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Yu H, Zhao X, Dong D, Chen C. Hamiltonian Identification via Quantum Ensemble Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:11261-11275. [PMID: 37030784 DOI: 10.1109/tnnls.2023.3258622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Identifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess, verification, and judgment. In the parameter interval guess step, the parameter interval is divided into several sub-intervals and the true Hamiltonian parameter is guessed in one of them. In the parameter interval verification step, cross verification is applied to verify the accuracy of the guess. In the parameter interval judgment step, an adaptive interval judgment (AIJ) algorithm is designed to determine the sub-interval containing the true Hamiltonian parameter. Numerical results on two typical quantum systems, i.e., two-level quantum systems and three-level quantum systems, demonstrate the effectiveness and superior performance of the proposed approach for quantum Hamiltonian identification.
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Ramón A, Bas A, Herrero S, Blasco P, Suárez M, Mateo J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. J Clin Med 2024; 13:1837. [PMID: 38610602 PMCID: PMC11013017 DOI: 10.3390/jcm13071837] [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: 02/21/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir's clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB's effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.
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Affiliation(s)
- Antonio Ramón
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Andrés Bas
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
| | - Santiago Herrero
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
| | - Pilar Blasco
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Miguel Suárez
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Department of Gastroenterology, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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Date P, Smith W. Quantum discriminator for binary classification. Sci Rep 2024; 14:1328. [PMID: 38225371 PMCID: PMC10789793 DOI: 10.1038/s41598-023-46469-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/01/2023] [Indexed: 01/17/2024] Open
Abstract
Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces-this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in [Formula: see text] time, and inferencing is performed on a universal quantum computer in [Formula: see text] time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit, and outputs the predicted label. We analyze its performance on the Iris and Bars and Stripes data sets, and show that it can attain 99% accuracy in simulation.
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Affiliation(s)
- Prasanna Date
- Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37830, USA.
| | - Wyatt Smith
- University of Tennessee, Knoxville, Tennessee, 37996, USA
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Casillas N, Ramón A, Torres AM, Blasco P, Mateo J. Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves. Viruses 2023; 15:2184. [PMID: 38005862 PMCID: PMC10675561 DOI: 10.3390/v15112184] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/21/2023] [Accepted: 10/28/2023] [Indexed: 11/26/2023] Open
Abstract
The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.
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Affiliation(s)
- Nazaret Casillas
- Department of Internal Medicine, Hospital Virgen De La Luz, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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Suárez M, Martínez R, Torres AM, Torres B, Mateo J. A Machine Learning Method to Identify the Risk Factors for Liver Fibrosis Progression in Nonalcoholic Steatohepatitis. Dig Dis Sci 2023; 68:3801-3809. [PMID: 37477764 DOI: 10.1007/s10620-023-08031-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/30/2023] [Indexed: 07/22/2023]
Abstract
AIM Nonalcoholic fatty liver disease (NAFLD) is a silent epidemy that has become the most common chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an advanced stage of NAFLD, which is linked to a high risk of cirrhosis and hepatocellular carcinoma. The aim of this study is to develop a predictive model to identify the main risk factors associated with the progression of hepatic fibrosis in patients with NASH. METHODS A database from a multicenter retrospective cross-sectional study was analyzed. A total of 215 patients with NASH biopsy-proven diagnosed were collected. NAFLD Activity Score and Kleiner scoring system were used to diagnose and staging these patients. Noninvasive tests (NITs) scores were added to identify which one were more reliable for follow-up and to avoid biopsy. For analysis, different Machine Learning methods were implemented, being the eXtreme Gradient Booster (XGB) system the proposed algorithm to develop the predictive model. RESULTS The most important variable in this predictive model was High-density lipoprotein (HDL) cholesterol, followed by systemic arterial hypertension and triglycerides (TG). NAFLD Fibrosis Score (NFS) was the most reliable NIT. As for the proposed method, XGB obtained higher results than the second method, K-Nearest Neighbors, in terms of accuracy (95.05 vs. 90.42) and Area Under the Curve (0.95 vs. 0.91). CONCLUSIONS HDL cholesterol, systemic arterial hypertension, and TG were the most important risk factors for liver fibrosis progression in NASH patients. NFS is recommended for monitoring and decision making.
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Affiliation(s)
- Miguel Suárez
- Gastroenterology Department, Virgen de La Luz Hospital, Av. Hermandad de Donantes de Sangre, 1, 16002, Cuenca, Spain.
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain.
| | - Raquel Martínez
- Gastroenterology Department, Virgen de La Luz Hospital, Av. Hermandad de Donantes de Sangre, 1, 16002, Cuenca, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | | | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
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Kang BS, Lee SU, Hong S, Choi SK, Shin JE, Wie JH, Jo YS, Kim YH, Kil K, Chung YH, Jung K, Hong H, Park IY, Ko HS. Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms. Sci Rep 2023; 13:13356. [PMID: 37587201 PMCID: PMC10432552 DOI: 10.1038/s41598-023-39680-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/28/2023] [Indexed: 08/18/2023] Open
Abstract
This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks' gestation), E1 (11-13 weeks' gestation) and M1 (14-24 weeks' gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.
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Affiliation(s)
- Byung Soo Kang
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seon Ui Lee
- Department of Obstetrics and Gynecology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Subeen Hong
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sae Kyung Choi
- Department of Obstetrics and Gynecology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jae Eun Shin
- Department of Obstetrics and Gynecology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeong Ha Wie
- Department of Obstetrics and Gynecology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yun Sung Jo
- Department of Obstetrics and Gynecology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yeon Hee Kim
- Department of Obstetrics and Gynecology, Uijeongbu St. Mary's Hospital,, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kicheol Kil
- Department of Obstetrics and Gynecology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoo Hyun Chung
- Department of Obstetrics and Gynecology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | - In Yang Park
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun Sun Ko
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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Ramón A, Zaragozá M, Torres AM, Cascón J, Blasco P, Milara J, Mateo J. Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab. J Clin Med 2022; 11:jcm11164729. [PMID: 36012968 PMCID: PMC9410189 DOI: 10.3390/jcm11164729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/05/2022] [Accepted: 08/07/2022] [Indexed: 11/16/2022] Open
Abstract
Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (SRF). The aim of our study was to provide evidence on predictors of poor outcome in patients with COVID-19 treated with tocilizumab, using machine learning (ML) techniques. We conducted a retrospective study, analyzing the clinical, laboratory and sociodemographic data of patients admitted for severe COVID-19 with SRF, treated with tocilizumab. The extreme gradient boost (XGB) method had the highest balanced accuracy (93.16%). The factors associated with a worse outcome of tocilizumab use in terms of mortality were: baseline situation at the start of tocilizumab treatment requiring invasive mechanical ventilation (IMV), elevated ferritin, lactate dehydrogenase (LDH) and glutamate-pyruvate transaminase (GPT), lymphopenia, and low PaFi [ratio between arterial oxygen pressure and inspired oxygen fraction (PaO2/FiO2)] values. The factors associated with a worse outcome of tocilizumab use in terms of hospital stay were: baseline situation at the start of tocilizumab treatment requiring IMV or supplemental oxygen, elevated levels of ferritin, glutamate-oxaloacetate transaminase (GOT), GPT, C-reactive protein (CRP), LDH, lymphopenia, and low PaFi values. In our study focused on patients with severe COVID-19 treated with tocilizumab, the factors that were weighted most strongly in predicting worse clinical outcome were baseline status at the start of tocilizumab treatment requiring IMV and hyperferritinemia.
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Affiliation(s)
- Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Marta Zaragozá
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Ana María Torres
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Joaquín Cascón
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Javier Milara
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- Centre for Biomedical Research Network on Respiratory Diseases (CIBERES), Health Institute Carlos III, 28029 Madrid, Spain
- Correspondence:
| | - Jorge Mateo
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
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Ramón A, Torres AM, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. J Investig Med 2022; 70:jim-2021-002278. [PMID: 35850970 DOI: 10.1136/jim-2021-002278] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2022] [Indexed: 01/08/2023]
Abstract
Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. The aim of this single-center observational study is to classify, based on different types of variables, adult patients with COVID-19 at increased risk of mortality. SARS-CoV-2 infection was defined by a positive reverse transcriptase PCR. A total of 203 patients were admitted between March 15 and June 15, 2020 to a tertiary hospital. Data were extracted from the electronic medical record. Four supervised ML algorithms (k-nearest neighbors (KNN), decision tree (DT), Gaussian naïve Bayes (GNB) and support vector machine (SVM)) were compared with the eXtreme Gradient Boosting (XGB) method proposed to have excellent scalability and high running speed, among other qualities. The results indicate that the XGB method has the best prediction accuracy (92%), high precision (>0.92) and high recall (>0.92). The KNN, SVM and DT approaches present moderate prediction accuracy (>80%), moderate recall (>0.80) and moderate precision (>0.80). The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.
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Affiliation(s)
- Antonio Ramón
- Pharmacy Department, General University Hospital Consortium of Valencia, Valencia, Spain
| | - Ana Maria Torres
- Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Javier Milara
- Pharmacy Department, General University Hospital Consortium of Valencia, Valencia, Spain
- Pharmacy Department, University of Valencia, Valencia, Spain
| | - Joaquín Cascón
- Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Pilar Blasco
- Pharmacy Department, General University Hospital Consortium of Valencia, Valencia, Spain
| | - Jorge Mateo
- Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
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Mora D, Nieto JA, Mateo J, Bikdeli B, Barco S, Trujillo-Santos J, Soler S, Font L, Bosevski M, Monreal M. Machine Learning to Predict Outcomes in Patients with Acute Pulmonary Embolism Who Prematurely Discontinued Anticoagulant Therapy. Thromb Haemost 2022; 122:570-577. [PMID: 34107539 DOI: 10.1055/a-1525-7220] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Patients with pulmonary embolism (PE) who prematurely discontinue anticoagulant therapy (<90 days) are at an increased risk for death or recurrences. METHODS We used the data from the RIETE (Registro Informatizado de Pacientes con Enfermedad TromboEmbólica) registry to compare the prognostic ability of five machine-learning (ML) models and logistic regression to identify patients at increased risk for the composite of fatal PE or recurrent venous thromboembolism (VTE) 30 days after discontinuation. ML models included decision tree, k-nearest neighbors algorithm, support vector machine, Ensemble, and neural network [NN]. A "full" model with 70 variables and a "reduced" model with 23 were analyzed. Model performance was assessed by confusion matrix metrics on the testing data for each model and a calibration plot. RESULTS Among 34,447 patients with PE, 1,348 (3.9%) discontinued therapy prematurely. Fifty-one (3.8%) developed fatal PE or sudden death and 24 (1.8%) had nonfatal VTE recurrences within 30 days after discontinuation. ML-NN was the best method for identification of patients experiencing the composite endpoint, predicting the composite outcome with an area under receiver operating characteristic (ROC) curve of 0.96 (95% confidence interval [CI]: 0.95-0.98), using either 70 or 23 variables captured before discontinuation. Similar numbers were obtained for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The discrimination of logistic regression was inferior (area under ROC curve, 0.76 [95% CI: 0.70-0.81]). Calibration plots showed similar deviations from the perfect line for ML-NN and logistic regression. CONCLUSION The ML-NN method very well predicted the composite outcome after premature discontinuation of anticoagulation and outperformed traditional logistic regression.
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Affiliation(s)
- Damián Mora
- Department of Internal Medicine, Hospital Virgen de la Luz, Cuenca, Spain
| | - José A Nieto
- Department of Internal Medicine, Hospital Virgen de la Luz, Cuenca, Spain
| | - Jorge Mateo
- Neurobiological Research Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Behnood Bikdeli
- Cardiovascular Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.,Yale/YNHH Center for Outcomes Research and Evaluation, New Haven, Connecticut, United States.,Cardiovascular Research Foundation (CRF), New York, New York, United States
| | - Stefano Barco
- Clinic of Angiology, University Hospital Zurich, Zurich, Switzerland.,Center for Thrombosis and Hemostasis, University Hospital Mainz, Mainz, Germany
| | - Javier Trujillo-Santos
- Department of Internal Medicine, Hospital General Universitario Santa Lucía, Universidad Católica de Murcia, Murcia, Spain
| | - Silvia Soler
- Department of Internal Medicine, Hospital Olot i Comarcal de la Garrotxa, Gerona, Spain
| | - Llorenç Font
- Department of Haematology, Hospital de Tortosa Verge de la Cinta, Tarragona, Spain
| | - Marijan Bosevski
- Faculty of Medicine, University Cardiology Clinic, Skopje, Republic of Macedonia
| | - Manuel Monreal
- Department of Internal Medicine, Hospital Germans Trias i Pujol, Badalona, Barcelona, Spain.,Department of Medicine, Universidad Católica de Murcia, Murcia, Spain
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Jiang K, Tang J, Wang Y, Qiu C, Zhang Y, Lin C. EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity. Front Neurosci 2020; 14:829. [PMID: 32848581 PMCID: PMC7423875 DOI: 10.3389/fnins.2020.00829] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 07/16/2020] [Indexed: 12/04/2022] Open
Abstract
In the field of brain-computer interface (BCI), selecting efficient and robust features is very seductive for artificial intelligence (AI)-assisted clinical diagnosis. In this study, based on an embedded feature selection model, we construct a stacked deep structure for feature selection in a layer-by-layer manner. Its promising performance is guaranteed by the stacked generalized principle that random projections added into the original features can help us to continuously open the manifold structure existing in the original feature space in a stacked way. With such benefits, the original input feature space becomes more linearly separable. We use the epilepsy EEG data provided by the University of Bonn to evaluate our model. Based on the EEG data, we construct three classification tasks. On each task, we use different feature selection models to select features and then use two classifiers to perform classification based on the selected features. Our experimental results show that features selected by our new structure are more meaningful and helpful to the classifier hence generates better performance than benchmarking models.
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Affiliation(s)
- Kui Jiang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Jiaxi Tang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Yulong Wang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Chengyu Qiu
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Yuanpeng Zhang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong, China
| | - Chuang Lin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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11
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Dong D, Xing X, Ma H, Chen C, Liu Z, Rabitz H. Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:3581-3593. [PMID: 31295133 DOI: 10.1109/tcyb.2019.2921424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.
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12
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Wu C, Qi B, Chen C, Dong D. Robust Learning Control Design for Quantum Unitary Transformations. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:4405-4417. [PMID: 27705875 DOI: 10.1109/tcyb.2016.2610979] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.
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