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Trescato I, Roversi C, Vettoretti M, Di Camillo B, Facchinetti A. A model to forecast the two-year variation of subjective wellbeing in the elderly population. BMC Med Inform Decis Mak 2023; 23:253. [PMID: 37940954 PMCID: PMC10634107 DOI: 10.1186/s12911-023-02360-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 10/31/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND The ageing global population presents significant public health challenges, especially in relation to the subjective wellbeing of the elderly. In this study, our aim was to investigate the potential for developing a model to forecast the two-year variation of the perceived wellbeing of individuals aged over 50. We also aimed to identify the variables that predict changes in subjective wellbeing, as measured by the CASP-12 scale, over a two-year period. METHODS Data from the European SHARE project were used, specifically the demographic, health, social and financial variables of 9422 subjects. The subjective wellbeing was measured through the CASP-12 scale. The study outcome was defined as binary, i.e., worsening/not worsening of the variation of CASP-12 in 2 years. Logistic regression, logistic regression with LASSO regularisation, and random forest were considered candidate models. Performance was assessed in terms of accuracy in correctly predicting the outcome, Area Under the Curve (AUC), and F1 score. RESULTS The best-performing model was the random forest, achieving an accuracy of 65%, AUC = 0.659, and F1 = 0.710. All models proved to be able to generalise both across subjects and over time. The most predictive variables were the CASP-12 score at baseline, the presence of depression and financial difficulties. CONCLUSIONS While we identify the random forest model as the more suitable, given the similarity of performance, the models based on logistic regression or on logistic regression with LASSO regularisation are also possible options.
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Affiliation(s)
- Isotta Trescato
- Department of Information Engineering, University of Padova, Padova (PD), Italy
| | - Chiara Roversi
- Department of Information Engineering, University of Padova, Padova (PD), Italy
| | - Martina Vettoretti
- Department of Information Engineering, University of Padova, Padova (PD), Italy
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Padova (PD), Italy
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro (PD), Italy
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova (PD), Italy.
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Tavazzi E, Longato E, Vettoretti M, Aidos H, Trescato I, Roversi C, Martins AS, Castanho EN, Branco R, Soares DF, Guazzo A, Birolo G, Pala D, Bosoni P, Chiò A, Manera U, de Carvalho M, Miranda B, Gromicho M, Alves I, Bellazzi R, Dagliati A, Fariselli P, Madeira SC, Di Camillo B. Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review. Artif Intell Med 2023; 142:102588. [PMID: 37316101 DOI: 10.1016/j.artmed.2023.102588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/14/2023] [Accepted: 05/16/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.
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Affiliation(s)
- Erica Tavazzi
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Enrico Longato
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Martina Vettoretti
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Helena Aidos
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Isotta Trescato
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Chiara Roversi
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Andreia S Martins
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Eduardo N Castanho
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Ruben Branco
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Diogo F Soares
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Alessandro Guazzo
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Giovanni Birolo
- Department of Medical Sciences, University of Torino, Corso Dogliotti 14, Turin, 10126, Italy
| | - Daniele Pala
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Pietro Bosoni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Adriano Chiò
- Department of Neurosciences "Rita Levi Montalcini", University of Turin, Via Cherasco 15, Turin, 10126, Italy
| | - Umberto Manera
- Department of Neurosciences "Rita Levi Montalcini", University of Turin, Via Cherasco 15, Turin, 10126, Italy
| | - Mamede de Carvalho
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Bruno Miranda
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Marta Gromicho
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Inês Alves
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Corso Dogliotti 14, Turin, 10126, Italy
| | - Sara C Madeira
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy; Department of Comparative Biomedicine and Food Science, University of Padova, Agripolis, Viale dell'Università, 16, Legnaro (PD), 35020, Italy.
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