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Guan Y, Yin X, Wang L, Diao Z, Huang H, Wang X. Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis. Diabetes Metab Syndr Obes 2024; 17:3399-3418. [PMID: 39290792 PMCID: PMC11407315 DOI: 10.2147/dmso.s472412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 09/11/2024] [Indexed: 09/19/2024] Open
Abstract
Background Diabetic nephropathy (DN) is a severe complication of diabetes influenced by arginine methylation. This study aimed to elucidate the role of protein arginine methylation-related genes (PRMT-RGs) in DN and identify potential biomarkers. Methods Differentially expressed genes in two GEO datasets (GSE30122 and GSE104954) were integrated with 9 PRMT-RGs. Candidate genes were identified using WGCNA and differential expression analysis, then screened using support vector machine-recursive feature elimination and least absolute shrinkage and selection operator. Biomarkers were defined as genes with consistent differential expression across both datasets. Regulatory networks were constructed using the miRNet and Network Analyst databases. Gene set enrichment analysis was performed to identify the signaling pathways in which the biomarkers were enriched in DN. Different immune cells in DN were identified using immune infiltration analysis. Meanwhile, drug prediction and molecular docking identified potential DN therapies. Finally, qRT-PCR and immunohistochemistry validated two biomarkers in STZ-induced DN mice and DN patients. Results Two biomarkers (FAM98A and FAM13B) of DN were identified in this study. The molecular regulatory network revealed that FAM98A and FAM13B were co-regulated by 6 microRNAs and 1 transcription factor and were enriched in signaling pathways. Immune infiltration and correlation analyses revealed that FAM98A and FAM13B were involved in developing DN along with PRMT-RGs and immune cells. The expression levels of Fam98a and Fam13b were significantly upregulated in the kidneys of DN mice revealed by qRT-PCR analysis. The expression levels of FAM98A were significantly upregulated in the kidneys of DN patients revealed by immunohistochemistry staining. Molecular docking showed that estradiol and rotenone exerted potential therapeutic effects on DN by targeting FAM98A. Conclusion Comprehensive bioinformatics analysis revealed that FAM98A and FAM13B were potential DN biomarkers correlated with PRMT-RGs and immune cells. This study provided useful insights for elucidating the molecular mechanisms and developing targeted therapy for DN.
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Affiliation(s)
- Yiming Guan
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xiayan Yin
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Liyan Wang
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zongli Diao
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hongdong Huang
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xueqi Wang
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
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Gallardo-Pizarro A, Peyrony O, Chumbita M, Monzo-Gallo P, Aiello TF, Teijon-Lumbreras C, Gras E, Mensa J, Soriano A, Garcia-Vidal C. Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning. Expert Rev Anti Infect Ther 2024; 22:179-187. [PMID: 38457198 DOI: 10.1080/14787210.2024.2322445] [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: 07/26/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine. AREAS COVERED In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality. EXPERT OPINION There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.
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Affiliation(s)
| | - Olivier Peyrony
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Mariana Chumbita
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | | | | | | | - Emmanuelle Gras
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Josep Mensa
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Alex Soriano
- Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain
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Okita J, Nakata T, Uchida H, Kudo A, Fukuda A, Ueno T, Tanigawa M, Sato N, Shibata H. Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease. BMC Nephrol 2024; 25:101. [PMID: 38493099 PMCID: PMC10943785 DOI: 10.1186/s12882-024-03527-9] [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: 12/13/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Predicting time to renal replacement therapy (RRT) is important in patients at high risk for end-stage kidney disease. We developed and validated machine learning models for predicting the time to RRT and compared its accuracy with conventional prediction methods that uses the rate of estimated glomerular filtration rate (eGFR) decline. METHODS Data of adult chronic kidney disease (CKD) patients who underwent hemodialysis at Oita University Hospital from April 2016 to March 2021 were extracted from electronic medical records (N = 135). A new machine learning predictor was compared with the established prediction method that uses the eGFR decline rate and the accuracy of the prediction models was determined using the coefficient of determination (R2). The data were preprocessed and split into training and validation datasets. We created multiple machine learning models using the training data and evaluated their accuracy using validation data. Furthermore, we predicted the time to RRT using a conventional prediction method that uses the eGFR decline rate for patients who had measured eGFR three or more times in two years and evaluated its accuracy. RESULTS The least absolute shrinkage and selection operator regression model exhibited moderate accuracy with an R2 of 0.60. By contrast, the conventional prediction method was found to be extremely low with an R2 of -17.1. CONCLUSIONS The significance of this study is that it shows that machine learning can predict time to RRT moderately well with continuous values from data at a single time point. This approach outperforms the conventional prediction method that uses eGFR time series data and presents new avenues for CKD treatment.
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Affiliation(s)
- Jun Okita
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Takeshi Nakata
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan.
| | - Hiroki Uchida
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Akiko Kudo
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Akihiro Fukuda
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
| | - Tamio Ueno
- Department of Medical Technology and Sciences, School of Health Sciences at Fukuoka, International University of Health and Welfare, Okawa, Japan
| | - Masato Tanigawa
- Department of Biophysics, Faculty of Medicine, Oita University, Oita, Japan
| | - Noboru Sato
- Department of Healthcare AI Data Science, Faculty of Medicine, Oita University, Oita, Japan
| | - Hirotaka Shibata
- Department of Endocrinology, Metabolism, Rheumatology and Nephrology, Faculty of Medicine, Oita University, 8795593, 1-1 idaigaoka Hasama-cho, Yufu-shi, Oita-ken, Japan
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Green BL, Murphy A, Robinson E. Accelerating health disparities research with artificial intelligence. Front Digit Health 2024; 6:1330160. [PMID: 38322109 PMCID: PMC10844447 DOI: 10.3389/fdgth.2024.1330160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Affiliation(s)
- B. Lee Green
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Anastasia Murphy
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Edmondo Robinson
- Center for Digital Health, Moffitt Cancer Center, Tampa, FL, United States
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Huang AA, Huang SY. Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives. Cureus 2023; 15:e46549. [PMID: 37933338 PMCID: PMC10625496 DOI: 10.7759/cureus.46549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.
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Affiliation(s)
- Alexander A Huang
- Surgery, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Samuel Y Huang
- Internal Medicine, Icahn School of Medicine at Mount Sinai South Nassau, Oceanside, USA
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Nwanosike EM, Sunter W, Ansari MA, Merchant HA, Conway B, Hasan SS. A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches. Am J Cardiovasc Drugs 2023; 23:287-299. [PMID: 36872389 DOI: 10.1007/s40256-023-00569-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/27/2022] [Indexed: 03/07/2023]
Abstract
INTRODUCTION The clinical outcomes of direct oral anticoagulant (DOAC) dosage regimens in morbid obesity are uncertain due to limited clinical evidence. This study seeks to bridge this evidence gap by identifying the factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. METHOD A data-driven observational study was carried out using supervised machine learning (ML) models with a dataset extracted from electronic health records and preprocessed. Following 70%:30% partitioning of the overall dataset via stratified sampling, the selected ML classifiers (e.g., random forest, decision trees, bootstrap aggregation) were applied to the training dataset (70%). The outcomes of the models were evaluated against the test dataset (30%). Multivariate regression analysis explored the association between DOAC regimens and clinical outcomes. RESULTS A sample of 4,275 morbidly obese patients was extracted and analysed. The decision trees, random forest, and bootstrap aggregation classifiers achieved acceptable (excellent) values of precision, recall, and F1 scores in terms of their contribution to clinical outcomes. The length of stay, treatment days, and age were ranked highest for relevance to mortality and stroke. Among DOAC regimens, apixaban 2.5 mg twice daily ranked highest for its association with mortality, increasing the mortality risk by 43% (odds ratio [OR] 1.430, 95% confidence interval [CI] 1.181-1.732, p = 0.001). On the other hand, apixaban 5 mg twice daily reduced the odds of mortality by 25% (OR 0.751, 95% CI 0.632-0.905, p = 0.003) but increased the odds of stroke events. No clinically relevant non-major bleeding events occurred in this group. CONCLUSION Data-driven approaches can identify key factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. This will help design further studies to explore well tolerated and effective DOAC doses for morbidly obese patients.
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Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK
| | - Wendy Sunter
- Anticoagulant Services, Calderdale and Huddersfield NHS Foundation Trust Hospital, Lindley, HD3 3EA, Huddersfield, UK
| | - Muhammad Ayub Ansari
- School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, West Yorkshire, UK
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK
| | - Barbara Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK.
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Luckscheiter A, Zink W, Lohs T, Eisenberger J, Thiel M, Viergutz T. Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial. Clin Exp Emerg Med 2022; 9:304-313. [PMID: 36418016 PMCID: PMC9834832 DOI: 10.15441/ceem.22.335] [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: 06/16/2022] [Accepted: 10/16/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The aim of this study was to determine the feasibility of using machine learning to establish the need for preclinical airway management for injured patients based on a standardized emergency dataset. METHODS A registry-based, retrospective analysis was conducted of adult trauma patients who were treated by physician-staffed emergency medical services in southwestern Germany between 2018 and 2020. The primary outcome was to assess the feasibility of using the random forest (RF) and Naive Bayes (NB) machine learning algorithms to predict the need for preclinical airway management. The secondary outcome was to use a principal component analysis to determine the attributes that can be used and advanced for future model development. RESULTS In total, 25,556 adults with multiple injuries were identified, including 1,451 patients (5.7%) who required airway management. Key attributes were auscultation, injury pattern, oxygen therapy, thoracic drainage, noninvasive ventilation, catecholamines, pelvic sling, colloid infusion, initial vital signs, preemergency status, and shock index. The area under the receiver operating characteristics curve was between 0.96 (RF; 95% confidence interval [CI], 0.96-0.97) and 0.93 (NB; 95% CI, 0.92-0.93; P<0.01). For the prediction of airway management, RF yielded a higher precision-recall area than NB (0.83 [95% CI, 0.8-0.85] vs. 0.66 [95% CI, 0.61-0.72], respectively; P<0.01). CONCLUSION To predict the need for preclinical airway management in injured patients, attributes that are commonly recorded in standardized datasets can be used with machine learning. In future models, the RF algorithm could be used because it has robust prediction accuracy.
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Affiliation(s)
- André Luckscheiter
- Department of Anesthesiology, Intensive Care and Emergency Medicine, Ludwigshafen Municipal Hospital, Ludwigshafen, Germany,Correspondence to: André Luckscheiter Department of Anesthesiology, Intensive Care and Emergency Medicine, Ludwigshafen Municipal Hospital, Bremserstrasse 79, Ludwigshafen 67063, Germany E-mail:
| | - Wolfgang Zink
- Department of Anesthesiology, Intensive Care and Emergency Medicine, Ludwigshafen Municipal Hospital, Ludwigshafen, Germany
| | - Torsten Lohs
- Center for Quality Management in Emergency Medical Services Baden-Wuerttemberg (SQR-BW), Stuttgart, Germany
| | - Johanna Eisenberger
- Center for Quality Management in Emergency Medical Services Baden-Wuerttemberg (SQR-BW), Stuttgart, Germany
| | - Manfred Thiel
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center Mannheim, Mannheim, Germany
| | - Tim Viergutz
- Clinic for Anesthesia, Intensive Care and Pain Therapy, BG Trauma Center Tuebingen, Tuebingen, Germany
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Glinton SL, Calcagni A, Lilaonitkul W, Pontikos N, Vermeirsch S, Zhang G, Arno G, Wagner SK, Michaelides M, Keane PA, Webster AR, Mahroo OA, Robson AG. Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography. Transl Vis Sci Technol 2022; 11:34. [PMID: 36178783 PMCID: PMC9527330 DOI: 10.1167/tvst.11.9.34] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Purpose Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype-phenotype correlations. Methods International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. Results Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. Conclusions This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.
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Affiliation(s)
- Sophie L. Glinton
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Antonio Calcagni
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Watjana Lilaonitkul
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK (HDRUK), London, UK
| | - Nikolas Pontikos
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | | | - Gongyu Zhang
- Institute of Ophthalmology, University College London, London, UK
| | - Gavin Arno
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Siegfried K. Wagner
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Michel Michaelides
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Pearse A. Keane
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Andrew R. Webster
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Omar A. Mahroo
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
| | - Anthony G. Robson
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, London, UK
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Soares GH, Sethi S, Hedges J, Jamieson L. Disparities in Human Papillomavirus vaccination coverage among adolescents in Australia: A geospatial analysis. Vaccine 2022; 40:4644-4653. [PMID: 35750540 DOI: 10.1016/j.vaccine.2022.06.030] [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: 03/30/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 11/29/2022]
Abstract
AIM This ecological study aimed to examine the geographic patterns in Human Papillomavirus (HPV) vaccination rates among boys and girls aged 15 years across locations in Australia, in addition to assessing contextual area-level factors that may explain the variations in HPV vaccination coverage. METHODS Aggregate HPV vaccination data for Australian girls and boys aged 15 years from 2015 to 16 was obtained from the Australian Institute of Health and Welfare for each Statistical Area level 4 (SA4). A Gradient Boosting Machine learning model was applied to assess the predictors' importance for the study outcomes. Geographically weighted regression (GWR) models were run to assess whether substantially different relationships between predictors and outcomes occur at different locations in space. RESULTS Completed HPV vaccination across the 88 SA4 regions ranged from 57.6% to 90.6% among girls, and from 53.6% to 85.5% among boys. The 2016 SEIFA Index of Economic Resources was the variable with the highest contribution to the predictions of both girls' and boys' HPV vaccination rates. Selected predictors explained 45% and 72% of the geographic variance in vaccination rates among boys and girls, respectively. Normalised coefficients for both GWR models showed a high variation in the associations between predictors and HPV vaccination rates across regions. CONCLUSION Socioeconomic and education factors were important predictors for HPV vaccination rates among Australian boys and girls aged 15 years, although no variable presented a uniform effect on HPV vaccination across SA4 regions. Important spatial heterogeneity in the effect of predictors was identified across the study area.
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Affiliation(s)
- Gustavo Hermes Soares
- Australian Research Centre for Population Oral Health, The University of Adelaide, Adelaide, SA, Australia.
| | - Sneha Sethi
- Australian Research Centre for Population Oral Health, The University of Adelaide, Adelaide, SA, Australia.
| | - Joanne Hedges
- Australian Research Centre for Population Oral Health, The University of Adelaide, Adelaide, SA, Australia.
| | - Lisa Jamieson
- Australian Research Centre for Population Oral Health, The University of Adelaide, Adelaide, SA, Australia.
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Basu S, Johnson KT, Berkowitz SA. Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes. Curr Diab Rep 2020; 20:80. [PMID: 33270183 DOI: 10.1007/s11892-020-01353-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/26/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW Machine learning approaches-which seek to predict outcomes or classify patient features by recognizing patterns in large datasets-are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020. RECENT FINDINGS Machine learning approaches using tree-based learners-which produce decision trees to help guide clinical interventions-frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and "deep learning" can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process. Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.
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Affiliation(s)
- Sanjay Basu
- Center for Primary Care, Harvard Medical School, Boston, MA, USA.
- Research and Population Health, Collective Health, San Francisco, CA, USA.
- School of Public Health, Imperial College London, London, SW7, UK.
| | - Karl T Johnson
- General Medicine and Clinical Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Seth A Berkowitz
- General Medicine and Clinical Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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