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Syed M, Syed S, Sexton K, Syeda HB, Garza M, Zozus M, Syed F, Begum S, Syed AU, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. INFORMATICS-BASEL 2021; 8. [PMID: 33981592 DOI: 10.3390/informatics8010016] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
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Affiliation(s)
- Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Kevin Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Surgery, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Health Policy and Management, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Hafsa Bareen Syeda
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Maryam Garza
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Meredith Zozus
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, USA
| | - Farhanuddin Syed
- Shadan Institute of Medical Sciences, College of Medicine, Hyderabad, Telangana 500086, India
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Abdullah Usama Syed
- Department of Information Science, University of Arkansas at Little Rock (UALR), Little Rock, Arkansas 72205, USA
| | - Joseph Sanford
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
- Department of Anesthesiology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas 72205, USA
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Wang Q, Li L, Zhang Y, Cui Q, Fu Y, Shi W, Wang Q, Xu D. Research on the Establishment and Application of the Environmental Health Indicator System of Atmospheric Pollution in China. BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2021; 106:225-234. [PMID: 33462648 DOI: 10.1007/s00128-020-03084-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/14/2020] [Indexed: 06/12/2023]
Abstract
To understand the health impact represented by exposure to current atmospheric pollution in China, an environmental health indicators (EHIs) system of atmospheric pollution was established. The EHIs were based on comprehensive consideration of environment, population, economy and diseases associated with atmospheric pollution. An EHIs evaluation system of atmospheric pollution, based on corresponding EHIs data collection and weighting coefficients determined using principal component analysis, was applied to major provinces and regions in China to evaluate the environmental health status. Results showed that the EHIs of atmospheric pollution in Central and East China were low, indicating a serious environmental health condition. Prevention and management of atmospheric pollution in these regions should be strengthened and protective measures taken to improve human health. Compared with other methods, the EHIs evaluation system was more intuitive, which facilitated users to identify the environmental health status and provided support for health management and pollution prevention.
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Affiliation(s)
- Qiong Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Liangzhong Li
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, Center for Environmental Health Research, South China Institute of Environmental Sciences, The Ministry of Ecological and Environment of PR China, Guangzhou, 510655, China
| | - Yanping Zhang
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Qian Cui
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
- School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China
| | - Yuanzheng Fu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, 110122, China
| | - Wanying Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Qin Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Dongqun Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
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Farnell DJJ, Richmond S, Galloway J, Zhurov AI, Pirttiniemi P, Heikkinen T, Harila V, Matthews H, Claes P. Multilevel principal components analysis of three-dimensional facial growth in adolescents. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 188:105272. [PMID: 31865094 DOI: 10.1016/j.cmpb.2019.105272] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/19/2019] [Accepted: 12/10/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES The study of age-related facial shape changes across different populations and sexes requires new multivariate tools to disentangle different sources of variations present in 3D facial images. Here we wish to use a multivariate technique called multilevel principal components analysis (mPCA) to study three-dimensional facial growth in adolescents. METHODS These facial shapes were captured for Welsh and Finnish subjects (both male and female) at multiple ages from 12 to 17 years old (i.e., repeated-measures data). 1000 "dense" 3D points were defined regularly for each shape by using a deformable template via "meshmonk" software. A three-level model was used here, namely: level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The technicalities underpinning the mPCA method are presented in Appendices. RESULTS Eigenvalues via mPCA predicted that: level 1 (ethnicity/sex) contained 7.9% of variation; level 2 contained 71.5%; and level 3 (age) contained 20.6%. The results for the eigenvalues via mPCA followed a similar pattern to those results of single-level PCA. Results for modes of variation made sense, where effects due to ethnicity, sex, and age were reflected in modes at appropriate levels of the model. Standardised scores at level 1 via mPCA showed much stronger differentiation between sex and ethnicity groups than results of single-level PCA. Results for standardised scores from both single-level PCA and mPCA at level 3 indicated that females had different average "trajectories" with respect to these scores than males, which suggests that facial shape matures in different ways for males and females. No strong evidence of differences in growth patterns between Finnish and Welsh subjects was observed. CONCLUSIONS mPCA results agree with existing research relating to the general process of facial changes in adolescents with respect to age quoted in the literature. They support previous evidence that suggests that males demonstrate larger changes and for a longer period of time compared to females, especially in the lower third of the face. These calculations are therefore an excellent initial test that multivariate multilevel methods such as mPCA can be used to describe such age-related changes for "dense" 3D point data.
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Affiliation(s)
- D J J Farnell
- School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom.
| | - S Richmond
- School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom
| | - J Galloway
- School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom
| | - A I Zhurov
- School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom
| | - P Pirttiniemi
- Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, Oulu, Finland
| | - T Heikkinen
- Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, Oulu, Finland
| | - V Harila
- Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, Oulu, Finland
| | - H Matthews
- Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium; OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium; Facial Sciences Research Group, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - P Claes
- Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium; Department of Electrical Engineering, ESAT/PSI, KU Leuven, 3000 Leuven, Belgium
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