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Adelson RP, Garikipati A, Zhou Y, Ciobanu M, Tawara K, Barnes G, Singh NP, Mao Q, Das R. Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes. Diagnostics (Basel) 2024; 14:1152. [PMID: 38893680 PMCID: PMC11172278 DOI: 10.3390/diagnostics14111152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.
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Affiliation(s)
| | | | | | | | | | | | | | - Qingqing Mao
- Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA; (R.P.A.); (A.G.); (Y.Z.); (M.C.); (K.T.); (G.B.); (N.P.S.); (R.D.)
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Puchades R, Tung-Chen Y, Salgueiro G, Lorenzo A, Sancho T, Fernández Capitán C. Artificial intelligence for predicting pulmonary embolism: A review of machine learning approaches and performance evaluation. Thromb Res 2024; 234:9-11. [PMID: 38113607 DOI: 10.1016/j.thromres.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 12/02/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Affiliation(s)
- Ramón Puchades
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain.
| | - Yale Tung-Chen
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Giorgina Salgueiro
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Alicia Lorenzo
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Teresa Sancho
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Carmen Fernández Capitán
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
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Liu L, Li Y, Liu N, Luo J, Deng J, Peng W, Bai Y, Zhang G, Zhao G, Yang N, Li C, Long X. Establishment of machine learning-based tool for early detection of pulmonary embolism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107977. [PMID: 38113803 DOI: 10.1016/j.cmpb.2023.107977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/11/2023] [Accepted: 12/11/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients. METHODS We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model. RESULTS We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism. CONCLUSIONS In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.
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Affiliation(s)
- Lijue Liu
- School of Automation, Central South University, Changsha, Hunan 410083, China; Xiangjiang Laboratory, Changsha 410205, China; Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China
| | - Yaming Li
- School of Automation, Central South University, Changsha, Hunan 410083, China
| | - Na Liu
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Jingmin Luo
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Jinhai Deng
- Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London SE1 1UL, UK
| | - Weixiong Peng
- Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan 410000, China; Department of Electrical and Electronic Engineering, College of Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong 518055, China
| | - Yongping Bai
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Guogang Zhang
- Department of Cardiovascular Medicine, The Third Xiangya Hospital, Central South University, Tongzipo Road 138#, Changsha 410008,China.
| | - Guihu Zhao
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Ning Yang
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Chuanchang Li
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
| | - Xueying Long
- Xiangya Hospital, Central South University, Xiangya Road 87#, Changsha 410008, China
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Sadegh-Zadeh SA, Sakha H, Movahedi S, Fasihi Harandi A, Ghaffari S, Javanshir E, Ali SA, Hooshanginezhad Z, Hajizadeh R. Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Comput Biol Med 2023; 167:107696. [PMID: 37979394 DOI: 10.1016/j.compbiomed.2023.107696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients. OBJECTIVE To devise an ML algorithm for early mortality prediction in PE patients by employing clinical and laboratory variables. METHODS This study utilized diverse oversampling techniques to improve the performance of various machine learning models including ANN, SVM, DT, RF, and AdaBoost for early mortality prediction. Appropriate oversampling methods were chosen for each model based on algorithm characteristics and dataset properties. Predictor variables included four lab tests, eight physiological time series indicators, and two general descriptors. Evaluation used metrics like accuracy, F1_score, precision, recall, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves, providing a comprehensive view of models' predictive abilities. RESULTS The findings indicated that the RF model with random oversampling exhibited superior performance among the five models assessed, achieving elevated accuracy and precision alongside high recall for predicting the death class. The oversampling approaches effectively equalized the sample distribution among the classes and enhanced the models' performance. CONCLUSIONS The suggested ML technique can efficiently prognosticate mortality in patients afflicted with acute PE. The RF model with random oversampling can aid healthcare professionals in making well-informed decisions regarding the treatment of patients with acute PE. The study underscores the significance of oversampling methods in managing imbalanced data and emphasizes the potential of ML algorithms in refining early mortality prediction for PE patients.
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Affiliation(s)
- Seyed-Ali Sadegh-Zadeh
- Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, England, United Kingdom
| | - Hanie Sakha
- Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, England, United Kingdom
| | | | | | - Samad Ghaffari
- Cardiovascular Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elnaz Javanshir
- Cardiovascular Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Syed Ahsan Ali
- Health Education England West Midlands, Birmingham, England, United Kingdom
| | - Zahra Hooshanginezhad
- Department of Cardiovascular Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Hajizadeh
- Department of Cardiology, Urmia University of Medical Sciences, Urmia, Iran.
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Fanni SC, Greco G, Rossi S, Aghakhanyan G, Masala S, Scaglione M, Tonerini M, Neri E. Role of artificial intelligence in oncologic emergencies: a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:344-354. [PMID: 37205309 PMCID: PMC10185441 DOI: 10.37349/etat.2023.00138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/13/2023] [Indexed: 05/21/2023] Open
Abstract
Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.
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Affiliation(s)
- Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Giuseppe Greco
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Sara Rossi
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Salvatore Masala
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Michele Tonerini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56126 Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
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Shen J, Casie Chetty S, Shokouhi S, Maharjan J, Chuba Y, Calvert J, Mao Q. Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients. Thromb Res 2022; 216:14-21. [DOI: 10.1016/j.thromres.2022.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 10/18/2022]
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