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Matthias J, Khan AM, Craze K, Karki S, Newman DR. Evaluation of Automated Processing of Electronically Reported Serological Tests for Syphilis Using Current and Historical Syphilis Results Compared With Traditional Reactor Grid Processing in Florida. Sex Transm Dis 2024; 51:420-424. [PMID: 38372524 PMCID: PMC11088518 DOI: 10.1097/olq.0000000000001952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
BACKGROUND Syphilis in Florida increased 49% from 2016 to 2020. Moreover, many serological tests for syphilis (STS) do not indicate current infection. Traditionally, syphilis surveillance systems used reactor grids, a method for prioritizing STS for investigation based on age, nontreponemal titer, and/or sex. In 2022, Florida's sexually transmitted disease surveillance system implemented an automated method for processing electronically reported STS (eSTS), expanding upon the reactor grid, using an individual's current STS (treponemal and nontreponemal), treatment history, and historical STS results aiming for more efficiently processing eSTS. We compared the new method of processing eSTS results against the reactor grid and determined potential value in time/cost savings of this change. METHODS All eSTSs (n = 4144) from January 2, 2023 to January 8, 2023, were compared by how the logic-based method processed test results versus how the reactor grid processed test results. Each method was compared using measurements of accuracy (e.g., sensitivity/specificity). Time and cost savings in eSTS processing were estimated. RESULTS Using the surveillance case definition as reference, the accuracy of the logic-based method for processing eSTS was nearly double (82.3% vs. 43.6%), had greater specificity (79.0% vs. 33.0%), and increased positive predictive value (47.5% vs. 22.0%) when compared with the reactor grid method. Sensitivity (99.5% vs. 98.6%) and negative predictive value (99.9% vs. 99.2%) remained similar. The logic-based method is estimated to save 7783 hours annually (~$185,000). CONCLUSIONS Processing eSTS based on current and historical STS results is significantly more accurate than using a reactor grid. Moreover, these improvements save time and resources that can be better allocated to other program prevention activities.
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Affiliation(s)
- James Matthias
- Centers for Disease Control and Prevention, Division of STD Prevention, Atlanta, GA
- Florida Department of Health, Division of Disease Control and Prevention, Tallahassee, FL
| | - Amir M. Khan
- Florida Department of Health, Division of Disease Control and Prevention, Tallahassee, FL
| | - Kristin Craze
- Florida Department of Health, Division of Disease Control and Prevention, Tallahassee, FL
| | - Saugat Karki
- Centers for Disease Control and Prevention, Division of STD Prevention, Atlanta, GA
| | - Daniel R. Newman
- Centers for Disease Control and Prevention, Division of STD Prevention, Atlanta, GA
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Althomsons SP, Winglee K, Heilig CM, Talarico S, Silk B, Wortham J, Hill AN, Navin TR. Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters. Am J Epidemiol 2022; 191:1936-1943. [PMID: 35780450 PMCID: PMC10790200 DOI: 10.1093/aje/kwac117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 05/05/2022] [Accepted: 06/28/2022] [Indexed: 02/01/2023] Open
Abstract
The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009-2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.
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Affiliation(s)
- Sandy P. Althomsons
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Kathryn Winglee
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Charles M. Heilig
- Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Sarah Talarico
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Benjamin Silk
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Jonathan Wortham
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Andrew N. Hill
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
| | - Thomas R. Navin
- Division of TB Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States
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Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39:120. [PMID: 35704152 PMCID: PMC9198206 DOI: 10.1007/s12032-022-01711-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 10/28/2022]
Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020; 2020:baaa010. [PMID: 32185396 PMCID: PMC7078068 DOI: 10.1093/database/baaa010] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.
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Affiliation(s)
- Zeeshan Ahmed
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, 67 North Eagleville Road, Storrs, CT, USA
| | - Khalid Mohamed
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
| | - Saman Zeeshan
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - XinQi Dong
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
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Abstract
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.
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Affiliation(s)
- Stephen J Mooney
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington 98122, USA;
| | - Vikas Pejaver
- Department of Biomedical Informatics and Medical Education and the eScience Institute, University of Washington, Seattle, Washington 98109, USA;
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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications. SUSTAINABILITY 2017. [DOI: 10.3390/su9122309] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Paydar K, Niakan Kalhori SR, Akbarian M, Sheikhtaheri A. A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. Int J Med Inform 2016; 97:239-246. [PMID: 27919382 DOI: 10.1016/j.ijmedinf.2016.10.018] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 10/14/2016] [Accepted: 10/29/2016] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Pregnancy among systemic lupus erythematosus (SLE)-affected women is highly associated with poor obstetric outcomes. Predicting the risk of foetal outcome is essential for maximizing the success of pregnancy. This study aimed to develop a clinical decision support system (CDSS) to predict pregnancy outcomes among SLE-affected pregnant women. METHODS We performed a retrospective analysis of 149 pregnant women with SLE, who were followed at Shariati Hospital (104 pregnancies) and a specialized clinic (45 pregnancies) from 1982 to 2014. We selected significant features (p<0.10) using a binary logistic regression model performed in IBM SPSS (version 20). Afterward, we trained several artificial neural networks (multi-layer perceptron [MLP] and radial basis function [RBF]) to predict the pregnancy outcome. In order to evaluate and select the most effective network, we used the confusion matrix and the receiver operating characteristic (ROC) curve. We finally developed a CDSS based on the most accurate network. MATLAB 2013b software was applied to design the neural networks and develop the CDSS. RESULTS Initially, 45 potential variables were analysed by the binary logistic regression and 16 effective features were selected as the inputs of neural networks (P-value<0.1). The accuracy (90.9%), sensitivity (80.0%), and specificity (94.1%) of the test data for the MLP network were achieved. These measures for the RBF network were 71.4%, 53.3%, and 79.4%, respectively. Having applied a 10-fold cross-validation method, the accuracy for the networks showed 75.16% accuracy for RBF and 90.6% accuracy for MLP. Therefore, the MLP network was selected as the most accurate network for prediction of pregnancy outcome. CONCLUSION The developed CDSS based on the MLP network can help physicians to predict pregnancy outcomes in women with SLE.
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Affiliation(s)
- Khadijeh Paydar
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran.
| | - Sharareh R Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran.
| | - Mahmoud Akbarian
- Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran.
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran.
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