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Mahesh N, Devishamani CS, Raghu K, Mahalingam M, Bysani P, Chakravarthy AV, Raman R. Advancing healthcare: the role and impact of AI and foundation models. Am J Transl Res 2024; 16:2166-2179. [PMID: 39006256 PMCID: PMC11236664 DOI: 10.62347/wqwv9220] [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: 01/07/2024] [Accepted: 05/06/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems. PURPOSE This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes. RESULTS This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models. CONCLUSION The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.
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Affiliation(s)
- Nandhini Mahesh
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Chitralekha S Devishamani
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Keerthana Raghu
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Maanasi Mahalingam
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Pragathi Bysani
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | | | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
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Yang YH, Wang TT, Su YH, Chu WY, Lin WT, Chen YJ, Chang YS, Lin YC, Lin CH, Lin YJ. Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning. Sci Rep 2024; 14:10833. [PMID: 38734835 PMCID: PMC11088707 DOI: 10.1038/s41598-024-61749-1] [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: 01/05/2024] [Accepted: 05/09/2024] [Indexed: 05/13/2024] Open
Abstract
Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (VLBW) preterm infants in Taiwan. We collected retrospective data from VLBW infants, dividing them into two cohorts: one for model development and internal validation (Cohort 1, 2016-2021), and another for external validation (Cohort 2, 2022). Primary outcomes included early mortality, severe IVH, and early poor outcomes (a combination of both). Data preprocessing involved 23 variables, with the top four predictors identified as gestational age, birth body weight, 5-min Apgar score, and endotracheal tube ventilation. Six machine learning algorithms were employed. Among 7471 infants analyzed, the selected predictors consistently performed well across all outcomes. Logistic regression and neural network models showed the highest predictive performance (AUC 0.81-0.90 in both internal and external validation) and were well-calibrated, confirmed by calibration plots and the lowest two mean Brier scores (0.0685 and 0.0691). We developed a robust machine learning-based outcome predictor using only four accessible variables, offering valuable prognostic information for parents and aiding healthcare providers in decision-making.
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Affiliation(s)
- Yun-Hsiang Yang
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
| | - Ts-Ting Wang
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
- Department of Pediatrics, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi, Taiwan
| | - Yi-Han Su
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
| | - Wei-Ying Chu
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
| | - Wei-Ting Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
| | - Yen-Ju Chen
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
| | - Yu-Shan Chang
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Yung-Chieh Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
| | - Chyi-Her Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan
- Department of Pediatrics, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Yuh-Jyh Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No.138, Sheng Li Road, Tainan, Taiwan.
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Ungkulpasvich U, Hatakeyama H, Hirotsu T, di Luccio E. Pancreatic Cancer and Detection Methods. Biomedicines 2023; 11:2557. [PMID: 37760999 PMCID: PMC10526344 DOI: 10.3390/biomedicines11092557] [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: 08/21/2023] [Revised: 09/05/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
The pancreas is a vital organ with exocrine and endocrine functions. Pancreatitis is an inflammation of the pancreas caused by alcohol consumption and gallstones. This condition can heighten the risk of pancreatic cancer (PC), a challenging disease with a high mortality rate. Genetic and epigenetic factors contribute significantly to PC development, along with other risk factors. Early detection is crucial for improving PC outcomes. Diagnostic methods, including imagining modalities and tissue biopsy, aid in the detection and analysis of PC. In contrast, liquid biopsy (LB) shows promise in early tumor detection by assessing biomarkers in bodily fluids. Understanding the function of the pancreas, associated diseases, risk factors, and available diagnostic methods is essential for effective management and early PC detection. The current clinical examination of PC is challenging due to its asymptomatic early stages and limitations of highly precise diagnostics. Screening is recommended for high-risk populations and individuals with potential benign tumors. Among various PC screening methods, the N-NOSE plus pancreas test stands out with its high AUC of 0.865. Compared to other commercial products, the N-NOSE plus pancreas test offers a cost-effective solution for early detection. However, additional diagnostic tests are required for confirmation. Further research, validation, and the development of non-invasive screening methods and standardized scoring systems are crucial to enhance PC detection and improve patient outcomes. This review outlines the context of pancreatic cancer and the challenges for early detection.
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Affiliation(s)
| | | | | | - Eric di Luccio
- Hirotsu Bioscience Inc., 22F The New Otani Garden Court, 4-1 Kioi-cho, Chiyoda-ku, Tokyo 102-0094, Japan; (U.U.); (H.H.); (T.H.)
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Liu J, Glied S, Yakusheva O, Bevin C, Schlak AE, Yoon S, Kulage KM, Poghosyan L. Using machine-learning methods to predict in-hospital mortality through the Elixhauser index: A Medicare data analysis. Res Nurs Health 2023; 46:411-424. [PMID: 37221452 PMCID: PMC10330510 DOI: 10.1002/nur.22322] [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: 12/07/2022] [Revised: 04/21/2023] [Accepted: 05/10/2023] [Indexed: 05/25/2023]
Abstract
Accurate in-hospital mortality prediction can reflect the prognosis of patients, help guide allocation of clinical resources, and help clinicians make the right care decisions. There are limitations to using traditional logistic regression models when assessing the model performance of comorbidity measures to predict in-hospital mortality. Meanwhile, the use of novel machine-learning methods is growing rapidly. In 2021, the Agency for Healthcare Research and Quality published new guidelines for using the Present-on-Admission (POA) indicator from the International Classification of Diseases, Tenth Revision, for coding comorbidities to predict in-hospital mortality from the Elixhauser's comorbidity measurement method. We compared the model performance of logistic regression, elastic net model, and artificial neural network (ANN) to predict in-hospital mortality from Elixhauser's measures under the updated POA guidelines. In this retrospective analysis, 1,810,106 adult Medicare inpatient admissions from six US states admitted after September 23, 2017, and discharged before April 11, 2019 were extracted from the Centers for Medicare and Medicaid Services data warehouse. The POA indicator was used to distinguish pre-existing comorbidities from complications that occurred during hospitalization. All models performed well (C-statistics >0.77). Elastic net method generated a parsimonious model, in which there were five fewer comorbidities selected to predict in-hospital mortality with similar predictive power compared to the logistic regression model. ANN had the highest C-statistics compared to the other two models (0.800 vs. 0.791 and 0.791). Elastic net model and AAN can be applied successfully to predict in-hospital mortality.
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Affiliation(s)
- Jianfang Liu
- Columbia University School of Nursing, New York City, New York, USA
| | - Sherry Glied
- Robert F. Wagner Graduate School of Public Service, New York University, New York City, New York, USA
| | - Olga Yakusheva
- University of Michigan School of Nursing, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Cohen Bevin
- Mount Sinai Health System, New York City, New York, USA
| | - Amelia E Schlak
- AAAS Science and Technology Policy Fellow, Office of Research and Development, U.S. Department of Veteran Affairs, Washington, DC, USA
| | - Sunmoo Yoon
- Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, USA
| | - Kristine M Kulage
- Office of Scholarship and Research Development, Columbia University School of Nursing, New York City, New York, USA
| | - Lusine Poghosyan
- Columbia University School of Nursing and Professor of Health Policy and Management, Mailman School of Public Health, Columbia University, Executive Director Center for Healthcare Delivery Research & Innovations (HDRI), New York City, New York, USA
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Sedigh A, Townsend C, Khawam SM, Vaccaro AR, Carreras BN, Beredjiklian PK, Rivlin M. Remote fit wrist braces through artificial intelligence. Prosthet Orthot Int 2023; 47:434-439. [PMID: 37068013 DOI: 10.1097/pxr.0000000000000233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/18/2023] [Indexed: 04/18/2023]
Abstract
INTRODUCTION Physical boundaries to access skilled orthotist or hand therapy care may be hindered by multiple factors, such as geography, or availability. This study evaluated the accuracy of fitting a prefabricated wrist splint using an app on a smart device. We hypothesize that remote brace fitting by artificial intelligence (AI) can accurately determine the brace size the patient needs without in-person fitting. METHODS Healthy volunteers were recruited to fit wrist braces. Using 2 standardized calibrated images captured by the smart device, each subject's image was loaded into the machine learning software (AI). Later, hand features were extracted, calibrated, and measured the application, calculated the correct splint size, and compared with the splint chosen by our subjects to improve its own accuracy. As a control (control 1), the subjects independently selected the best brace fit from an array of available splints. Subject selection was recorded and compared with the AI fit splint. As the second method of fitting (control 2), we compared the manufacturer recommended brace size (based on measured wrist circumference and provided sizing chart/insert brochure) with the AI fit splint. RESULTS A total of 54 volunteers were included. Thirty-two splints predicted by the algorithm matched the exact size chosen by each subject yielding 70% accuracy with a standard deviation of 10% ( p < 0.001). The accuracy increased to 90% with 5% standard deviation if the splints were predicted within the next size category. Fit by manufacturer sizing chart was only 33% in agreement with participant selection. CONCLUSION Remote brace fitting using AI prediction model may be an acceptable alternative to current standards because it can accurately predict wrist splint size. As more subjects were analyzed, the AI algorithm became more accurate predicting proper brace fit. In addition, AI fit braces are more than twice as accurate as relying on the manufacturer sizing chart.
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Affiliation(s)
| | | | - Sultan M Khawam
- Rowan University School of Osteopathic Medicine, Stratford, NJ
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Zha Y, Xue C, Liu Y, Ni J, De La Fuente JM, Cui D. Artificial intelligence in theranostics of gastric cancer, a review. MEDICAL REVIEW (2021) 2023; 3:214-229. [PMID: 37789960 PMCID: PMC10542883 DOI: 10.1515/mr-2022-0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/26/2023] [Indexed: 10/05/2023]
Abstract
Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.
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Affiliation(s)
- Yiqian Zha
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | - Cuili Xue
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | - Yanlei Liu
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | - Jian Ni
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
| | | | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center for Nanotechnology, Shanghai, China
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Resmi SL, Hashim V, Mohammed J, Dileep PN. Bone Mineral Density Prediction from CT Image: A Novel Approach using ANN. Appl Bionics Biomech 2023; 2023:1123953. [PMID: 37153753 PMCID: PMC10162883 DOI: 10.1155/2023/1123953] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 02/25/2023] [Accepted: 03/25/2023] [Indexed: 05/10/2023] Open
Abstract
Background Though treatable, osteoporosis continues as a substantially underdiagnosed and undertreated condition. Bone mineral density (BMD) monitoring will definitely aid in the prediction and prevention of medical emergencies arising from osteoporosis. Although quantitative computed tomography (QCT) is one of the most widely accepted tools for measuring BMD, it lacks the contribution of bone architecture in predicting BMD, which is significant as aging progresses. This paper presents an innovative approach for the prediction of BMD incorporating bone architecture that involves no extra cost, time, and exposure to severe radiation. Methods In this approach, the BMD is predicted using clinical CT scan images taken for other indications based on image processing and artificial neural network (ANN). The network used in this study is a standard backpropagation neural network having five input neurons with one hidden layer having 40 neurons with a tan-sigmoidal activation function. The Digital Imaging and Communications in Medicine (DICOM) image properties extracted from QCT of human skull and femur bone of rabbit that are closely associated with the BMD are used as input parameters of the ANN. The density value of the bone which is computed from the Hounsfield units of QCT scan image through phantom calibration is used as the target value for training the network. Results The ANN model predicts the density values using the image properties from the clinical CT of the same rabbit femur bone and is compared with the density value computed from QCT scan. The correlation coefficient between predicted BMD and QCT density valued to 0.883. The proposed network can assist clinicians in identifying early stage of osteoporosis and devise suitable strategies to improve BMD with no additional cost.
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Affiliation(s)
- S. L. Resmi
- Department of Mechanical Engineering, TKM College of Engineering, Kollam, Kerala, India
| | - V. Hashim
- Department of Mechanical Engineering, TKM College of Engineering, Kollam, Kerala, India
| | - Jesna Mohammed
- Department of Mechanical Engineering, TKM College of Engineering, Kollam, Kerala, India
| | - P. N. Dileep
- Department of Mechanical Engineering, TKM College of Engineering, Kollam, Kerala, India
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Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review. Ann Emerg Med 2022; 80:440-455. [PMID: 35842343 DOI: 10.1016/j.annemergmed.2022.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/20/2022] [Accepted: 05/04/2022] [Indexed: 11/23/2022]
Abstract
STUDY OBJECTIVE Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research. METHODS We conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers. RESULTS Of the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed. CONCLUSION This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.
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Vohra R, Hussain A, Dudyala AK, Pahareeya J, Khan W. Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting. PLoS One 2022; 17:e0269685. [PMID: 35793343 PMCID: PMC9258850 DOI: 10.1371/journal.pone.0269685] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 05/26/2022] [Indexed: 11/19/2022] Open
Abstract
Anemia is one of the most pressing public health issues in the world with iron deficiency a major public health issue worldwide. The highest prevalence of anemia is in developing countries. The complete blood count is a blood test used to diagnose the prevalence of anemia. While earlier studies have framed the problem of diagnosis as a binary classification problem, this paper frames it as a multi class (three classes) classification problem with mild, moderate and severe classes. The three classes for the anemia classification (mild, moderate, severe) are so chosen as the world health organization (WHO) guidelines formalize this categorization based on the Haemoglobin (HGB) values of the chosen sample of patients in the Complete Blood Count (CBC) patient data set. Complete blood count test data was collected in an outpatient clinical setting in India. We used Feature selection with Majority voting to identify the key attributes in the input patient data set. In addition, since the original data set was imbalanced we used Synthetic Minority Oversampling Technique (SMOTE) to balance the data set. Four data sets including the original data set were used to perform the data experiments. Six standard machine learning algorithms were utilised to test our four data sets, performing multi class classification. Benchmarking these algorithms was performed and tabulated using both10 fold cross validation and hold out methods. The experimental results indicated that multilayer perceptron network was predominantly giving good recall values across mild and moderate class which are early and middle stages of the disease. With a good prediction model at early stages, medical intervention can provide preventive measure from further deterioration into severe stage or recommend the use of supplements to overcome this problem.
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Affiliation(s)
- Rajan Vohra
- School of Creative Technologies, University of Bolton, Bolton, United Kingdom
- * E-mail:
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah, UAE
- Department of Computer Science, Liverpool John Moore’s University, Liverpool, United Kingdom
| | - Anil Kumar Dudyala
- Department of Computer Science, National Institute of Technology Patna (NIT Patna), Patna, India
| | - Jankisharan Pahareeya
- Department of Information Technology, Rustamji Institute of Technology, BSF Academy, Tekanpur, Gwalior, India
| | - Wasiq Khan
- Department of Computer Science, Liverpool John Moore’s University, Liverpool, United Kingdom
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A. SK, Kumar A, Bajaj V, Singh G. A compact fuzzy min max network with novel trimming strategy for pattern classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Gajendran MK, Rohowetz LJ, Koulen P, Mehdizadeh A. Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma. Front Neurosci 2022; 16:869137. [PMID: 35600610 PMCID: PMC9115110 DOI: 10.3389/fnins.2022.869137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/28/2022] [Indexed: 01/05/2023] Open
Abstract
PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.ConclusionsThe present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.
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Affiliation(s)
- Mohan Kumar Gajendran
- Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Landon J. Rohowetz
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Peter Koulen
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
- Department of Biomedical Sciences, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Amirfarhang Mehdizadeh
- Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
- *Correspondence: Amirfarhang Mehdizadeh
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Mfateneza E, Rutayisire PC, Biracyaza E, Musafiri S, Mpabuka WG. Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset. BMC Pregnancy Childbirth 2022; 22:388. [PMID: 35509018 PMCID: PMC9066935 DOI: 10.1186/s12884-022-04699-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 04/18/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Extensive research on infant mortality (IM) exists in developing countries; however, most of the methods applied thus far relied on conventional regression analyses with limited prediction capability. Advanced of Machine Learning (AML) methods provide accurate prediction of IM; however, there is no study conducted using ML methods in Rwanda. This study, therefore, applied Machine Learning Methods for predicting infant mortality in Rwanda. METHODS: A cross-sectional study design was conducted using the 2014-15 Rwanda Demographic and Health Survey. Python software version 3.8 was employed to test and apply ML methods through Random Forest (RF), Decision Tree, Support Vector Machine and Logistic regression. STATA version 13 was used for analysing conventional methods. Evaluation metrics methods specifically confusion matrix, accuracy, precision, recall, F1 score, and Area under the Receiver Operating Characteristics (AUROC) were used to evaluate the performance of predictive models. RESULTS Ability of prediction was between 68.6% and 61.5% for AML. We preferred with the RF model (61.5%) presenting the best performance. The RF model was the best predictive model of IM with accuracy (84.3%), recall (91.3%), precision (80.3%), F1 score (85.5%), and AUROC (84.2%); followed by decision tree model with model accuracy (83%), recall (91%), precision (79%), F1 score (84.67%) and AUROC(82.9%), followed by support vector machine with model accuracy (68.6%), recall (74.9%), precision(67%), F1 score (70.73%) and AUROC (68.6%) and last was a logistic regression with the low accuracy of prediction (61.5%), recall (61.1%), precision (62.2%), F1 score (61.6%) and AUROC (61.5%) compared to other predictive models. Our predictive models showed that marital status, children ever born, birth order and wealth index are the 4 top predictors of IM. CONCLUSIONS In developing a predictive model, ML methods are used to classify certain hidden information that could not be detected by traditional statistical methods. Random Forest was classified as the best classifier to be used for the predictive models of IM.
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Affiliation(s)
- Emmanuel Mfateneza
- African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
| | | | | | - Sanctus Musafiri
- Clinical Department of Internal Medicine, University of Rwanda, Kigali, Rwanda
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Alrafiah AR. Application and performance of artificial intelligence technology in cytopathology. Acta Histochem 2022; 124:151890. [PMID: 35366580 DOI: 10.1016/j.acthis.2022.151890] [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: 01/18/2022] [Revised: 03/17/2022] [Accepted: 03/24/2022] [Indexed: 11/27/2022]
Abstract
Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent advancements in computer vision and machine learning. The first use of AI in a pathology lab was in cytopathology, when a computer-assisted Pap test screening was created. Initially designed to diagnose rather than screen, there was a lot of disagreement concerning their wide use to clinical specimens. However, whole-slide imaging of both gynaecological and non-gynaecological histopathology have been the subject of recent AI work. An overview of the literature on AI in cytopathology is provided in this brief review. To be more precise, it intends to emphasize the relevance of applications of AI algorithms to gynaecological and non-gynaecologic cytology. Between January 2000 and December 2021, a search on artificial intelligence in cytopathology was conducted in several well-known databases, including PubMed, Web of Science, Scopus, Embase, and Google Scholar. Only full-text papers that could be accessed online were evaluated.
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B T B, Kapoor S, Chen JM. Estimating vocal tract geometry from acoustic impedance using deep neural network. JASA EXPRESS LETTERS 2022; 2:034801. [PMID: 36154632 DOI: 10.1121/10.0009599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
A data-driven approach using artificial neural networks is proposed to address the classic inverse area function problem, i.e., to determine the vocal tract geometry (modelled as a tube of nonuniform cylindrical cross-sections) from the vocal tract acoustic impedance spectrum. The predicted cylindrical radii and the actual radii were found to have high correlation in the three- and four-cylinder model (Pearson coefficient (ρ) and Lin concordance coefficient (ρc) exceeded 95%); however, for the six-cylinder model, the correlation was low (ρ around 75% and ρc around 69%). Upon standardizing the impedance value, the correlation improved significantly for all cases (ρ and ρc exceeded 90%).
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Affiliation(s)
- Balamurali B T
- Singapore University of Technology and Design, Singapore , ,
| | - Saumitra Kapoor
- Singapore University of Technology and Design, Singapore , ,
| | - Jer-Ming Chen
- Singapore University of Technology and Design, Singapore , ,
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15
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Lee KC, Lin TC, Chiang HF, Horng GJ, Hsu CC, Wu NC, Su HC, Chen KT. Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning. Medicine (Baltimore) 2021; 100:e27753. [PMID: 34889225 PMCID: PMC8663914 DOI: 10.1097/md.0000000000027753] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 10/27/2021] [Indexed: 01/05/2023] Open
Abstract
In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early prognostic model based on admission features and initial ED management.We only recruited patients with severe trauma (defined as an injury severity score >15) as the study cohort and excluded children (defined as patients <16 years old) from a 4-years database (Chi-Mei Medical Center, from January 2015, to December 2018) recording the clinical features of all admitted trauma patients. We considered only patient features that could be determined within the first 2 hours after arrival to the ED. These variables included Glasgow Coma Scale (GCS) score; heart rate; respiratory rate; mean arterial pressure (MAP); prehospital cardiac arrest; abbreviated injury scales (AIS) of head and neck, thorax, and abdomen; and ED interventions (tracheal intubation/tracheostomy, blood product transfusion, thoracostomy, and cardiopulmonary resuscitation). The endpoint for prognostic analyses was mortality within 7 days of admission.We divided the study cohort into the early death group (149 patients who died within 7 days of admission) and non-early death group (2083 patients who survived at >7 days of admission). The extreme Gradient Boosting (XGBoost) machine learning model provided mortality prediction with higher accuracy (94.0%), higher sensitivity (98.0%), moderate specificity (54.8%), higher positive predict value (PPV) (95.4%), and moderate negative predictive value (NPV) (74.2%).We developed a machine learning-based prognostic model that showed high accuracy, high sensitivity, and high PPV for predicting the mortality of patients with severe trauma.
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Affiliation(s)
- Kuo-Chang Lee
- Emergency Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Tzu-Chieh Lin
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Hsiu-Fen Chiang
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Gwo-Jiun Horng
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Chien-Chin Hsu
- Emergency Department, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Biotechnology, Southern Tainan University of Technology, Tainan, Taiwan
| | - Nan-Chun Wu
- Division of Traumatology, Department of Surgery, Chi-Mei Medical Center, Tainan, Taiwan
| | - Hsiu-Chen Su
- Division of Traumatology, Department of Surgery, Chi-Mei Medical Center, Tainan, Taiwan
| | - Kuo-Tai Chen
- Emergency Department, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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16
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Azuri I, Rosenhek-Goldian I, Regev-Rudzki N, Fantner G, Cohen SR. The role of convolutional neural networks in scanning probe microscopy: a review. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2021; 12:878-901. [PMID: 34476169 PMCID: PMC8372315 DOI: 10.3762/bjnano.12.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/23/2021] [Indexed: 05/13/2023]
Abstract
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
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Affiliation(s)
- Ido Azuri
- Weizmann Institute of Science, Department of Life Sciences Core Facilities, Rehovot 76100, Israel
| | - Irit Rosenhek-Goldian
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
| | - Neta Regev-Rudzki
- Weizmann Institute of Science, Department of Biomolecular Sciences, Rehovot 76100, Israel
| | - Georg Fantner
- École Polytechnique Fédérale de Lausanne, Laboratory for Bio- and Nano-Instrumentation, CH1015 Lausanne, Switzerland
| | - Sidney R Cohen
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
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17
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Chiu WT, Chung CC, Huang CH, Chien YS, Hsu CH, Wu CH, Wang CH, Chiu HW, Chan L. Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks. J Formos Med Assoc 2021; 121:490-499. [PMID: 34330620 DOI: 10.1016/j.jfma.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 05/12/2021] [Accepted: 07/05/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN). METHODS The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively. CONCLUSIONS The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.
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Affiliation(s)
- Wei-Ting Chiu
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taiwan; Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Chen-Chih Chung
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Chien-Hua Huang
- Department of Emergency Medicine, National Taiwan University Medical College and Hospital, Taipei, Taiwan; Cardiovascular Division, Department of Internal Medicine, National Taiwan University College of Medicine and Hospital, Taiwan
| | - Yu-San Chien
- Department of Critical Care Medicine, MacKay Memorial Hospital, Taipei Branch, Taiwan
| | - Chih-Hsin Hsu
- Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital Dou Liou Branch, College of Medicine, National Cheng Kung University, Taiwan
| | - Cheng-Hsueh Wu
- Department of Critical Care Medicine, Taipei Veterans General Hospital, National Yang-Ming University, Taipei, Taiwan
| | - Chen-Hsu Wang
- Attending Physician, Coronary Care Unit, Cardiovascular Center, Cathay General Hospital, Taipei, Taiwan
| | - Hung-Wen Chiu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan
| | - Lung Chan
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taiwan.
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18
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Modeling and Predicting the Cell Migration Properties from Scratch Wound Healing Assay on Cisplatin-Resistant Ovarian Cancer Cell Lines Using Artificial Neural Network. Healthcare (Basel) 2021; 9:healthcare9070911. [PMID: 34356289 PMCID: PMC8305856 DOI: 10.3390/healthcare9070911] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/14/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
The study of artificial neural networks (ANN) has undergone a tremendous revolution in recent years, boosted by deep learning tools. The presence of a greater number of learning tools and their applications, in particular, favors this revolution. However, there is a significant need to deal with the issue of implementing a systematic method during the development phase of the ANN to increase its performance. A multilayer feedforward neural network (FNN) was proposed in this paper to predict the cell migration assay on cisplatin-sensitive and cisplatin-resistant (CisR) ovarian cancer (OC) cell lines via scratch wound healing assay. An FNN training algorithm model was generated using the MATLAB fitting function in a MATLAB script to accomplish this task. The input parameters were types of cell lines, times, and wound area, and outputs were relative wound area, percentage of wound closure, and wound healing speed. In addition, we tested and compared the initial accuracy of various supervised learning classifier and support vector regression (SVR) algorithms. The proposed ANN model achieved good agreement with the experimental data and minimized error between the estimated and experimental values. The conclusions drawn demonstrate that the developed ANN model is a useful, accurate, fast, and inexpensive method to predict cancerous cell migration characteristics evaluated via scratch wound healing assay.
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19
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Myrovali E, Fragakis N, Vassilikos V, Hadjileontiadis LJ. Efficient syncope prediction from resting state clinical data using wavelet bispectrum and multilayer perceptron neural network. Med Biol Eng Comput 2021; 59:1311-1324. [PMID: 33959855 DOI: 10.1007/s11517-021-02353-7] [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: 06/24/2020] [Accepted: 03/19/2021] [Indexed: 11/28/2022]
Abstract
Neurally mediated syncope (NMS) is the most common type of syncope, and head up tilt test (HUTT) is, so far, the most appropriate tool to identify NMS. In this work, an effort to predict the NMS before performing the HUTT is attempted. To achieve this, the heart rate variability (HRV) at rest and during the first minutes of tilting position during HUTT was analyzed using both time and frequency domains. Various features from HRV regularity and complexity, along with wavelet higher-order spectrum (WHOS) analysis in low-frequency (LF) and high-frequency (HF) bands were examined. The experimental results from 26 patients with history of NMS have shown that at rest, a time domain entropy measure and WHOS-based features in LF band exhibit significant differences between positive and negative HUTT as well as among 10 healthy subjects and NMS patients. The best performance of multilayer perceptron neural network (MPNN) was achieved by using an input vector consisted of WHOS-based HRV features in the LF zone and systolic blood pressure from the resting period, yielding an accuracy of 89.7%, assessed by 5-fold cross-validation. The promising results presented here pave the way for an early prediction of the HUTT outcome from resting state, contributing to the identification of patients at higher risk NMS. The HRV analysis along with systolic blood pressure at rest predict NMS using a multilayer perceptron neural network.
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Affiliation(s)
- Evangelia Myrovali
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 54645, Thessaloniki, Greece.
| | - Nikolaos Fragakis
- 3rd Cardiology Department, Aristotle University of Thessaloniki, Hippokration General Hospital, 49 Konstantinoupoleos str, 54642, Thessaloniki, Greece
| | - Vassilios Vassilikos
- 3rd Cardiology Department, Aristotle University of Thessaloniki, Hippokration General Hospital, 49 Konstantinoupoleos str, 54642, Thessaloniki, Greece
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 54645, Thessaloniki, Greece.,Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, PO BOX 127788, Abu Dhabi, UAE
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20
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Yu CS, Chang SS, Lin CH, Lin YJ, Wu JL, Chen RJ. Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach. Front Med (Lausanne) 2021; 8:626580. [PMID: 33898478 PMCID: PMC8058220 DOI: 10.3389/fmed.2021.626580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 03/08/2021] [Indexed: 12/16/2022] Open
Abstract
Introduction: A third of the world's population is classified as having Metabolic Syndrome (MetS). Traditional diagnostic criteria for MetS are based on three or more of five components. However, the outcomes of patients with different combinations of specific metabolic components are undefined. It is challenging to be discovered and introduce treatment in advance for intervention, since the related research is still insufficient. Methods: This retrospective cohort study attempted to establish a method of visualizing metabolic components by using unsupervised machine learning and treemap technology to discover the relations between predicting factors and different metabolic components. Several supervised machine-learning models were used to explore significant predictors of MetS and to construct a powerful prediction model for preventive medicine. Results: The random forest had the best performance with accuracy and c-statistic of 0.947 and 0.921, respectively, and found that body mass index, glycated hemoglobin, and controlled attenuation parameter (CAP) score were the optimal primary predictors of MetS. In treemap, high triglyceride level plus high fasting blood glucose or large waist circumference group had higher CAP scores (>260) than other groups. Moreover, 32.2% of patients with high CAP scores during 3 years of follow-up had metabolic diseases are observed. This reveals that the CAP score may be used for detecting MetS, especially for the non-obese MetS phenotype. Conclusions: Machine learning and data visualization can illustrate the complicated relationships between metabolic components and potential risk factors for MetS.
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Affiliation(s)
- Cheng-Sheng Yu
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Shy-Shin Chang
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chang-Hsien Lin
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Jiun Lin
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jenny L Wu
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ray-Jade Chen
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of General Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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21
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Zhang W, Li H, Li Y, Liu H, Chen Y, Ding X. Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09967-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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22
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Yang J, Zhao Z, Du C, Wang W, Peng Q, Qiu J, Wang G. The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis. Expert Rev Med Devices 2020; 17:1311-1322. [PMID: 33252284 DOI: 10.1080/17434440.2020.1852930] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Introduction: Although there is a need for rehabilitation treatment with the increase in the aging population, the shortage of skilled physicians frustrates this necessity. Robotic technology has been advocated as one of the most viable methods with the potential to replace humans in providing physical rehabilitation of patients with neurological impairment. However, because the pioneering robot devices suffer several reservations such as safety and comfort concerns in clinical practice, there is an urgent need to provide upgraded replacements. The rapid development of intelligent computing has attracted the attention of researchers concerning the utilization of computational intelligence algorithms for robots in rehabilitation. Areas covered: This article reviews the state of the art and advances of robotic neurorehabilitation with computational intelligence. We classified advances into two categories: mechanical structures and control methods. Prospective outlooks of rehabilitation robots also have been discussed. Expert opinion: The aggravation of global aging has promoted the application of robotic technology in neurorehabilitation. However, this approach is not mature enough to guarantee the safety of patients. Our critical review summarizes multiple computation algorithms which have been proved to be valuable for better robotic use in clinical settings and guide the possible future advances in this industry.
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Affiliation(s)
- Jiali Yang
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China
| | - Zhiqi Zhao
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China
| | - Chenzhen Du
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China
| | - Wei Wang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital , Chongqing, China
| | - Qin Peng
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen, China
| | - Juhui Qiu
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China
| | - Guixue Wang
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China
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23
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Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions. J Thyroid Res 2020; 2020:5464787. [PMID: 33299540 PMCID: PMC7707952 DOI: 10.1155/2020/5464787] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 07/17/2020] [Accepted: 10/24/2020] [Indexed: 01/21/2023] Open
Abstract
Objective This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.
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24
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Sana MK, Hussain ZM, Shah PA, Maqsood MH. Artificial intelligence in celiac disease. Comput Biol Med 2020; 125:103996. [PMID: 32979542 DOI: 10.1016/j.compbiomed.2020.103996] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/01/2020] [Accepted: 09/07/2020] [Indexed: 12/14/2022]
Abstract
Celiac disease (CD) has been on the rise in the world and a large part of it remains undiagnosed. Novel methods are required to address the gaps in prompt detection and management. Artificial intelligence (AI) has seen an exponential surge in the last decade worldwide. With the advent of big data and powerful computational ability, we now have self-driving cars and smart devices in our daily lives. Huge databases in the form of electronic medical records and images have rendered healthcare a lucrative sector where AI can prove revolutionary. It is being used extensively to overcome the barriers in clinical workflows. From the perspective of a disease, it can be deployed in multiple steps i.e. screening tools, diagnosis, developing novel therapeutic agents, proposing management plans, and defining prognostic indicators, etc. We review the areas where it may augment physicians in the delivery of better healthcare by summarizing current literature on the use of AI in healthcare using CD as a model. We further outline major barriers to its large-scale implementations and prospects from the healthcare point of view.
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Affiliation(s)
- Muhammad Khawar Sana
- Department of Internal Medicine, King Edward Medical University, Mayo Hospital Lahore, Lahore, Punjab, 54000, Pakistan.
| | - Zeshan M Hussain
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, United States.
| | - Pir Ahmad Shah
- Department of Internal Medicine, University of Texas Health Science Center, San Antonio, TX, 78229, United States.
| | - Muhammad Haisum Maqsood
- Department of Internal Medicine, King Edward Medical University, Mayo Hospital Lahore, Lahore, Punjab, 54000, Pakistan.
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25
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Hou J, Nast CC. Artificial Intelligence: The Next Frontier in Kidney Biopsy Evaluation. Clin J Am Soc Nephrol 2020; 15:1389-1391. [PMID: 32938618 PMCID: PMC7536757 DOI: 10.2215/cjn.13450820] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jean Hou
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Cynthia C Nast
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California
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26
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Davagdorj K, Pham VH, Theera-Umpon N, Ryu KH. XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17186513. [PMID: 32906777 PMCID: PMC7558165 DOI: 10.3390/ijerph17186513] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/28/2020] [Accepted: 09/05/2020] [Indexed: 12/23/2022]
Abstract
Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of developing SiNCDs. However, determining the most significant features and developing interpretable models are rather challenging in such systems. In this study, we propose an efficient extreme gradient boosting (XGBoost) based framework incorporated with the hybrid feature selection (HFS) method for SiNCDs prediction among the general population in South Korea and the United States. Initially, HFS is performed in three stages: (I) significant features are selected by t-test and chi-square test; (II) multicollinearity analysis serves to obtain dissimilar features; (III) final selection of best representative features is done based on least absolute shrinkage and selection operator (LASSO). Then, selected features are fed into the XGBoost predictive model. The experimental results show that our proposed model outperforms several existing baseline models. In addition, the proposed model also provides important features in order to enhance the interpretability of the SiNCDs prediction model. Consequently, the XGBoost based framework is expected to contribute for early diagnosis and prevention of the SiNCDs in public health concerns.
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Affiliation(s)
- Khishigsuren Davagdorj
- Database and Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea;
| | - Van Huy Pham
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam;
| | - Nipon Theera-Umpon
- Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand;
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Keun Ho Ryu
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam;
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
- Correspondence: ; Tel.: +82-10-4930-1500
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Structure equation model and neural network analyses to predict coronary artery lesions in Kawasaki disease: a single-centre retrospective study. Sci Rep 2020; 10:11868. [PMID: 32681105 PMCID: PMC7368009 DOI: 10.1038/s41598-020-68657-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 06/28/2020] [Indexed: 12/17/2022] Open
Abstract
A new method to predict coronary artery lesions (CALs) in Kawasaki disease (KD) was developed using a mean structure equation model (SEM) and neural networks (Nnet). There were 314 admitted children with KD who met at least four of the six diagnostic criteria for KD. We defined CALs as the presence of a maximum z score of ≥ 3.0. The SEM using age, sex, intravenous immunoglobulin resistance, number of steroid pulse therapy sessions, C-reactive protein level, and urinary β2-microglobulin (u-β2MG/Cr) values revealed a perfect fit based on the root mean square error of approximation with an R2 value of 1.000 and the excellent discrimination of CALs with a sample score (SS) of 2.0 for a latent variable. The Nnet analysis enabled us to predict CALs with a sensitivity, specificity and c-index of 73%, 99% and 0.86, respectively. This good and simple statistical model that uses common parameters in clinical medicine is useful in deciding the appropriate therapy to prevent CALs in Japanese KD patients.
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Farhadian M, Shokouhi P, Torkzaban P. A decision support system based on support vector machine for diagnosis of periodontal disease. BMC Res Notes 2020; 13:337. [PMID: 32660549 PMCID: PMC7359226 DOI: 10.1186/s13104-020-05180-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 07/08/2020] [Indexed: 01/22/2023] Open
Abstract
Objective Early diagnosis of many diseases is essential for their treatment. Furthermore, the existence of abundant and unknown variables makes more complicated decision making. For this reason, the diagnosis and classification of diseases using machine learning algorithms have attracted a lot of attention. Therefore, this study aimed to design a support vector machine (SVM) based decision-making support system to diagnosis various periodontal diseases. Data were collected from 300 patients referring to Periodontics department of Hamadan University of Medical Sciences, west of Iran. Among these patients, 160 were Gingivitis, 60 were localized periodontitis and 80 were generalized periodontitis. In the designed classification model, 11 variables such as age, sex, smoking, gingival index, plaque index and so on used as input and output variable show the individual’s status as a periodontal disease. Results Using different kernel functions in the design of the SVM classification model showed that the radial kernel function with an overall correct classification accuracy of 88.7% and the overall hypervolume under the manifold (HUM) value was to 0.912 has the best performance. The results of the present study show that the designed classification model has an acceptable performance in predicting periodontitis.
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Affiliation(s)
- Maryam Farhadian
- Department of Biostatistics, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Parisa Shokouhi
- Dental School, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Parviz Torkzaban
- Department of Periodontics, Dental School, Dental Research Center, Hamadan University of Medical Sciences, P.O. Box 4171-65175, Hamadan, Iran.
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An artificial neural network approach to predict energy consumption and surface roughness of a natural material. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2987-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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30
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A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17103703. [PMID: 32456329 PMCID: PMC7277622 DOI: 10.3390/ijerph17103703] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/16/2020] [Accepted: 05/22/2020] [Indexed: 12/12/2022]
Abstract
Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals.
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Moon S, Ahmadnezhad P, Song HJ, Thompson J, Kipp K, Akinwuntan AE, Devos H. Artificial neural networks in neurorehabilitation: A scoping review. NeuroRehabilitation 2020; 46:259-269. [DOI: 10.3233/nre-192996] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Sanghee Moon
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA
| | - Pedram Ahmadnezhad
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA
| | - Hyun-Je Song
- Department of Information Technology, Jeonbuk National University, Jeonju, South Korea
| | - Jeffrey Thompson
- Department of Biostatistics, School of Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Kristof Kipp
- Department of Physical Therapy, College of Health Sciences, Marquette University, Milwaukee, WI, USA
| | - Abiodun E. Akinwuntan
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA
- Office of the Dean, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA
| | - Hannes Devos
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA
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32
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A Comparative Analysis of Machine Learning Methods for Class Imbalance in a Smoking Cessation Intervention. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10093307] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Smoking is one of the major public health issues, which has a significant impact on premature death. In recent years, numerous decision support systems have been developed to deal with smoking cessation based on machine learning methods. However, the inevitable class imbalance is considered a major challenge in deploying such systems. In this paper, we study an empirical comparison of machine learning techniques to deal with the class imbalance problem in the prediction of smoking cessation intervention among the Korean population. For the class imbalance problem, the objective of this paper is to improve the prediction performance based on the utilization of synthetic oversampling techniques, which we called the synthetic minority over-sampling technique (SMOTE) and an adaptive synthetic (ADASYN). This has been achieved by the experimental design, which comprises three components. First, the selection of the best representative features is performed in two phases: the lasso method and multicollinearity analysis. Second, generate the newly balanced data utilizing SMOTE and ADASYN technique. Third, machine learning classifiers are applied to construct the prediction models among all subjects and each gender. In order to justify the effectiveness of the prediction models, the f-score, type I error, type II error, balanced accuracy and geometric mean indices are used. Comprehensive analysis demonstrates that Gradient Boosting Trees (GBT), Random Forest (RF) and multilayer perceptron neural network (MLP) classifiers achieved the best performances in all subjects and each gender when SMOTE and ADASYN were utilized. The SMOTE with GBT and RF models also provide feature importance scores that enhance the interpretability of the decision-support system. In addition, it is proven that the presented synthetic oversampling techniques with machine learning models outperformed baseline models in smoking cessation prediction.
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33
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Lyapunov stability-Dynamic Back Propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems. Soft comput 2020. [DOI: 10.1007/s00500-019-04496-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Yu CS, Lin YJ, Lin CH, Wang ST, Lin SY, Lin SH, Wu JL, Chang SS. Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study. JMIR Med Inform 2020; 8:e17110. [PMID: 32202504 PMCID: PMC7136841 DOI: 10.2196/17110] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/07/2020] [Accepted: 03/05/2020] [Indexed: 12/18/2022] Open
Abstract
Background Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. Objective We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. Methods Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. Results Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. Conclusions Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.
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Affiliation(s)
- Cheng-Sheng Yu
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Jiun Lin
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chang-Hsien Lin
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Sen-Te Wang
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Shiyng-Yu Lin
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Sanders H Lin
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Jenny L Wu
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Shy-Shin Chang
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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35
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Ben-Israel D, Jacobs WB, Casha S, Lang S, Ryu WHA, de Lotbiniere-Bassett M, Cadotte DW. The impact of machine learning on patient care: A systematic review. Artif Intell Med 2020; 103:101785. [DOI: 10.1016/j.artmed.2019.101785] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 12/11/2019] [Accepted: 12/30/2019] [Indexed: 01/04/2023]
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36
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Walczak S, Velanovich V. Prediction of perioperative transfusions using an artificial neural network. PLoS One 2020; 15:e0229450. [PMID: 32092108 PMCID: PMC7039514 DOI: 10.1371/journal.pone.0229450] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 02/06/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations. METHODS Over 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients. RESULTS Various ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity. CONCLUSION ANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.
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Affiliation(s)
- Steven Walczak
- School of Information, Florida Center for Cybersecurity, University of South Florida, Tampa, FL, United States of America
| | - Vic Velanovich
- Department of Surgery, Morsani College of Medicine, University of South Florida, Tampa, FL, United States of America
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37
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Otsuka R, Nomura Y, Okada A, Uematsu H, Nakano M, Hikiji K, Hanada N, Momoi Y. Properties of manual toothbrush that influence on plaque removal of interproximal surface in vitro. J Dent Sci 2019; 15:14-21. [PMID: 32256995 PMCID: PMC7109512 DOI: 10.1016/j.jds.2019.07.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 07/14/2019] [Indexed: 11/17/2022] Open
Abstract
Background/purpose Few papers were available on the interproximal cleaning efficiency by manual toothbrushes when used alone. The aim was to investigate the efficiency of commercially available toothbrushes on interproximal cleaning and determine the key properties that would make the differences. Materials and methods Artificial-teeth were coated with manicure type experimental dental plaque covering mainly the interproximal surface and fixed in the jaw model of a dental simulator. A modified scrubbing technique was employed to brush out the plaque conducted by one trained dentist using 26 different toothbrushes from the equal number of separate interproximal conditions. The rate of the plaque removal (%) was calculated by measuring the plaque free areas on the post-brush images. Results The data analysis using mixed effect modelling showed that stiffness, number of tufts and total length have effect on the rate of the plaque removable from the interproximal surfaces. Conclusion This study indicated consideration should be given to toothbrush properties to enhance plaque removal from the interproximal surfaces.
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Affiliation(s)
- Ryoko Otsuka
- Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama, Japan.,Department of Operative Dentistry, Tsurumi University School of Dental Medicine, Yokohama, Japan
| | - Yoshiaki Nomura
- Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama, Japan
| | - Ayako Okada
- Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama, Japan.,Department of Operative Dentistry, Tsurumi University School of Dental Medicine, Yokohama, Japan
| | - Hiromi Uematsu
- Department of Operative Dentistry, Tsurumi University School of Dental Medicine, Yokohama, Japan.,The Nippon Dental University Hospital, Division of Dental Hygiene, Chiyoda-ku, Japan
| | - Masahiro Nakano
- Department of Operative Dentistry, Tsurumi University School of Dental Medicine, Yokohama, Japan.,Nakano Dental Clinic, Ota, Japan
| | - Kiyomi Hikiji
- Tsurumi University Dental Hospital, Division of Dental Hygienists, Yokohama, Japan
| | - Nobuhiro Hanada
- Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama, Japan
| | - Yasuko Momoi
- Department of Operative Dentistry, Tsurumi University School of Dental Medicine, Yokohama, Japan
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38
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Mufti HN, Hirsch GM, Abidi SR, Abidi SSR. Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study. JMIR Med Inform 2019; 7:e14993. [PMID: 31558433 PMCID: PMC6913743 DOI: 10.2196/14993] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 09/02/2019] [Accepted: 09/24/2019] [Indexed: 12/28/2022] Open
Abstract
Background Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. Results Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.
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Affiliation(s)
- Hani Nabeel Mufti
- Division of Cardiac Surgery, Department of Cardiac Sciences, King Faisal Cardiac Center, King Abdulaziz Medical City, Ministry of National Guard Health Affairs - Western Region, Jeddah, Saudi Arabia.,College of Medicine-Jeddah, King Saud bin Abdulaziz University for Health, Ministry of National Guard Health Affairs, Jeddah, Saudi Arabia.,King Abdullah International Medical Research Center, Jeddah, Saudi Arabia
| | | | - Samina Raza Abidi
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Syed Sibte Raza Abidi
- kNowledge Intensive Computing for Healthcare Enterprise Research Group, Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
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Tavana M, Hajipour V. A practical review and taxonomy of fuzzy expert systems: methods and applications. BENCHMARKING-AN INTERNATIONAL JOURNAL 2019. [DOI: 10.1108/bij-04-2019-0178] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Purpose
Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.
Design/methodology/approach
The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.
Findings
The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.
Originality/value
Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.
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40
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Shillan D, Sterne JAC, Champneys A, Gibbison B. Use of machine learning to analyse routinely collected intensive care unit data: a systematic review. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:284. [PMID: 31439010 PMCID: PMC6704673 DOI: 10.1186/s13054-019-2564-9] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/09/2019] [Indexed: 01/30/2023]
Abstract
BACKGROUND Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians. METHODS Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted. RESULTS Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43 [16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108-4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000-10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]). CONCLUSIONS The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice.
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Affiliation(s)
- Duncan Shillan
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jonathan A C Sterne
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alan Champneys
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
| | - Ben Gibbison
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK. .,Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. .,Department of Anaesthesia, Bristol Royal Infirmary, Level 7 Queens Building, Upper Maudlin St, Bristol, BS2 8HW, UK.
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41
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Alabi RO, Elmusrati M, Sawazaki-Calone I, Kowalski LP, Haglund C, Coletta RD, Mäkitie AA, Salo T, Leivo I, Almangush A. Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. Virchows Arch 2019; 475:489-497. [PMID: 31422502 PMCID: PMC6828835 DOI: 10.1007/s00428-019-02642-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/26/2019] [Accepted: 07/31/2019] [Indexed: 12/25/2022]
Abstract
Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Iris Sawazaki-Calone
- Oral Pathology and Oral Medicine, Dentistry School, Western Parana State University, Cascavel, PR, Brazil
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, SP, Brazil
| | - Caj Haglund
- Research Programs Unit, Translational Cancer Biology, University of Helsinki, Helsinki, Finland.,Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ricardo D Coletta
- Department of Oral Diagnosis, School of Dentistry, University of Campinas, Piracicaba, São Paulo, Brazil
| | - Antti A Mäkitie
- Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Programme in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Tuula Salo
- Department of Pathology, University of Helsinki, Helsinki, Finland.,Department of Oral and Maxillofacial Diseases, University of Helsinki, Helsinki, Finland.,Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Alhadi Almangush
- Research Programme in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. .,Department of Pathology, University of Helsinki, Helsinki, Finland. .,Institute of Biomedicine, Pathology, University of Turku, Turku, Finland. .,Faculty of Dentistry, University of Misurata, Misurata, Libya.
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Choi A, Jung H, Mun JH. Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2974. [PMID: 31284482 PMCID: PMC6651410 DOI: 10.3390/s19132974] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 06/29/2019] [Accepted: 07/04/2019] [Indexed: 11/16/2022]
Abstract
A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.
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Affiliation(s)
- Ahnryul Choi
- Department of Biomedical Engineering, College of Medical Convergence, Catholic Kwandong University, 24, Beomilro 579beongil, Gangneung, Gangwon 25601, Korea
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seoburo, Jangan, Suwon, Gyeonggi 16419, Korea
| | - Hyunwoo Jung
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seoburo, Jangan, Suwon, Gyeonggi 16419, Korea
| | - Joung Hwan Mun
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seoburo, Jangan, Suwon, Gyeonggi 16419, Korea.
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Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2019. [DOI: 10.2478/pjmpe-2019-0018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.
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Sow B, Mukhtar H, Ahmad HF, Suguri H. Assessing the relative importance of social determinants of health in malaria and anemia classification based on machine learning techniques. Inform Health Soc Care 2019; 45:229-241. [PMID: 30917718 DOI: 10.1080/17538157.2019.1582056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Disparate types of data including biological and environmental have been used in supervised learning to predict a specific disease outcome. However, social determinants of health, which have been explored very little, promise to be significant predictors of public health problems such as malaria and anemia among children. We considered studying their contribution power in malaria and anemia predictions based on Variable Importance in Projection (VIP). This innovative method has potential advantages as it analyzes the impact of independent variables on disease prediction. In addition, we applied five machine learning algorithms to classify both diseases, using social determinants of health data, and compared their results. Of them all, artificial neural networks gave the best results of 94.74% and 84.17% accuracy for malaria and anemia prediction, respectively. These results are consistent and reflect the significance of non-medical factors in disease prediction.
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Affiliation(s)
- Boubacar Sow
- Graduate School of Project Design, Miyagi University , Sendai City, Miyagi Prefecture, Japan
| | - Hamid Mukhtar
- Department of Computing, School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST) , Islamabad, Pakistan
| | - Hafiz Farooq Ahmad
- Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University , Alahsa, Saudi Arabia
| | - Hiroki Suguri
- Graduate School of Project Design, Miyagi University , Sendai City, Miyagi Prefecture, Japan
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45
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Farhadian M, Salemi F, Saati S, Nafisi N. Dental age estimation using the pulp-to-tooth ratio in canines by neural networks. Imaging Sci Dent 2019; 49:19-26. [PMID: 30941284 PMCID: PMC6444008 DOI: 10.5624/isd.2019.49.1.19] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/26/2018] [Accepted: 11/07/2018] [Indexed: 12/02/2022] Open
Abstract
Purpose It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.
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Affiliation(s)
- Maryam Farhadian
- Department of Biostatistics, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Fatemeh Salemi
- Department of Oral and Maxillofacial Radiology, Dental School, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Samira Saati
- Department of Oral and Maxillofacial Radiology, Dental School, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Nika Nafisi
- Department of Oral and Maxillofacial Radiology, Dental School, Hamadan University of Medical Sciences, Hamadan, Iran
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46
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A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04109-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Prakash A, Saha SK. A study on use of the web for automatic answering of remedy finding questions of common users. Technol Health Care 2018; 27:23-35. [PMID: 30507596 DOI: 10.3233/thc-181382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The World Wide Web has become a huge repository of knowledge in many domains, including health problems and remedy. An intelligent system, having the capability of mining the relevant information from the web, can provide instant guidance in our basic health problems. OBJECTIVE The first objective is to convert the free-form long user query into a structured summary. The second objective is to provide an advice for a health query posed by a user. The suggestion can be in the form of names of medicines and related information or a warning to indicate that the situation is a medical emergency. METHODS First, a set of template information is extracted from the user question. A search query is formed to retrieve relevant pages from a set of trusted websites. The retrieved pages are processed in various levels to extract the remedy and related information. RESULTS AND CONCLUSION The system is tested using a set of real questions collected from various relevant websites. The system generated suggestions are evaluated by experts. Evaluation results show that the system provides relevant results in 92.92% cases.
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Vellido A, Ribas V, Morales C, Ruiz Sanmartín A, Ruiz Rodríguez JC. Machine learning in critical care: state-of-the-art and a sepsis case study. Biomed Eng Online 2018; 17:135. [PMID: 30458795 PMCID: PMC6245501 DOI: 10.1186/s12938-018-0569-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. RESULTS The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. CONCLUSION We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.
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Affiliation(s)
- Alfredo Vellido
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya, C. Jordi Girona, 1-3, 08034, Barcelona, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain.
| | - Vicent Ribas
- Data Analytics in Medicine, EureCat, Avinguda Diagonal, 177, 08018, Barcelona, Spain
| | - Carles Morales
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya, C. Jordi Girona, 1-3, 08034, Barcelona, Spain
| | - Adolfo Ruiz Sanmartín
- Critical Care Deparment, Vall d'Hebron University Hospital. Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d' Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08035, Barcelona, Spain
| | - Juan Carlos Ruiz Rodríguez
- Critical Care Deparment, Vall d'Hebron University Hospital. Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d' Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08035, Barcelona, Spain
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Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2016:7035463. [PMID: 27195660 PMCID: PMC5058566 DOI: 10.1155/2016/7035463] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 03/15/2016] [Indexed: 11/18/2022]
Abstract
For hospitals' admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.
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50
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Pomi A. Exploring the sources and mechanisms of cognitive errors in medical diagnosis with associative memory models. ACTA ACUST UNITED AC 2018. [PMID: 29536941 DOI: 10.1515/dx-2017-0024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND One of the central challenges of third millennium medicine is the abatement of medical errors. Among the most frequent and hardiest causes of misdiagnosis are cognitive errors produced by faulty medical reasoning. These errors have been analyzed from the perspectives of cognitive psychology and empirical medical studies. We introduce a neurocognitive model of medical diagnosis to address this issue. METHODS We construct a connectionist model based on the associative nature of human memory to explore the non-analytical, pattern-recognition mode of diagnosis. A context-dependent matrix memory associates signs and symptoms with their corresponding diseases. The weights of these associations depend on the frequencies of occurrence of each disease and on the different combinations of signs and symptoms of each presentation of that disease. The system receives signs and symptoms and by a second input, the degree of diagnostic uncertainty. Its output is a probabilistic map on the set of possible diseases. RESULTS The model reproduces different kinds of well-known cognitive errors in diagnosis. Errors in the model come from two sources. One, dependent on the knowledge stored in memory, varies with the accumulated experience of the physician and explains age-dependent errors and effects such as epidemiological masking. The other is independent of experience and explains contextual effects such as anchoring. CONCLUSIONS Our results strongly suggest that cognitive biases are inevitable consequences of associative storage and recall. We found that this model provides valuable insight into the mechanisms of cognitive error and we hope it will prove useful in medical education.
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Affiliation(s)
- Andrés Pomi
- Group of Cognitive Systems Modeling, Sección Biofísica, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo 11400, Uruguay
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