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Ben Yehuda O, Itelman E, Vaisman A, Segal G, Lerner B. Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study. J Med Internet Res 2024; 26:e48595. [PMID: 39079116 PMCID: PMC11322683 DOI: 10.2196/48595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 12/02/2023] [Accepted: 04/30/2024] [Indexed: 08/18/2024] Open
Abstract
BACKGROUND Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' lives-is a major challenge confronting modern medicine. OBJECTIVE We aimed to establish accurate and informative machine learning (ML) models to identify patients at high risk for PE as they are admitted to the hospital, before their initial clinical checkup, by using only the information in their medical records. METHODS We collected demographics, comorbidities, and medications data for 2568 patients with PE and 52,598 control patients. We focused on data available prior to emergency department admission, as these are the most universally accessible data. We trained an ML random forest algorithm to detect PE at the earliest possible time during a patient's hospitalization-at the time of his or her admission. We developed and applied 2 ML-based methods specifically to address the data imbalance between PE and non-PE patients, which causes misdiagnosis of PE. RESULTS The resulting models predicted PE based on age, sex, BMI, past clinical PE events, chronic lung disease, past thrombotic events, and usage of anticoagulants, obtaining an 80% geometric mean value for the PE and non-PE classification accuracies. Although on hospital admission only 4% (1942/46,639) of the patients had a diagnosis of PE, we identified 2 clustering schemes comprising subgroups with more than 61% (705/1120 in clustering scheme 1; 427/701 and 340/549 in clustering scheme 2) positive patients for PE. One subgroup in the first clustering scheme included 36% (705/1942) of all patients with PE who were characterized by a definite past PE diagnosis, a 6-fold higher prevalence of deep vein thrombosis, and a 3-fold higher prevalence of pneumonia, compared with patients of the other subgroups in this scheme. In the second clustering scheme, 2 subgroups (1 of only men and 1 of only women) included patients who all had a past PE diagnosis and a relatively high prevalence of pneumonia, and a third subgroup included only those patients with a past diagnosis of pneumonia. CONCLUSIONS This study established an ML tool for early diagnosis of PE almost immediately upon hospital admission. Despite the highly imbalanced scenario undermining accurate PE prediction and using information available only from the patient's medical history, our models were both accurate and informative, enabling the identification of patients already at high risk for PE upon hospital admission, even before the initial clinical checkup was performed. The fact that we did not restrict our patients to those at high risk for PE according to previously published scales (eg, Wells or revised Genova scores) enabled us to accurately assess the application of ML on raw medical data and identify new, previously unidentified risk factors for PE, such as previous pulmonary disease, in general populations.
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Affiliation(s)
- Ori Ben Yehuda
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Edward Itelman
- Education Authority, Chaim Sheba Medical Center, Faculty of Health Science and Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Cardiology Division, Rabin Medical Center, Petach-Tikva, Israel
| | - Adva Vaisman
- Education Authority, Chaim Sheba Medical Center, Faculty of Health Science and Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Gad Segal
- Education Authority, Chaim Sheba Medical Center, Faculty of Health Science and Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Boaz Lerner
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Din Abdul Jabbar MA, Guo L, Nag S, Guo Y, Simmons Z, Pioro EP, Ramasamy S, Yeo CJJ. Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning. Amyotroph Lateral Scler Frontotemporal Degener 2024; 25:242-255. [PMID: 38052485 DOI: 10.1080/21678421.2023.2285443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/14/2023] [Indexed: 12/07/2023]
Abstract
OBJECTIVE To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML). METHODS We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM). XGBoost identified predictors of progression while BLSTM provided a confidence level for each prediction. RESULTS ML models achieved area under receiver-operating-characteristics curve (AUROC) of 0.570-0.748 and were non-inferior to clinician assessments. Performance was similar with observation lengths of a single visit, 3, 6, or 12 months and on a holdout validation dataset, but was better for longer prediction lengths. 21 important predictors were identified, with the top 3 being days since disease onset, past ALSFRS-R and forced vital capacity. Nonstandard predictors included phosphorus, chloride and albumin. BLSTM demonstrated higher performance for the samples about which it was most confident. Patient screening by models may reduce hypothetical Phase II/III clinical trial sizes by 18.3%. CONCLUSION Similar accuracies across ML models using different observation lengths suggest that a clinical trial observation period could be shortened to a single visit and clinical trial sizes reduced. Confidence levels provided by BLSTM gave additional information on the trustworthiness of predictions, which could aid decision-making. The identified predictors of ALS progression are potential biomarkers and therapeutic targets for further research.
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Affiliation(s)
- Muzammil Arif Din Abdul Jabbar
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
- Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Ling Guo
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Sonakshi Nag
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Yang Guo
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Zachary Simmons
- Department of Neurology, Pennsylvania State University College of Medicine, State College, PA, USA
| | - Erik P Pioro
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Savitha Ramasamy
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Crystal Jing Jing Yeo
- Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Lee Kong Chien School of Medicine, Imperial College London and Nanyang Technological University Singapore, Singapore, Singapore
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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Papaiz F, Dourado MET, de Medeiros Valentim RA, Pinto R, de Morais AHF, Arrais JP. Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis. BMC Med Inform Decis Mak 2024; 24:80. [PMID: 38504285 PMCID: PMC10949816 DOI: 10.1186/s12911-024-02484-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials.
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Affiliation(s)
- Fabiano Papaiz
- Federal University of Rio Grande Do Norte, Natal, Brazil.
- University of Coimbra, Coimbra, Portugal.
- Federal Institute of Rio Grande Do Norte, Natal, Brazil.
| | | | | | - Rafael Pinto
- Federal University of Rio Grande Do Norte, Natal, Brazil
- Federal Institute of Rio Grande Do Norte, Natal, Brazil
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Fernandes F, Barbalho I, Bispo Júnior A, Alves L, Nagem D, Lins H, Arrais Júnior E, Coutinho KD, Morais AHF, Santos JPQ, Machado GM, Henriques J, Teixeira C, Dourado Júnior MET, Lindquist ARR, Valentim RAM. Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have. J Clin Med 2023; 12:5235. [PMID: 37629277 PMCID: PMC10455505 DOI: 10.3390/jcm12165235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/05/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human-Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human-Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human-Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.
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Affiliation(s)
- Felipe Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ingridy Barbalho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Arnaldo Bispo Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Luca Alves
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Danilo Nagem
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Hertz Lins
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ernano Arrais Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Karilany D. Coutinho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Antônio H. F. Morais
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, Brazil; (A.H.F.M.); (J.P.Q.S.)
| | - João Paulo Q. Santos
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, Brazil; (A.H.F.M.); (J.P.Q.S.)
| | | | - Jorge Henriques
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, 3030-788 Coimbra, Portugal; (J.H.); (C.T.)
| | - César Teixeira
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, 3030-788 Coimbra, Portugal; (J.H.); (C.T.)
| | - Mário E. T. Dourado Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
- Department of Integrated Medicine, Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil
| | - Ana R. R. Lindquist
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ricardo A. M. Valentim
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
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5
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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] [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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Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023; 11:887. [PMID: 36979866 PMCID: PMC10045927 DOI: 10.3390/biomedicines11030887] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023] Open
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
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Affiliation(s)
- Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Bianca Roncaglia
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
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Levodopa responsiveness in Parkinson's disease: harnessing real-life experience with machine-learning analysis. J Neural Transm (Vienna) 2022; 129:1289-1297. [PMID: 36030311 DOI: 10.1007/s00702-022-02540-2] [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: 05/16/2022] [Accepted: 08/13/2022] [Indexed: 10/15/2022]
Abstract
Responsiveness to levodopa varies greatly among patients with Parkinson's disease (PD). The factors that affect it are ill defined. The aim of the study was to identify factors predictive of long-term response to levodopa. The medical records of 296 patients with PD (mean age of onset, 62.2 ± 9.7 years) were screened for demographics, previous treatments, and clinical phenotypes. All patients were assessed with the Unified PD Rating Scale (UPDRS)-III before and 3 months after levodopa initiation. Regression and machine-learning analyses were used to determine factors that are associated with levodopa responsiveness and might identify patients who will benefit from treatment. The UPDRS-III score improved by ≥ 30% (good response) in 128 patients (43%). On regression analysis, female gender, young age at onset, and early use of dopamine agonists predicted a good response. Time to initiation of levodopa treatment had no effect on responsiveness except in patients older than 72 years, who were less responsive. Machine-learning analysis validated these factors and added several others: symptoms of rigidity and bradykinesia, disease onset in the legs and on the left side, and fewer white vascular ischemic changes, comorbidities, and pre-non-motor symptoms. The main determinants of variations in levodopa responsiveness are gender, age, and clinical phenotype. Early use of dopamine agonists does not hamper levodopa responsiveness. In addition to validating the regression analysis results, machine-learning methods helped to determine the specific clinical phenotype of patients who may benefit from levodopa in terms of comorbidities and pre-motor and non-motor symptoms.
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Ramakrishnan J, Mavaluru D, Sakthivel RS, Alqahtani AS, Mubarakali A, Retnadhas M. Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network. Neural Comput Appl 2022. [DOI: 10.1007/s00521-020-05026-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Papaiz F, Dourado MET, Valentim RADM, de Morais AHF, Arrais JP. Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: A Review. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.869140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities.
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Lipid level alteration in human and cellular models of alpha synuclein mutations. NPJ Parkinsons Dis 2022; 8:52. [PMID: 35468903 PMCID: PMC9039073 DOI: 10.1038/s41531-022-00313-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 03/31/2022] [Indexed: 12/15/2022] Open
Abstract
Lipid profiles in biological fluids from patients with Parkinson's disease (PD) are increasingly investigated in search of biomarkers. However, the lipid profiles in genetic PD remain to be determined, a gap of knowledge of particular interest in PD associated with mutant α-synuclein (SNCA), given the known relationship between this protein and lipids. The objective of this research is to identify serum lipid composition from SNCA A53T mutation carriers and to compare these alterations to those found in cells and transgenic mice carrying the same genetic mutation. We conducted an unbiased lipidomic analysis of 530 lipid species from 34 lipid classes in serum of 30 participants with SNCA mutation with and without PD and 30 healthy controls. The primary analysis was done between 22 PD patients with SNCA+ (SNCA+/PD+) and 30 controls using machine-learning algorithms and traditional statistics. We also analyzed the lipid composition of human clonal-cell lines and tissue from transgenic mice overexpressing the same SNCA mutation. We identified specific lipid classes that best discriminate between SNCA+/PD+ patients and healthy controls and found certain lipid species, mainly from the glycerophosphatidylcholine and triradylglycerol classes, that are most contributory to this discrimination. Most of these alterations were also present in human derived cells and transgenic mice carrying the same mutation. Our combination of lipidomic and machine learning analyses revealed alterations in glycerophosphatidylcholine and triradylglycerol in sera from PD patients as well as cells and tissues expressing mutant α-Syn. Further investigations are needed to establish the pathogenic significance of these α-Syn-associated lipid changes.
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Gromicho M, Leão T, Oliveira Santos M, Pinto S, Carvalho AM, Madeira SC, de Carvalho M. Dynamic Bayesian Networks for stratification of disease progression in Amyotrophic Lateral Sclerosis. Eur J Neurol 2022; 29:2201-2210. [DOI: 10.1111/ene.15357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/31/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Marta Gromicho
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
| | - Tiago Leão
- Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal
| | - Miguel Oliveira Santos
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
- Department of Neurosciences and Mental Health Centro Hospitalar Universitário de Lisboa‐Norte Lisbon Portugal
| | - Susana Pinto
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
| | - Alexandra M. Carvalho
- Instituto de Telecomunicações and Lisbon ELLIS Unit (LUMLIS) Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal
| | - Sara C. Madeira
- LASIGE Faculdade de Ciências Universidade de Lisboa Lisbon Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
- Department of Neurosciences and Mental Health Centro Hospitalar Universitário de Lisboa‐Norte Lisbon Portugal
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Bede P, Murad A, Lope J, Li Hi Shing S, Finegan E, Chipika RH, Hardiman O, Chang KM. Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach. J Neurol Sci 2022; 432:120079. [PMID: 34875472 DOI: 10.1016/j.jns.2021.120079] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022]
Abstract
Motor neuron disease is an umbrella term encompassing a multitude of clinically heterogeneous phenotypes. The early and accurate categorisation of patients is hugely important, as MND phenotypes are associated with markedly different prognoses, progression rates, care needs and benefit from divergent management strategies. The categorisation of patients shortly after symptom onset is challenging, and often lengthy clinical monitoring is needed to assign patients to the appropriate phenotypic subgroup. In this study, a multi-class machine-learning strategy was implemented to classify 300 patients based on their radiological profile into diagnostic labels along the UMN-LMN spectrum. A comprehensive panel of cortical thickness measures, subcortical grey matter variables, and white matter integrity metrics were evaluated in a multilayer perceptron (MLP) model. Additional exploratory analyses were also carried out using discriminant function analyses (DFA). Excellent classification accuracy was achieved for amyotrophic lateral sclerosis in the testing cohort (93.7%) using the MLP model, but poor diagnostic accuracy was detected for primary lateral sclerosis (43.8%) and poliomyelitis survivors (60%). Feature importance analyses highlighted the relevance of white matter diffusivity metrics and the evaluation of cerebellar indices, cingulate measures and thalamic radiation variables to discriminate MND phenotypes. Our data suggest that radiological data from single patients may be meaningfully interpreted if large training data sets are available and the provision of diagnostic probability outcomes may be clinically useful in patients with short symptom duration. The computational interpretation of multimodal radiology datasets herald viable diagnostic, prognostic and clinical trial applications.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France.
| | - Aizuri Murad
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Department of Electronics and Computer Science, University of Southampton, UK
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Avisar H, Guardia-Laguarta C, Area-Gomez E, Surface M, Chan AK, Alcalay RN, Lerner B. Lipidomics Prediction of Parkinson's Disease Severity: A Machine-Learning Analysis. JOURNAL OF PARKINSONS DISEASE 2021; 11:1141-1155. [PMID: 33814463 DOI: 10.3233/jpd-202476] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND The role of the lipidome as a biomarker for Parkinson's disease (PD) is a relatively new field that currently only focuses on PD diagnosis. OBJECTIVE To identify a relevant lipidome signature for PD severity markers. METHODS Disease severity of 149 PD patients was assessed by the Unified Parkinson's Disease Rating Scale (UPDRS) and the Montreal Cognitive Assessment (MoCA). The lipid composition of whole blood samples was analyzed, consisting of 517 lipid species from 37 classes; these included all major classes of glycerophospholipids, sphingolipids, glycerolipids, and sterols. To handle the high number of lipids, the selection of lipid species and classes was consolidated via analysis of interrelations between lipidomics and disease severity prediction using the random forest machine-learning algorithm aided by conventional statistical methods. RESULTS Specific lipid classes dihydrosphingomyelin (dhSM), plasmalogen phosphatidylethanolamine (PEp), glucosylceramide (GlcCer), dihydro globotriaosylceramide (dhGB3), and to a lesser degree dihydro GM3 ganglioside (dhGM3), as well as species dhSM(20:0), PEp(38:6), PEp(42:7), GlcCer(16:0), GlcCer(24:1), dhGM3(22:0), dhGM3(16:0), and dhGB3(16:0) contribute to PD severity prediction of UPDRS III score. These, together with age, age at onset, and disease duration, also contribute to prediction of UPDRS total score. We demonstrate that certain lipid classes and species interrelate differently with the degree of severity of motor symptoms between men and women, and that predicting intermediate disease stages is more accurate than predicting less or more severe stages. CONCLUSION Using machine-learning algorithms and methodologies, we identified lipid signatures that enable prediction of motor severity in PD. Future studies should focus on identifying the biological mechanisms linking GlcCer, dhGB3, dhSM, and PEp with PD severity.
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Affiliation(s)
- Hila Avisar
- Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Israel
| | | | - Estela Area-Gomez
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Matthew Surface
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Amanda K Chan
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Roy N Alcalay
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Boaz Lerner
- Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Israel
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Leão T, Madeira SC, Gromicho M, de Carvalho M, Carvalho AM. Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis. J Biomed Inform 2021; 117:103730. [PMID: 33737206 DOI: 10.1016/j.jbi.2021.103730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/17/2021] [Accepted: 02/25/2021] [Indexed: 10/21/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.
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Affiliation(s)
- Tiago Leão
- Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Marta Gromicho
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal; Department of Neurosciences and Mental Health, Centro Hospitalar Universitário de Lisboa-Norte, Lisbon, Portugal
| | - Alexandra M Carvalho
- Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Lisbon ELLIS Unit (Lisbon Unit for Learning and Intelligent Systems), Portugal.
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Steinbach R, Prell T, Gaur N, Stubendorff B, Roediger A, Ilse B, Witte OW, Grosskreutz J. Triage of Amyotrophic Lateral Sclerosis Patients during the COVID-19 Pandemic: An Application of the D50 Model. J Clin Med 2020; 9:jcm9092873. [PMID: 32899481 PMCID: PMC7565659 DOI: 10.3390/jcm9092873] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/30/2020] [Accepted: 09/02/2020] [Indexed: 12/11/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, the management of which requires the continuous provision of multidisciplinary therapies. Owing to the novel coronavirus disease (COVID-19) pandemic, regular contact with ALS patients at our center was severely restricted and patient care was at risk by delay of supportive therapies. We established a triage system based on the D50 disease progression model and were thus able to identify a prospective cohort with high disease aggressiveness (D50 < 30). Thirty-seven patients with highly aggressive disease were actively offered follow-up, either via telephone or on-site, depending on their disease-specific needs and abilities. We describe here the procedures, obstacles, and results of these prescient efforts during the restrictions caused by COVID-19 in the period between March and June 2020. In conclusion, four patients with highly aggressive disease were initiated with non-invasive ventilation and two received a gastrostomy. We could show that a comparable amount of advanced care was induced in a retrospective cohort within a similar time period one year prior to the COVID-19 outbreak. Our workflow to identify high-risk patients via D50 model metrics can be easily implemented and integrated within existing centers. It helped to maintain a high quality of advanced care planning for our ALS patients.
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Affiliation(s)
- Robert Steinbach
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
- Correspondence: ; Tel.: +49-3641-9323-587
| | - Tino Prell
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
- Center for Healthy Ageing, Jena University Hospital, 07747 Jena, Germany
| | - Nayana Gaur
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
| | - Beatrice Stubendorff
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
| | - Annekathrin Roediger
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
| | - Benjamin Ilse
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
| | - Otto W. Witte
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
- Center for Healthy Ageing, Jena University Hospital, 07747 Jena, Germany
| | - Julian Grosskreutz
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany; (T.P.); (N.G.); (B.S.); (A.R.); (B.I.); (O.W.W.); (J.G.)
- Center for Healthy Ageing, Jena University Hospital, 07747 Jena, Germany
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Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier. Mach Learn 2020. [DOI: 10.1007/s10994-020-05869-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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