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Ramos-Triguero A, Navarro-Tapia E, Vieiros M, Mirahi A, Astals Vizcaino M, Almela L, Martínez L, García-Algar Ó, Andreu-Fernández V. Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders. Front Neurosci 2024; 18:1400933. [PMID: 38808031 PMCID: PMC11131948 DOI: 10.3389/fnins.2024.1400933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/15/2024] [Indexed: 05/30/2024] Open
Abstract
Introduction Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders. Methods This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions. Results This study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems. Discussion Our findings emphasize the importance of ML analyses for early diagnoses of FASD, allowing a better understanding of FASD subtypes to potentially improve clinical practice and avoid misdiagnosis.
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Affiliation(s)
- Anna Ramos-Triguero
- Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department de Cirurgia i Especialitats Mèdico-Quirúrgiques, Universitat de Barcelona, Barcelona, Spain
| | - Elisabet Navarro-Tapia
- Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Hospital Universitario La Paz, Madrid, Spain
- Faculty of Health Sciences, Valencian International University (VIU), Valencia, Spain
| | - Melina Vieiros
- Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Hospital Universitario La Paz, Madrid, Spain
| | - Afrooz Mirahi
- Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Neonatology, Instituto Clínic de Ginecología, Obstetricia y Neonatología (ICGON), Hospital Clínic-Maternitat, BCNatal, Barcelona, Spain
| | - Marta Astals Vizcaino
- Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department de Cirurgia i Especialitats Mèdico-Quirúrgiques, Universitat de Barcelona, Barcelona, Spain
| | - Lucas Almela
- Department de Cirurgia i Especialitats Mèdico-Quirúrgiques, Universitat de Barcelona, Barcelona, Spain
| | - Leopoldo Martínez
- Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Hospital Universitario La Paz, Madrid, Spain
- Department of Pediatric Surgery, Hospital Universitario La Paz, Madrid, Spain
| | - Óscar García-Algar
- Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Neonatology, Instituto Clínic de Ginecología, Obstetricia y Neonatología (ICGON), Hospital Clínic-Maternitat, BCNatal, Barcelona, Spain
| | - Vicente Andreu-Fernández
- Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Biosanitary Research Institute, Valencian International University (VIU), Valencia, Spain
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Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
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Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
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Ribeiro P, Sá J, Paiva D, Rodrigues PM. Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis. Bioengineering (Basel) 2024; 11:58. [PMID: 38247935 PMCID: PMC10813154 DOI: 10.3390/bioengineering11010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/13/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. METHODS the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. RESULTS the Accuracy discrimination results ranged between 73% and 100%, the Recall between 68% and 100%, and the AUC between 0.42 and 1. CONCLUSIONS the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.
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Affiliation(s)
| | | | | | - Pedro Miguel Rodrigues
- CBQF—Centro de Biotecnologia e Química Fina, Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal; (P.R.); (J.S.); (D.P.)
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