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Cai W, Wu X, Chen Y, Chen J, Lin X. Risk Factors and Prediction of 28-Day-All Cause Mortality Among Critically Ill Patients with Acute Pancreatitis Using Machine Learning Techniques: A Retrospective Analysis of Multi-Institutions. J Inflamm Res 2024; 17:4611-4623. [PMID: 39011419 PMCID: PMC11249114 DOI: 10.2147/jir.s463701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 06/22/2024] [Indexed: 07/17/2024] Open
Abstract
Objective This study aimed to identify the risk factors and construct a reliable prediction model of 28-day all-cause mortality in critically ill patients with acute pancreatitis (AP) using machine learning techniques. Methods A total of 534 patients from three different institutions were included. Thirty-eight possible variables were collected from the Intensive care unit (ICU) admission for investigation. Patients were split into a training cohort (n = 400) and test cohort (n = 134) according to their source of hospital. The synthetic minority oversampling technique (SMOTE) was introduced to handle the inherent class imbalance. Six machine learning algorithms were applied in this study. The optimal machine learning model was chosen after patients in the test cohort were selected to validate the models. SHapley Additive exPlanation (SHAP) analysis was performed to rank the importance of variable. The predictive performance of the models was evaluated by the calibration curve, area under the receiver operating characteristics curves (AUROC), and decision clinical analysis. Results About 13.5% (72/534) of all patients eventually died of all-cause within 28 days of ICU admission. Eight important variables were screened out, including white blood cell count, platelets, body temperature, age, blood urea nitrogen, red blood cell distribution width, SpO2, and hemoglobin. The support vector machine (SVM) algorithm performed best in predicting 28-d all-cause death. Its AUROC reached 0.877 (95% CI: 0.809 to 0.927, p < 0.001), the Youden index was 0.634 (95% CI: 0.459 to 0.717). Based on the risk stratification system, the difference between the high-risk and low-risk groups was significantly different. Conclusion In conclusion, this study developed and validated SVM model, which better predicted 28-d all-cause mortality in critically ill patients with AP. In the future, we will continue to include patients from more institutions to conduct validation in different contexts and countries.
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Affiliation(s)
- Weimin Cai
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Xiao Wu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Yongxian Chen
- Department of Respiratory, Xiamen Second hospital, Xiamen, People’s Republic of China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, People’s Republic of China
| | - Xinran Lin
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
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Tóth B, Berek L, Gulácsi L, Péntek M, Zrubka Z. Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed. Syst Rev 2024; 13:174. [PMID: 38978132 PMCID: PMC11229257 DOI: 10.1186/s13643-024-02592-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice. METHODS In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies. RESULTS From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs. CONCLUSIONS Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.
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Affiliation(s)
- Barbara Tóth
- Doctoral School of Innovation Management, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - László Berek
- Doctoral School for Safety and Security, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
- University Library, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - László Gulácsi
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - Márta Péntek
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - Zsombor Zrubka
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary.
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Yang TH, Liao ZY, Yu YH, Hsia M. RDDL: A systematic ensemble pipeline tool that streamlines balancing training schemes to reduce the effects of data imbalance in rare-disease-related deep-learning applications. Comput Biol Chem 2023; 106:107929. [PMID: 37517206 DOI: 10.1016/j.compbiolchem.2023.107929] [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: 12/07/2022] [Revised: 04/19/2023] [Accepted: 07/14/2023] [Indexed: 08/01/2023]
Abstract
Identifying lowly prevalent diseases, or rare diseases, in their early stages is key to disease treatment in the medical field. Deep learning techniques now provide promising tools for this purpose. Nevertheless, the low prevalence of rare diseases entangles the proper application of deep networks for disease identification due to the severe class-imbalance issue. In the past decades, some balancing methods have been studied to handle the data-imbalance issue. The bad news is that it is verified that none of these methods guarantees superior performance to others. This performance variation causes the need to formulate a systematic pipeline with a comprehensive software tool for enhancing deep-learning applications in rare disease identification. We reviewed the existing balancing schemes and summarized a systematic deep ensemble pipeline with a constructed tool called RDDL for handling the data imbalance issue. Through two real case studies, we showed that rare disease identification could be boosted with this systematic RDDL pipeline tool by lessening the data imbalance problem during model training. The RDDL pipeline tool is available at https://github.com/cobisLab/RDDL/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan; Medical Device Innovation Center, National Cheng Kung University, Tainan City 701, Taiwan.
| | - Zhan-Yi Liao
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Yu-Huai Yu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Min Hsia
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
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Oliveira Dos Santos Á, Sergio da Silva E, Machado Couto L, Valadares Labanca Reis G, Silva Belo V. The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review. J Biomed Inform 2023; 142:104389. [PMID: 37187321 DOI: 10.1016/j.jbi.2023.104389] [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: 02/06/2023] [Revised: 04/11/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceeds the capacity of human-only analysis. In this context, artificial intelligence (AI) and its branches such as machine learning (ML) can be used to facilitate human efforts in analyzing the literature to foster EBM. The present scoping review aimed to examine the use of AI in the automation of biomedical literature survey and analysis with a view to establishing the state-of-the-art and identifying knowledge gaps. MATERIALS AND METHODS Comprehensive searches of the main databases were performed for articles published up to June 2022 and studies were selected according to inclusion and exclusion criteria. Data were extracted from the included articles and the findings categorized. RESULTS The total number of records retrieved from the databases was 12,145, of which 273 were included in the review. Classification of the studies according to the use of AI in evaluating the biomedical literature revealed three main application groups, namely assembly of scientific evidence (n=127; 47%), mining the biomedical literature (n=112; 41%) and quality analysis (n=34; 12%). Most studies addressed the preparation of systematic reviews, while articles focusing on the development of guidelines and evidence synthesis were the least frequent. The biggest knowledge gap was identified within the quality analysis group, particularly regarding methods and tools that assess the strength of recommendation and consistency of evidence. CONCLUSION Our review shows that, despite significant progress in the automation of biomedical literature surveys and analyses in recent years, intense research is needed to fill knowledge gaps on more difficult aspects of ML, deep learning and natural language processing, and to consolidate the use of automation by end-users (biomedical researchers and healthcare professionals).
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Affiliation(s)
| | - Eduardo Sergio da Silva
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
| | - Letícia Machado Couto
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
| | | | - Vinícius Silva Belo
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
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Lokker C, Bagheri E, Abdelkader W, Parrish R, Afzal M, Navarro T, Cotoi C, Germini F, Linkins L, Brian Haynes R, Chu L, Iorio A. Deep Learning to Refine the Identification of High-Quality Clinical Research Articles from the Biomedical Literature: Performance Evaluation. J Biomed Inform 2023; 142:104384. [PMID: 37164244 DOI: 10.1016/j.jbi.2023.104384] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence. OBJECTIVE To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice. METHODS We fine-tuned variations of BERT models (BERTBASE, BioBERT, BlueBERT, and PubMedBERT) and compared their performance in classifying articles based on methodological quality criteria. The dataset used for fine-tuning models included titles and abstracts of >160,000 PubMed records from 2012-2020 that were of interest to human health which had been manually labeled based on meeting established critical appraisal criteria for methodological rigor. The data was randomly divided into 80:10:10 sets for training, validating, and testing. In addition to using the full unbalanced set, the training data was randomly undersampled into four balanced datasets to assess performance and select the best performing model. For each of the four sets, one model that maintained sensitivity (recall) at ≥99% was selected and were ensembled. The best performing model was evaluated in a prospective, blinded test and applied to an established reference standard, the Clinical Hedges dataset. RESULTS In training, three of the four selected best performing models were trained using BioBERTBASE. The ensembled model did not boost performance compared with the best individual model. Hence a solo BioBERT-based model (named DL-PLUS) was selected for further testing as it was computationally more efficient. The model had high recall (>99%) and 60% to 77% specificity in a prospective evaluation conducted with blinded research associates and saved >60% of the work required to identify high quality articles. CONCLUSIONS Deep learning using pretrained language models and a large dataset of classified articles produced models with improved specificity while maintaining >99% recall. The resulting DL-PLUS model identifies high-quality, clinically relevant articles from PubMed at the time of publication. The model improves the efficiency of a literature surveillance program, which allows for faster dissemination of appraised research.
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Affiliation(s)
- Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
| | - Elham Bagheri
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Wael Abdelkader
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Rick Parrish
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Muhammad Afzal
- Department of Computing, Birmingham City University, Birmingham, UK
| | - Tamara Navarro
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Chris Cotoi
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Federico Germini
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Lori Linkins
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - R Brian Haynes
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Lingyang Chu
- Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada
| | - Alfonso Iorio
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada
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Wang X, Ren H, Ren J, Song W, Qiao Y, Ren Z, Zhao Y, Linghu L, Cui Y, Zhao Z, Chen L, Qiu L. Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107340. [PMID: 36640604 DOI: 10.1016/j.cmpb.2023.107340] [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: 04/09/2022] [Revised: 11/25/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.
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Affiliation(s)
- Xuchun Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Hao Ren
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Jiahui Ren
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Wenzhu Song
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Yuchao Qiao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Zeping Ren
- Shanxi Centre for Disease Control and Prevention, Taiyuan, Shanxi 030012, China
| | - Ying Zhao
- Shanxi Centre for Disease Control and Prevention, Taiyuan, Shanxi 030012, China
| | - Liqin Linghu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China; Shanxi Centre for Disease Control and Prevention, Taiyuan, Shanxi 030012, China
| | - Yu Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Zhiyang Zhao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China
| | - Limin Chen
- The Fifth Hospital (Shanxi People's Hospital) of Shanxi Medical University, No. 29, Shuangtaji Street, Taiyuan, Shanxi 030012, China.
| | - Lixia Qiu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi 030001, China.
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A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105959. [PMID: 35627495 PMCID: PMC9141951 DOI: 10.3390/ijerph19105959] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.
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A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3183469. [PMID: 35469205 PMCID: PMC9034946 DOI: 10.1155/2022/3183469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/21/2022] [Indexed: 11/17/2022]
Abstract
Text classification is widely studied by researchers in the natural language processing field. However, real-world text data often follow a long-tailed distribution as the frequency of each class is typically different. The performance of current mainstream learning algorithms in text classification suffers when the training data are highly imbalanced. The problem can get worse when the categories with fewer data are severely undersampled to the extent that the variation within each category is not fully captured by the given data. At present, there are a few studies on long-tailed text classification which put forward effective solutions. Encouraged by the progress of handling long-tailed data in the field of image, we try to integrate effective ideas into the field of long-tailed text classification and prove the effectiveness. In this paper, we come up with a novel approach of feature space reconstruction with the help of three-way decisions (3WDs) for long-tailed text classification. In detail, we verify the rationality of using a 3WD model for feature selection in long-tailed text data classification, propose a new feature space reconstruction method for long-tailed text data for the first time, and demonstrate how to effectively generate new samples for tail classes in reconstructed feature space. By adding new samples, we enrich the representing information of tail classes, to improve the classification results of long-tailed text classification. After some comparative experiments, we have verified that our model is an effective strategy to improve the performance of long-tailed text classification.
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Zhang Y, Razbek J, Li D, Yang L, Bao L, Xia W, Mao H, Daken M, Zhang X, Cao M. Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models. BMC Public Health 2022; 22:251. [PMID: 35135534 PMCID: PMC8822755 DOI: 10.1186/s12889-022-12617-y] [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: 06/08/2021] [Accepted: 01/17/2022] [Indexed: 12/03/2022] Open
Abstract
Background We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS. Methods This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model. Results Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise. Conclusions Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.
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Affiliation(s)
- Yan Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jaina Razbek
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Deyang Li
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lei Yang
- Xinjiang De Kang Ci Hui Health Services Group, Urumqi, Xinjiang, China
| | - Liangliang Bao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wenjun Xia
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hongkai Mao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mayisha Daken
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoxu Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mingqin Cao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
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Lu H, Li F, Yuan L, Domenzain I, Yu R, Wang H, Li G, Chen Y, Ji B, Kerkhoven EJ, Nielsen J. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Mol Syst Biol 2021; 17:e10427. [PMID: 34676984 PMCID: PMC8532513 DOI: 10.15252/msb.202110427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/02/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.
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Affiliation(s)
- Hongzhong Lu
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Feiran Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Le Yuan
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Iván Domenzain
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Rosemary Yu
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Hao Wang
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- National Bioinformatics Infrastructure SwedenScience for Life LaboratoryChalmers University of TechnologyGothenburgSweden
| | - Gang Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Yu Chen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Boyang Ji
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
| | - Eduard J Kerkhoven
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
- The Novo Nordisk Foundation Center for BiosustainabilityTechnical University of DenmarkLyngbyDenmark
- BioInnovation InstituteCopenhagen NDenmark
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11
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Abdelkader W, Navarro T, Parrish R, Cotoi C, Germini F, Iorio A, Haynes RB, Lokker C. Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review. JMIR Med Inform 2021; 9:e30401. [PMID: 34499041 PMCID: PMC8461527 DOI: 10.2196/30401] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/15/2021] [Accepted: 07/25/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE The goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.
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Affiliation(s)
- Wael Abdelkader
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Tamara Navarro
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Rick Parrish
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Chris Cotoi
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Federico Germini
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Alfonso Iorio
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - R Brian Haynes
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
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12
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Wang L, Zhao Z, Luo Y, Yu H, Wu S, Ren X, Zheng C, Huang X. Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105567. [PMID: 32544778 DOI: 10.1016/j.cmpb.2020.105567] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/18/2020] [Accepted: 05/21/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Treatments are limited for patients with relapsed/refractory Diffuse large B-cell lymphoma (DLBCL), and their survival rate is low. Prediction of the recurrence hazard for each patient could provide a reference regarding chemotherapy regimens for clinicians to extend patients' period of long-term remission. As current strategies cannot satisfy such need, we have established predictive models to classify patients with DLBCL with complete remission who had recurrences in 2 years from ones who did not. METHODS We assessed 518 patients with DLBCL and measured 52 variables of each patient. They were treated between January 2011 and July 2016. 17 variables were first selected by variable selection methods (including Lasso, Adaptive Lasso, and Elastic net). Then, we set classifiers and probability models for imbalanced data by combining the SMOTE sampling, cost-sensitive, and ensemble learning (consisting of AdaBoost, voting strategy, and Stacking) methods with the machine learning methods (Support Vector Machine, BackPropagation Artificial Neural Network, Random Forest), respectively. Last, assessed their performance. RESULTS The disease stage and other 5 variables are significant indicators for recurrence. The SVM with AdaBoost ensemble learning method modeling by SMOTE data performs the best (Sensitivity=97.3%, AUC=96%, RMSE=19.6%, G-mean=96%) in all classifiers. The SVM with AdaBoost method(AUC=98.7%, RMSE=17.7%, MXE=12.7%, Cal mean=3.2%, BS0=2.5%, BS1=4%, BSALL=3.1%) and random forest (AUC=99.5%, RMSE=19.8%, MXE=16.2%, Cal mean=9.1%, BS0=4.8%, BS1=2.9%, BSALL=3.9%) both modeling by SMOTE sampling data perform well in probability models. CONCLUSIONS This predictive model has high accuracy for almost all DLBCL patients and the six indicators can be recurrence signals.
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Affiliation(s)
- Lei Wang
- Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.
| | - ZhiQiang Zhao
- Hematology department of Shanxi cancer hospital, China.
| | - YanHong Luo
- Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.
| | - HongMei Yu
- Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.
| | - ShuQing Wu
- Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.
| | - XiaoLu Ren
- Radiology department of Shanxi cancer hospital, China.
| | - ChuChu Zheng
- Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.
| | - XueQian Huang
- Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.
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