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

[1]  U. Wiil,et al.  A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders , 2023, Sensors.

[2]  U. Wiil,et al.  Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods , 2022, BMC Medical Informatics and Decision Making.

[3]  Chinmay Chakraborty,et al.  Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19 , 2022, IEEE Journal of Biomedical and Health Informatics.

[4]  S. Puthusserypady,et al.  Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches , 2022, Int. J. Medical Informatics.

[5]  M. Mansournia,et al.  Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods , 2022, BMC Medical Informatics and Decision Making.

[6]  U. Wiil,et al.  Machine learning techniques for mortality prediction in emergency departments: a systematic review , 2021, BMJ Open.

[7]  Uffe Kock Wiil,et al.  Analysis of Comorbidities of Alcohol Use Disorder , 2021, 2021 IEEE Symposium on Computers and Communications (ISCC).

[8]  Qian M. Zhou,et al.  A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve , 2021, Diagnostic and Prognostic Research.

[9]  Chinmay Chakraborty,et al.  Early and accurate prediction of diabetics based on FCBF feature selection and SMOTE , 2021, International Journal of System Assurance Engineering and Management.

[10]  Chen Chen,et al.  Discrimination of alcohol dependence based on the convolutional neural network , 2020, PloS one.

[11]  Yi Wang,et al.  A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping , 2020, Int. J. Geogr. Inf. Sci..

[12]  A. Christensen,et al.  Can We Talk about Alcohol for a Minute? Thoughts and Opinions Expressed by Health Professionals and Patients at a Somatic Hospital , 2020, Alcoholism Treatment Quarterly.

[13]  Uffe Kock Wiil,et al.  A Predictive Machine Learning Model to Determine Alcohol Use Disorder , 2020, 2020 IEEE Symposium on Computers and Communications (ISCC).

[14]  S. Srinivasa Rao,et al.  Early Detection of Dementia Disease Using Data Mining Techniques , 2020, Studies in Big Data.

[15]  N. Volkow,et al.  Neuropsychosocial Markers of Binge Drinking in Young Adults , 2020, Molecular Psychiatry.

[16]  B. Littenberg,et al.  A Machine Learning Approach to Identification of Unhealthy Drinking , 2020, The Journal of the American Board of Family Medicine.

[17]  Jing-Hao Xue,et al.  Adjusting the imbalance ratio by the dimensionality of imbalanced data , 2020, Pattern Recognit. Lett..

[18]  J. Mishra,et al.  Impact of Childhood Trauma on Executive Function in Adolescence-Mediating Functional Brain Networks and Prediction of High-Risk Drinking. , 2020, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[19]  Chinmay Chakraborty,et al.  ROC Analysis for detection of Epileptical Seizures using Haralick features of Gamma band , 2020, 2020 National Conference on Communications (NCC).

[20]  Gonzalo Martínez-Muñoz,et al.  A comparative analysis of gradient boosting algorithms , 2019, Artificial Intelligence Review.

[21]  Daniel V. Pitti,et al.  Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study , 2019, Molecular Psychiatry.

[22]  J. H. Rudd,et al.  Alcohol use disorders and the heart , 2019, Addiction.

[23]  C. Holstege,et al.  Development and validation of a risk predictive model for student harmful drinking-A longitudinal data linkage study. , 2019, Drug and alcohol dependence.

[24]  J. Søgaard,et al.  Making a bridge between general hospital and specialised community-based treatment for alcohol use disorder-A pragmatic randomised controlled trial. , 2019, Drug and alcohol dependence.

[25]  Adrian B. R. Shatte,et al.  Machine learning in mental health: a scoping review of methods and applications , 2019, Psychological Medicine.

[26]  J. Rehm,et al.  Global Burden of Disease and the Impact of Mental and Addictive Disorders , 2019, Current Psychiatry Reports.

[27]  M. Sunderland,et al.  Machine‐learning prediction of adolescent alcohol use: a cross‐study, cross‐cultural validation , 2018, Addiction.

[28]  F. Terra,et al.  Alcohol consumption/dependence and resilience in older adults with high blood pressure 1 , 2018, Revista latino-americana de enfermagem.

[29]  Dilip Singh Sisodia,et al.  A Comparative Performance of Classification Algorithms in Predicting Alcohol Consumption Among Secondary School Students , 2018, Advances in Intelligent Systems and Computing.

[30]  Xinyi Liu,et al.  Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE , 2018, Genes.

[31]  A. Nielsen,et al.  Lifestyle factors in somatic patients with and without potential alcohol problems , 2018, Journal of Public Health.

[32]  A. Nielsen,et al.  Changes in profile of patients seeking alcohol treatment and treatment outcomes following policy changes , 2017, Journal of Public Health.

[33]  Lior Rokach,et al.  Fast-CBUS: A fast clustering-based undersampling method for addressing the class imbalance problem , 2017, Neurocomputing.

[34]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[35]  Yijing Li,et al.  Learning from class-imbalanced data: Review of methods and applications , 2017, Expert Syst. Appl..

[36]  F. D. Del Boca,et al.  Screening, brief intervention and referral to treatment (SBIRT): implementation barriers, facilitators and model migration , 2017, Addiction.

[37]  Richard Barnett Alcohol use disorders , 2017, The Lancet.

[38]  A. Nielsen,et al.  Factors influencing whether nurses talk to somatic patients about their alcohol consumption , 2016 .

[39]  Saeed Safari,et al.  Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve , 2016, Emergency.

[40]  Hiroshi de Silva,et al.  Missing data imputation using Evolutionary k- Nearest neighbor algorithm for gene expression data , 2016, 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer).

[41]  W. Meurer,et al.  Logistic Regression: Relating Patient Characteristics to Outcomes. , 2016, JAMA.

[42]  J. Søgaard,et al.  Relay model for recruiting alcohol dependent patients in general hospitals - a single-blind pragmatic randomized trial , 2016, BMC Health Services Research.

[43]  Marco Tulio Ribeiro,et al.  “Why Should I Trust You?”: Explaining the Predictions of Any Classifier , 2016, NAACL.

[44]  Wing W. Y. Ng,et al.  Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems , 2015, IEEE Transactions on Cybernetics.

[45]  Mu Zhu,et al.  A Relationship between the Average Precision and the Area Under the ROC Curve , 2015, ICTIR.

[46]  Gregory Traversy,et al.  Alcohol Consumption and Obesity: An Update , 2015, Current Obesity Reports.

[47]  G. Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[48]  J. Rehm,et al.  General Practitioners Recognizing Alcohol Dependence: A Large Cross-Sectional Study in 6 European Countries , 2015, The Annals of Family Medicine.

[49]  A. Hope,et al.  Harm Experienced from the Heavy Drinking of Family and Friends in the General Population: A Comparative Study of Six Northern European Countries , 2015, Substance abuse : research and treatment.

[50]  A. Hope,et al.  Experienced Harm from Other People’s Drinking: A Comparison of Northern European Countries , 2015, Substance abuse : research and treatment.

[51]  M. Gissler,et al.  Mortality and life expectancy of people with alcohol use disorder in Denmark, Finland and Sweden , 2014, Acta psychiatrica Scandinavica.

[52]  D. Kanny,et al.  Alcohol-Attributable Deaths and Years of Potential Life Lost — 11 States, 2006–2010 , 2014, MMWR. Morbidity and mortality weekly report.

[53]  Bieke Dejaegher,et al.  Feature selection methods in QSAR studies. , 2012, Journal of AOAC International.

[54]  Rajeev Kumar,et al.  Receiver operating characteristic (ROC) curve for medical researchers , 2011, Indian pediatrics.

[55]  U. Becker,et al.  The number of persons with alcohol problems in the Danish population , 2011, Scandinavian journal of public health.

[56]  Matt Stevenson,et al.  Alcohol Use Disorders Identification Test , 2010 .

[57]  Jürgen Rehm,et al.  Volume of alcohol consumption, patterns of drinking and burden of disease in the European region 2002. , 2006, Addiction.

[58]  A. Tjønneland,et al.  The relation between drinking pattern and body mass index and waist and hip circumference , 2005, International Journal of Obesity.

[59]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[60]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[61]  Tong Zhang An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods , 2001, AI Mag..

[62]  B. Nielsen,et al.  [Differences between male and female alcoholics and differences in their need of treatment]. , 1999, Ugeskrift for laeger.

[63]  Justus Eisfeld Book Reviews : International Statistical Classification of Diseases and Related Health Problems 10th Revision, Vol 2. Instruction Manual. by World Health Organisation, 1993. 160 pp, Sw fr 40. Hardback. ISBN: 92-4-154420-1 , 1994 .

[64]  McGinnis Jm,et al.  Actual causes of death in the United States. , 1993 .

[65]  O. Aasland,et al.  Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption--II. , 1993, Addiction.

[66]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[67]  D. Wolpert Original Contribution: Stacked generalization , 1992 .

[68]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[69]  U. Wiil,et al.  Predicting the Risk of Alcohol Use Disorder Using Machine Learning: A Systematic Literature Review , 2021, IEEE Access.

[70]  Ganjar Alfian,et al.  HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System , 2020, IEEE Access.

[71]  F. Beyer,et al.  Effectiveness of brief alcohol interventions in primary care populations. , 2018, The Cochrane database of systematic reviews.

[72]  Goran Arbanas Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , 2015 .

[73]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[74]  Chandan Chakraborty,et al.  Statistical analysis of mammographic features and its classification using support vector machine , 2010, Expert Syst. Appl..

[75]  Thomas Seidl,et al.  k-Nearest Neighbor Classification , 2009, Encyclopedia of Database Systems.

[76]  Copenhagen,et al.  European Health for All Database (HFA-DB) , 2005 .

[77]  J. R. Quinlan Induction of decision trees , 2004, Machine Learning.

[78]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[79]  Maristela Monteiro,et al.  AUDIT - The alcohol use disorders identification test: guidelines for use in primary care. , 2001 .

[80]  L. Breiman Random Forests , 2001, Machine Learning.