , 27 – 29 ], support vector machines (SVM) [ 24 – 26 , 29 – 32 ], random forests (RF) [ 24 , 25 , 28 , 29 , 31 ], elastic nets [ 24 , 31 ], k-nearest neighbour (KNN) [ 25 ], decision trees (DT) [ 25 , 28 ], naive bayes [ 28 ], etc., have been successfully utilized to develop predictive models for the early detection of patients with AUD based on EHRs. However, in the ML field, missing values, feature redundancy, noisy datasets, and imbalanced data may arise and impact the performance of such prediction models [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

AUD-DSS: a decision support system for early detection of patients with alcohol use disorder

Ebrahimi,

Wiil,

Baskaran

et al. 2023

BMC Bioinformatics

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Background Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients’ AUD status earlier. Methods This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD. Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM. Results The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype. Conclusion It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.

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“…Several studies have been conducted related to detection and prediction of patients with AUD using traditional machine learning (ML) and deep learning methods based on Electronic Health Records (EHRs). Ngo et al [4] developed a risk predictive model using Linear Regression (LR) based on an electronic database consisting of student's enrollment and their medical records. In their study, they considered a filter feature selection method as the feature engineering task to reduce the number of risk factors.…”

Section: Introductionmentioning

confidence: 99%

“…Although previous studies in prediction of patients with AUD achieved their goals successfully, several factors distinguish their work from the current study. One of the main challenges of employing traditional ML methods such as SVM and LR is the feature reduction task, which needs to be conducted by human experts [4,6]. Moreover, all the mentioned studies on AUD use binary classification approaches.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network to Identify Patients with Alcohol Use Disorder

Ebrahimi

Wiil

Mansourvar

et al. 2021

Studies in Health Technology and Informatics

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This paper presents an application of deep neural networks (DNN) to identify patients with Alcohol Use Disorder based on historical electronic health records. Our methodology consists of four stages including data collection, preprocessing, predictive model development, and validation. Data are collected from two sources and labeled into three classes including Normal, Hazardous, and Harmful drinkers. Moreover, problems such as imbalanced classes, noise, and categorical variables were handled. A four-layer fully-connected feedforward DNN architecture was designed and developed to predict Normal, Hazardous, and Harmful drinkers. Results show that our proposed method could successfully classify about 96%, 82%, and 89% of Normal, Hazardous, and Harmful drinkers, respectively, which is better than classical machine learning approaches.

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Development and validation of a risk predictive model for student harmful drinking—A longitudinal data linkage study

Cited by 7 publications

References 27 publications

Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods

Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods

AUD-DSS: a decision support system for early detection of patients with alcohol use disorder

Deep Neural Network to Identify Patients with Alcohol Use Disorder

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