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Jing L, Ulloa Cerna AE, Good CW, Sauers NM, Schneider G, Hartzel DN, Leader JB, Kirchner HL, Hu Y, Riviello DM, Stough JV, Gazes S, Haggerty A, Raghunath S, Carry BJ, Haggerty CM, Fornwalt BK. A Machine Learning Approach to Management of Heart Failure Populations. JACC-HEART FAILURE 2020; 8:578-587. [PMID: 32387064 DOI: 10.1016/j.jchf.2020.01.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 01/02/2020] [Accepted: 01/02/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. OBJECTIVES This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. METHODS Geisinger electronic health record data were used to train machine learning models to predict 1-year all-cause mortality in 26,971 patients with heart failure who underwent 276,819 clinical episodes. There were 26 clinical variables (demographics, laboratory test results, medications), 90 diagnostic codes, 41 electrocardiogram measurements and patterns, 44 echocardiographic measurements, and 8 evidence-based "care gaps": flu vaccine, blood pressure of <130/80 mm Hg, A1c of <8%, cardiac resynchronization therapy, and active medications (active angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker/angiotensin receptor-neprilysin inhibitor, aldosterone receptor antagonist, hydralazine, and evidence-based beta-blocker) were collected. Care gaps represented actionable variables for which associations with all-cause mortality were modeled from retrospective data and then used to predict the benefit of prospective interventions in 13,238 currently living patients. RESULTS Machine learning models achieved areas under the receiver-operating characteristic curve (AUCs) of 0.74 to 0.77 in a split-by-year training/test scheme, with the nonlinear XGBoost model (AUC: 0.77) outperforming linear logistic regression (AUC: 0.74). Out of 13,238 currently living patients, 2,844 were predicted to die within a year, and closing all care gaps was predicted to save 231 of these lives. Prioritizing patients for intervention by using the predicted reduction in 1-year mortality risk outperformed all other priority rankings (e.g., random selection or Seattle Heart Failure risk score). CONCLUSIONS Machine learning can be used to priority-rank patients most likely to benefit from interventions to optimize evidence-based therapies. This approach may prove useful for optimizing heart failure population health management teams within value-based payment models.
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Affiliation(s)
- Linyuan Jing
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | - Alvaro E Ulloa Cerna
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | | | - Nathan M Sauers
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania
| | | | - Dustin N Hartzel
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania
| | - Joseph B Leader
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania
| | - H Lester Kirchner
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - Yirui Hu
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - David M Riviello
- Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania
| | - Joshua V Stough
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania
| | - Seth Gazes
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania
| | - Allyson Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | - Sushravya Raghunath
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | | | - Christopher M Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Heart Institute, Geisinger, Danville, Pennsylvania
| | - Brandon K Fornwalt
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Heart Institute, Geisinger, Danville, Pennsylvania; Department of Radiology, Geisinger, Danville, Pennsylvania.
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Xiong J, Liang X, Zhao L, Lo B, Li J, Liu C. Improving Accuracy of Heart Failure Detection Using Data Refinement. ENTROPY 2020; 22:e22050520. [PMID: 33286292 PMCID: PMC7517015 DOI: 10.3390/e22050520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 04/25/2020] [Accepted: 04/30/2020] [Indexed: 01/05/2023]
Abstract
Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.
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Affiliation(s)
- Jinle Xiong
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (J.X.); (X.L.); (L.Z.); (J.L.)
| | - Xueyu Liang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (J.X.); (X.L.); (L.Z.); (J.L.)
| | - Lina Zhao
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (J.X.); (X.L.); (L.Z.); (J.L.)
| | - Benny Lo
- The Hamlyn Centre/Department Surgery and Cancer, Imperial College London, London SW7 2AZ, UK;
| | - Jianqing Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (J.X.); (X.L.); (L.Z.); (J.L.)
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (J.X.); (X.L.); (L.Z.); (J.L.)
- Correspondence: ; Tel.: +86-25-8379-3993; Fax: +86-25-8379-3993
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Gossec L, Guyard F, Leroy D, Lafargue T, Seiler M, Jacquemin C, Molto A, Sellam J, Foltz V, Gandjbakhch F, Hudry C, Mitrovic S, Fautrel B, Servy H. Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology. Arthritis Care Res (Hoboken) 2020; 71:1336-1343. [PMID: 30242992 DOI: 10.1002/acr.23768] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 09/18/2018] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Flares in rheumatoid arthritis (RA) and axial spondyloarthritis (SpA) may influence physical activity. The aim of this study was to assess longitudinally the association between patient-reported flares and activity-tracker-provided steps per minute, using machine learning. METHODS This prospective observational study (ActConnect) included patients with definite RA or axial SpA. For a 3-month time period, physical activity was assessed continuously by number of steps/minute, using a consumer grade activity tracker, and flares were self-assessed weekly. Machine-learning techniques were applied to the data set. After intrapatient normalization of the physical activity data, multiclass Bayesian methods were used to calculate sensitivities, specificities, and predictive values of the machine-generated models of physical activity in order to predict patient-reported flares. RESULTS Overall, 155 patients (1,339 weekly flare assessments and 224,952 hours of physical activity assessments) were analyzed. The mean ± SD age for patients with RA (n = 82) was 48.9 ± 12.6 years and was 41.2 ± 10.3 years for those with axial SpA (n = 73). The mean ± SD disease duration was 10.5 ± 8.8 years for patients with RA and 10.8 ± 9.1 years for those with axial SpA. Fourteen patients with RA (17.1%) and 41 patients with axial SpA (56.2%) were male. Disease was well-controlled (Disease Activity Score in 28 joints mean ± SD 2.2 ± 1.2; Bath Ankylosing Spondylitis Disease Activity Index score mean ± SD 3.1 ± 2.0), but flares were frequent (22.7% of all weekly assessments). The model generated by machine learning performed well against patient-reported flares (mean sensitivity 96% [95% confidence interval (95% CI) 94-97%], mean specificity 97% [95% CI 96-97%], mean positive predictive value 91% [95% CI 88-96%], and negative predictive value 99% [95% CI 98-100%]). Sensitivity analyses were confirmatory. CONCLUSION Although these pilot findings will have to be confirmed, the correct detection of flares by machine-learning processing of activity tracker data provides a framework for future studies of remote-control monitoring of disease activity, with great precision and minimal patient burden.
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Affiliation(s)
- Laure Gossec
- Sorbonne Université and Pitié Salpêtrière Hospital, AP-HP, Paris, France
| | | | | | | | | | | | - Anna Molto
- Cochin Hospital, AP-HP, INSERM U1153, PRES Sorbonne Paris-Cité, Paris Descartes University, Paris, France
| | - Jérémie Sellam
- Sorbonne Université, INSERM UMRS 938, Paris, France, St. Antoine Hospital, AP-HP, DHU i2B, Paris, France
| | - Violaine Foltz
- Sorbonne Université and Pitié Salpêtrière Hospital, AP-HP, Paris, France
| | | | | | - Stéphane Mitrovic
- Sorbonne Université and Pitié Salpêtrière Hospital, AP-HP, Paris, France
| | - Bruno Fautrel
- Sorbonne Université and Pitié Salpêtrière Hospital, AP-HP, Paris, France
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Gong J, Bai X, Li DA, Zhao J, Li X. Prognosis Analysis of Heart Failure Based on Recurrent Attention Model. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2019.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis. J Pers Med 2020; 10:jpm10020021. [PMID: 32244292 PMCID: PMC7354442 DOI: 10.3390/jpm10020021] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/09/2020] [Accepted: 03/23/2020] [Indexed: 02/07/2023] Open
Abstract
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.
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Affiliation(s)
- Gopi Battineni
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Correspondence: ; Tel.: +39-333-172-8206
| | - Getu Gamo Sagaro
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Nalini Chinatalapudi
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Francesco Amenta
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Research Department, International Medical Radio Center Foundation (C.I.R.M.), 00144 Roma, Italy
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Ricciardi C, Edmunds KJ, Recenti M, Sigurdsson S, Gudnason V, Carraro U, Gargiulo P. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Sci Rep 2020; 10:2863. [PMID: 32071412 PMCID: PMC7029006 DOI: 10.1038/s41598-020-59873-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 02/04/2020] [Indexed: 11/24/2022] Open
Abstract
The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.
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Affiliation(s)
- Carlo Ricciardi
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.,Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy
| | - Kyle J Edmunds
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland
| | - Marco Recenti
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, (Hjartavernd), Kópavogur, Iceland.,Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Ugo Carraro
- CIR-Myo, Department of Biomedical Sciences, University of, Padova, Italy.,A&C M-C Foundation for Translational Myology, Padova, Italy
| | - Paolo Gargiulo
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland. .,Department of Science, Landspítali, Reykjavík, Iceland.
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Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 2020; 20:16. [PMID: 32013925 PMCID: PMC6998201 DOI: 10.1186/s12911-020-1023-5] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 01/14/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. METHODS In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. RESULTS Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. CONCLUSIONS This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.
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Affiliation(s)
- Davide Chicco
- Krembil Research Institute, Toronto, Ontario, Canada
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Nunes D, Rocha T, Traver V, Teixeira C, Ruano M, Paredes S, Carvalho P, Henriques J. Latent states extraction through Kalman Filter for the prediction of heart failure decompensation events. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3947-3950. [PMID: 31946736 DOI: 10.1109/embc.2019.8857591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions.In this paper we introduce a methodology with the goal of finding onsets of worsening progressions from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the model composed by only two features using a telemonitoring dataset (myHeart) with 41 patients. Results were achieved by applying leave-one-subject-out validation and correspond to a geometric mean of 83.67%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.
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Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure. Sci Rep 2020; 10:130. [PMID: 31924803 PMCID: PMC6954181 DOI: 10.1038/s41598-019-56889-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 12/18/2019] [Indexed: 11/08/2022] Open
Abstract
The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.
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60
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Predicting bipolar disorder and schizophrenia based on non-overlapping genetic phenotypes using deep neural network. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-019-00346-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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61
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Porumb M, Iadanza E, Massaro S, Pecchia L. A convolutional neural network approach to detect congestive heart failure. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101597] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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63
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Ahmed FZ, Taylor JK, Green C, Moore L, Goode A, Black P, Howard L, Fullwood C, Zaidi A, Seed A, Cunnington C, Motwani M. Triage-HF Plus: a novel device-based remote monitoring pathway to identify worsening heart failure. ESC Heart Fail 2019; 7:107-116. [PMID: 31794140 PMCID: PMC7083434 DOI: 10.1002/ehf2.12529] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 08/08/2019] [Accepted: 09/06/2019] [Indexed: 11/23/2022] Open
Abstract
Aims Remote monitoring of patients with physiological data derived from cardiac implanted electronic devices (CIEDs) offers potential to reconfigure clinical services. The ‘Heart Failure Risk Score' (HFRS) uses input from integrated device physiological monitoring to risk‐stratify patients as low‐risk, medium‐risk, or high‐risk of a heart failure event in the next 30 days. This study aimed to evaluate a novel clinical pathway utilizing a combination of CIED risk‐stratification and telephone triage to identify patients with worsening heart failure (WHF). Methods and results A prospective, single‐centre, real‐world evaluation of the ‘Triage‐HF Plus' clinical pathway (HFRS in combination with telephone triage) over a 27 month period. One hundred and fifty‐seven high‐risk HFRS transmissions were referred for telephone triage assessment. Interventions were at the discretion of the clinical assessor acting in accordance with clinical guidelines. An additional 3month consecutive sample of low and medium HFRS transmissions (control group) were also contacted for telephone triage assessment (n = 98). Successful telephone contact was made in 127 (81%) of referred high‐risk HFRS cases: 71 (55.9%) were confirmed to have WHF requiring intervention; 19 (14.9%) had an alternative acute medical problem; one patient had been recently discharged from hospital with WHF; and 36 (28.0%) had no apparent cause for the high score. In the control group, only one patient had symptoms of WHF. The sensitivity and specificity of CIED‐based remote monitoring to identify WHF 98.6% (92.5–100.0%) and 63.4% (55.2–71.0%), respectively. Conclusions The Triage‐HF Plus clinical pathway is a potentially useful remote monitoring tool for patients with heart failure and in situ CIEDs.
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Affiliation(s)
- Fozia Zahir Ahmed
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK.,Department of Cardiology, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Joanne K Taylor
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK.,Department of Cardiology, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK.,Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Caroline Green
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Lucy Moore
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Angelic Goode
- Lancashire Cardiac Centre, Blackpool Victoria Hospital, Blackpool, UK
| | - Paula Black
- Lancashire Cardiac Centre, Blackpool Victoria Hospital, Blackpool, UK
| | - Lesley Howard
- Lancashire Cardiac Centre, Blackpool Victoria Hospital, Blackpool, UK
| | - Catherine Fullwood
- Manchester Academic Health Science Centre, Research and Innovation, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK.,Centre for Biostatistics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Amir Zaidi
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Alison Seed
- Lancashire Cardiac Centre, Blackpool Victoria Hospital, Blackpool, UK
| | - Colin Cunnington
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK.,Department of Cardiology, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Manish Motwani
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK.,Department of Cardiology, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
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Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients. J Clin Med 2019; 8:jcm8091298. [PMID: 31450546 PMCID: PMC6780582 DOI: 10.3390/jcm8091298] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/20/2019] [Accepted: 08/22/2019] [Indexed: 12/23/2022] Open
Abstract
The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.
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Mezzatesta S, Torino C, Meo PD, Fiumara G, Vilasi A. A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:9-15. [PMID: 31319965 DOI: 10.1016/j.cmpb.2019.05.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 04/15/2019] [Accepted: 05/09/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients. METHODS To achieve our aim, machine learning techniques have been used. Two datasets have been taken into consideration: the first is an Italian dataset obtained from the Istituto di Fisiologia Clinica of Consiglio Nazionale delle Ricerche of Reggio Calabria; the second is an American dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) repository. From each one we obtained 5 datasets, according to the outcome of interest. We tested different types of algorithm (both linear and non-linear), but the final choice was to use Support Vector Machine. In particular, we obtained the best performances using the non-linear SVC with RBF kernel algorithm, optimizing it with GridSearch. The last is an algorithm useful to search the best combination of hyper-parameters (in our case, to find the best couple (C, γ)), in order to improve the accuracy of the algorithm. RESULTS The use of non-linear SVC with RBF kernel algorithm, optimized with GridSearch, allowed to obtain an accuracy of 95.25% in the Italian dataset and of 92.15% in the American dataset, in a timeframe of 2.5 years,in the prediction of Ischaemic Heart Disease. A worse performance was obtained for the other outcomes. CONCLUSIONS The machine learning-based approach applied in our study is able to predict, with a high accuracy, the outbreak of cardiovascular diseases in patients on dialysis.
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Affiliation(s)
- Sabrina Mezzatesta
- Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy
| | - Claudia Torino
- Institute of Clinical Physiology - Reggio Calabria Unit, Laboratory of Bioinformatics, National Research Council, Italy
| | - Pasquale De Meo
- Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy
| | - Giacomo Fiumara
- Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy
| | - Antonio Vilasi
- Institute of Clinical Physiology - Reggio Calabria Unit, Laboratory of Bioinformatics, National Research Council, Italy.
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Eggerth A, Hayn D, Schreier G. Medication management needs information and communications technology-based approaches, including telehealth and artificial intelligence. Br J Clin Pharmacol 2019; 86:2000-2007. [PMID: 31271668 PMCID: PMC7495302 DOI: 10.1111/bcp.14045] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 06/13/2019] [Accepted: 06/17/2019] [Indexed: 01/07/2023] Open
Abstract
Life expectancy is rising in most parts of the world as is the prevalence of chronic diseases. Suboptimal adherence to long-term medications is still rather the norm than the exception, although it is well known that suboptimal adherence compromises the therapeutic effectiveness. Information and communications technology provides new concepts for improving adherence to medications. These so-called telehealth concepts or services help to implement closed-loop healthcare paradigms and to establish collaborative care networks involving all stakeholders relevant to optimising the overall medication therapy. Together with data from Electronic Health Records and Electronic Medical Records, these networks pave the way to data-driven decision support systems. Recent advances in machine learning, predictive analytics, and artificial intelligence allow further steps towards fully autonomous telehealth systems. This might bring advances in the future: disburden healthcare professionals from repetitive tasks, enable them to timely react to critical situations, and offer a comprehensive overview of the patients' medication status. Advanced analytics can help to assess whether patients have taken their medications as prescribed, to improve adherence via automatic reminders. Ultimately, all relevant data sources need to be collated into a basis for data-driven methods, with the goal to assist healthcare professionals in guiding patients to obtain the best possible health status, with a reasonable resource utilisation and a risk-adjusted safety and privacy approach. This paper summarises the state-of-the-art of telehealth and artificial intelligence applications in medication management. It focuses on 3 major aspects: latest technologies, current applications, and patient related issues.
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Affiliation(s)
- Alphons Eggerth
- Digital Health Information Systems, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Austria.,Institute of Neural Engineering, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Austria
| | - Dieter Hayn
- Digital Health Information Systems, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Austria
| | - Günter Schreier
- Digital Health Information Systems, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Austria.,Institute of Neural Engineering, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Austria
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Bhurane AA, Sharma M, San-Tan R, Acharya UR. An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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68
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Tripathy RK, Paternina MRA, Arrieta JG, Zamora-Méndez A, Naik GR. Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 173:53-65. [PMID: 31046996 DOI: 10.1016/j.cmpb.2019.03.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 02/12/2019] [Accepted: 03/13/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. METHODS The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. RESULTS The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. CONCLUSIONS The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.
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Affiliation(s)
- R K Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
| | - Mario R A Paternina
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | | | - Alejandro Zamora-Méndez
- Electrical Engineering Faculty, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mich. 58030, Mexico
| | - Ganesh R Naik
- MARCS Institute, Western Sydney University Kingswood, NSW - 2747, Australia
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Bello GA, Dawes TJ, Duan J, Biffi C, de Marvao A, Howard LSGE, Gibbs JSR, Wilkins MR, Cook SA, Rueckert D, O’Regan DP. Deep learning cardiac motion analysis for human survival prediction. NAT MACH INTELL 2019; 1:95-104. [PMID: 30801055 PMCID: PMC6382062 DOI: 10.1038/s42256-019-0019-2] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 01/09/2019] [Indexed: 01/09/2023]
Abstract
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
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Affiliation(s)
- Ghalib A. Bello
- MRC London Institute of Medical Sciences, Imperial College London,UK
| | - Timothy J.W. Dawes
- MRC London Institute of Medical Sciences, Imperial College London,UK
- National Heart and Lung Institute, Imperial College London, UK
| | - Jinming Duan
- MRC London Institute of Medical Sciences, Imperial College London,UK
- Department of Computing, Imperial College London, UK
| | - Carlo Biffi
- MRC London Institute of Medical Sciences, Imperial College London,UK
- Department of Computing, Imperial College London, UK
| | - Antonio de Marvao
- MRC London Institute of Medical Sciences, Imperial College London,UK
| | | | - J. Simon R. Gibbs
- National Heart and Lung Institute, Imperial College London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Martin R. Wilkins
- Division of Experimental Medicine, Department of Medicine, Imperial College London, UK
| | - Stuart A. Cook
- MRC London Institute of Medical Sciences, Imperial College London,UK
- National Heart and Lung Institute, Imperial College London, UK
- National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore
| | | | - Declan P. O’Regan
- MRC London Institute of Medical Sciences, Imperial College London,UK
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. High-Throughput Metabolomics by 1D NMR. Angew Chem Int Ed Engl 2019; 58:968-994. [PMID: 29999221 PMCID: PMC6391965 DOI: 10.1002/anie.201804736] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Indexed: 12/12/2022]
Abstract
Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.
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Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P.Via Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Veronica Ghini
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Gaia Meoni
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Cristina Licari
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of FlorenceLargo Brambilla 3FlorenceItaly
| | - Paola Turano
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
| | - Claudio Luchinat
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
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Nunes D, Leal A, Rocha T, Traver V, Teixeira C, Ruano M, Paredes S, Carvalho P, Henriques J. Risk Prediction of Heart Failure Decompensation Events in Multiparametric Feature Spaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4030-4033. [PMID: 30441241 DOI: 10.1109/embc.2018.8513096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions. In this paper we introduce a methodology with the goal of finding relevant feature spaces from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the feature space comprising the following features: mean weight, standard deviation of the blood pressure and mean of extra-thoracic impedance in a time window of 20 days. Results were achieved by applying leave-one-out validation and correspond to a geometric mean of 88.32%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. Hochdurchsatz‐Metabolomik mit 1D‐NMR. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201804736] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P. Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Veronica Ghini
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Gaia Meoni
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Cristina Licari
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of Florence Largo Brambilla 3 Florence Italien
| | - Paola Turano
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
| | - Claudio Luchinat
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
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Mlakar M, Puddu PE, Somrak M, Bonfiglio S, Luštrek M. Mining telemonitored physiological data and patient-reported outcomes of congestive heart failure patients. PLoS One 2018; 13:e0190323. [PMID: 29494601 PMCID: PMC5832202 DOI: 10.1371/journal.pone.0190323] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 12/12/2017] [Indexed: 11/19/2022] Open
Abstract
This paper addresses patient-reported outcomes (PROs) and telemonitoring in congestive heart failure (CHF), both increasingly important topics. The interest in CHF trials is shifting from hard end-points such as hospitalization and mortality, to softer end-points such health-related quality of life. However, the relation of these softer end-points to objective parameters is not well studied. Telemonitoring is suitable for collecting both patient-reported outcomes and objective parameters. Most telemonitoring studies, however, do not take full advantage of the available sensor technology and intelligent data analysis. The Chiron clinical observational study was performed among 24 CHF patients (17 men and 7 women, age 62.9 ± 9.4 years, 15 NYHA class II and 9 class III, 10 of ishaemic, aetiology, 6 dilated, 2 valvular, and 6 of multiple aetiologies or cardiomyopathy) in Italy and UK. A large number of physiological and ambient parameters were collected by wearable and other devices, together with PROs describing how well the patients felt, over 1,086 days of observation. The resulting data were mined for relations between the objective parameters and the PROs. The objective parameters (humidity, ambient temperature, blood pressure, SpO2, and sweeting intensity) could predict the PROs with accuracies up to 86% and AUC up to 0.83, making this the first report providing evidence for ambient and physiological parameters to be objectively related to PROs in CHF patients. We also analyzed the relations in the predictive models, gaining some insights into what affects the feeling of health, which was also generally not attempted in previous investigations. The paper strongly points to the possibility of using PROs as primary end-points in future trials.
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Affiliation(s)
- Miha Mlakar
- Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenija
| | - Paolo Emilio Puddu
- Department of Cardiovascular, Respiratory, Nephrological, Anesthesiological and Geriatric Sciences, Sapienza University of Rome, Rome, Italy
| | - Maja Somrak
- Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenija
| | | | - Mitja Luštrek
- Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenija
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Kim YK, Na KS. Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective. Prog Neuropsychopharmacol Biol Psychiatry 2018. [PMID: 28648568 DOI: 10.1016/j.pnpbp.2017.06.024] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.
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Affiliation(s)
- Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Kyoung-Sae Na
- Department of Psychiatry, Gachon University Gil Medical Center, Incheon, Republic of Korea.
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Cohort Description for MADDEC – Mass Data in Detection and Prevention of Serious Adverse Events in Cardiovascular Disease. IFMBE PROCEEDINGS 2018. [DOI: 10.1007/978-981-10-5122-7_278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Alonso-Betanzos A, Bolón-Canedo V. Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1065:607-626. [PMID: 30051410 DOI: 10.1007/978-3-319-77932-4_37] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Medicine will experience many changes in the coming years because the so-called "medicine of the future" will be increasingly proactive, featuring four basic elements: predictive, personalized, preventive, and participatory. Drivers for these changes include the digitization of data in medicine and the availability of computational tools that deal with massive volumes of data. Thus, the need to apply machine-learning methods to medicine has increased dramatically in recent years while facing challenges related to an unprecedented large number of clinically relevant features and highly specific diagnostic tests. Advances regarding data-storage technology and the progress concerning genome studies have enabled collecting vast amounts of patient clinical details, thus permitting the extraction of valuable information. In consequence, big-data analytics is becoming a mandatory technology to be used in the clinical domain.Machine learning and big-data analytics can be used in the field of cardiology, for example, for the prediction of individual risk factors for cardiovascular disease, for clinical decision support, and for practicing precision medicine using genomic information. Several projects employ machine-learning techniques to address the problem of classification and prediction of heart failure (HF) subtypes and unbiased clustering analysis using dense phenomapping to identify phenotypically distinct HF categories. In this chapter, these ideas are further presented, and a computerized model allowing the distinction between two major HF phenotypes on the basis of ventricular-volume data analysis is discussed in detail.
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Gentil ML, Cuggia M, Fiquet L, Hagenbourger C, Le Berre T, Banâtre A, Renault E, Bouzille G, Chapron A. Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature. BMC Med Inform Decis Mak 2017; 17:139. [PMID: 28946908 PMCID: PMC5613384 DOI: 10.1186/s12911-017-0538-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 09/14/2017] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Primary care data gathered from Electronic Health Records are of the utmost interest considering the essential role of general practitioners (GPs) as coordinators of patient care. These data represent the synthesis of the patient history and also give a comprehensive picture of the population health status. Nevertheless, discrepancies between countries exist concerning routine data collection projects. Therefore, we wanted to identify elements that influence the development and durability of such projects. METHODS A systematic review was conducted using the PubMed database to identify worldwide current primary care data collection projects. The gray literature was also searched via official project websites and their contact person was emailed to obtain information on the project managers. Data were retrieved from the included studies using a standardized form, screening four aspects: projects features, technological infrastructure, GPs' roles, data collection network organization. RESULTS The literature search allowed identifying 36 routine data collection networks, mostly in English-speaking countries: CPRD and THIN in the United Kingdom, the Veterans Health Administration project in the United States, EMRALD and CPCSSN in Canada. These projects had in common the use of technical facilities that range from extraction tools to comprehensive computing platforms. Moreover, GPs initiated the extraction process and benefited from incentives for their participation. Finally, analysis of the literature data highlighted that governmental services, academic institutions, including departments of general practice, and software companies, are pivotal for the promotion and durability of primary care data collection projects. CONCLUSION Solid technical facilities and strong academic and governmental support are required for promoting and supporting long-term and wide-range primary care data collection projects.
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Affiliation(s)
- Marie-Line Gentil
- Department of General Practice, University of Rennes 1, F-35000, Rennes, France.
- CIC (Clinical investigation center) INSERM 1414, F-35000, Rennes, France.
| | - Marc Cuggia
- INSERM, U1099, F-35000, Rennes, France
- University of Rennes 1, LTSI (Laboratory for signal and image processing), F-35000, Rennes, France
- CHU Rennes, CIC Inserm 1414, F-35000, Rennes, France
- CHU Rennes, Centre de Données Cliniques, F-35000, Rennes, France
| | - Laure Fiquet
- Department of General Practice, University of Rennes 1, F-35000, Rennes, France
- CIC (Clinical investigation center) INSERM 1414, F-35000, Rennes, France
| | | | - Thomas Le Berre
- Department of General Practice, University of Rennes 1, F-35000, Rennes, France
| | - Agnès Banâtre
- Department of General Practice, University of Rennes 1, F-35000, Rennes, France
- CIC (Clinical investigation center) INSERM 1414, F-35000, Rennes, France
| | - Eric Renault
- University of Rennes 1, LTSI (Laboratory for signal and image processing), F-35000, Rennes, France
| | - Guillaume Bouzille
- INSERM, U1099, F-35000, Rennes, France
- University of Rennes 1, LTSI (Laboratory for signal and image processing), F-35000, Rennes, France
- CHU Rennes, CIC Inserm 1414, F-35000, Rennes, France
- CHU Rennes, Centre de Données Cliniques, F-35000, Rennes, France
| | - Anthony Chapron
- Department of General Practice, University of Rennes 1, F-35000, Rennes, France
- CIC (Clinical investigation center) INSERM 1414, F-35000, Rennes, France
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Blood AJ, Fraiche AM, Eapen ZJ. Is an Admission for Decompensated Heart Failure Inevitable? Prog Cardiovasc Dis 2017; 60:171-177. [PMID: 28733079 DOI: 10.1016/j.pcad.2017.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 07/16/2017] [Indexed: 01/27/2023]
Abstract
Given the high prevalence of heart failure (HF) and the profound impact on morbid, mortality, and health care costs, strategies to improve outcomes and reduce cost have become progressively more attractive. Reducing HF hospitalizations as a study outcome has gained traction in recent years. The basic hypothesis of these investigations is that HF hospitalizations are preventable and harmful. This article examines advancements in pharmacotherapy, medical devices, and health care delivery techniques targeting reductions in HF hospitalizations and evaluates the role and implications of hospitalization in the natural history of HF.
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Affiliation(s)
- Alexander J Blood
- Department of Medicine, Duke University Medical Center, Durham, NC, United States
| | - Ariane M Fraiche
- Department of Medicine, Duke University Medical Center, Durham, NC, United States
| | - Zubin J Eapen
- Department of Medicine, Duke University Medical Center, Durham, NC, United States.
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Tripoliti EE, Papadopoulos TG, Karanasiou GS, Kalatzis FG, Goletsis Y, Bechlioulis A, Ghimenti S, Lomonaco T, Bellagambi F, Trivella MG, Fuoco R, Marzilli M, Scali MC, Naka KK, Errachid A, Fotiadis DI. A computational approach for the estimation of heart failure patients status using saliva biomarkers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3648-3651. [PMID: 29060689 DOI: 10.1109/embc.2017.8037648] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The aim of this work is to present a computational approach for the estimation of the severity of heart failure (HF) in terms of New York Heart Association (NYHA) class and the characterization of the status of the HF patients, during hospitalization, as acute, progressive or stable. The proposed method employs feature selection and classification techniques. However, it is differentiated from the methods reported in the literature since it exploits information that biomarkers fetch. The method is evaluated on a dataset of 29 patients, through a 10-fold-cross-validation approach. The accuracy is 94 and 77% for the estimation of HF severity and the status of HF patients during hospitalization, respectively.
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