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Ahmad E, Lim S, Lamptey R, Webb DR, Davies MJ. Type 2 diabetes. Lancet 2022; 400:1803-1820. [PMID: 36332637 DOI: 10.1016/s0140-6736(22)01655-5] [Citation(s) in RCA: 241] [Impact Index Per Article: 120.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/10/2022] [Accepted: 08/19/2022] [Indexed: 11/06/2022]
Abstract
Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.
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Affiliation(s)
- Ehtasham Ahmad
- Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK
| | - Soo Lim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Roberta Lamptey
- Family Medicine Department, Korle Bu Teaching Hospital, Accra Ghana and Community Health Department, University of Ghana Medical School, Accra, Ghana
| | - David R Webb
- Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK
| | - Melanie J Davies
- Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK.
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Pitchai R, Dappuri B, Pramila PV, Vidhyalakshmi M, Shanthi S, Alonazi WB, Almutairi KMA, Sundaram RS, Beyene I. An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5489084. [PMID: 36275965 PMCID: PMC9581610 DOI: 10.1155/2022/5489084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/10/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022]
Abstract
Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.
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Affiliation(s)
- R. Pitchai
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur 502313, Telangana, India
| | - Bhasker Dappuri
- Department of Electronics and Communication Engineering, CMR Engineering College, Kandlakoya 501401, Telangana, India
| | - P. V. Pramila
- Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 600124, Tamil Nadu, India
| | - M. Vidhyalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai 600089, Tamilnadu, India
| | - S. Shanthi
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai 638060, Tamil Nadu, India
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, P. O Box: 71115, Riyadh 11587, Saudi Arabia
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box 10219, Riyadh 11433, Saudi Arabia
| | - R. S. Sundaram
- Department of Health Sciences, University of Texas, TX, USA
| | - Ibsa Beyene
- Department of IT, Mettu University, Mettu, Ethiopia
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