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Narasimhan R, Gopalan M, Sikkandar MY, Alassaf A, AlMohimeed I, Alhussaini K, Aleid A, Sheik SB. Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers. SENSORS (BASEL, SWITZERLAND) 2023; 23:8867. [PMID: 37960568 PMCID: PMC10647614 DOI: 10.3390/s23218867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/22/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023]
Abstract
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual's activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults' activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.
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Affiliation(s)
- Rajaram Narasimhan
- Centre for Sensors and Process Control, Hindustan Institute of Technology and Science, Chennai 603103, India;
| | - Muthukumaran Gopalan
- Centre for Sensors and Process Control, Hindustan Institute of Technology and Science, Chennai 603103, India;
| | - Mohamed Yacin Sikkandar
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Ahmad Alassaf
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Ibrahim AlMohimeed
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Khalid Alhussaini
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (K.A.); (A.A.)
| | - Adham Aleid
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (K.A.); (A.A.)
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Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
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Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
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Taylor B, Barboi C, Boustani M. Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review. J Am Geriatr Soc 2023; 71:2966-2974. [PMID: 37249252 DOI: 10.1111/jgs.18426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 04/12/2023] [Accepted: 04/30/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND The timely detection of Alzheimer's disease and other related dementias (ADRD) is suboptimal. Digital data already stored in electronic health records (EHR) offer opportunities for enhancing the timely detection of ADRD by facilitating the development of passive digital markers (PDMs). We conducted a systematic evidence review to identify studies that describe the development, performance, and validity of EHR-based PDMs for ADRD. METHODS We searched the literature published from January 2000 to August 2022 and reviewed cross-sectional, retrospective, or prospective observational studies with a patient population of 18 years or older, published in English that collected and interpreted original data, included EHR as a source of digital data, and had the primary purpose of supporting ADRD care. We extracted relevant data from the included studies with guidance from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and used the US Preventive Services Task Force criteria to appraise each study. RESULTS We included and appraised 19 studies. Four studies were considered to have a fair quality, and none was considered to have a good quality. The functionality of the PDMs varied from detecting mild cognitive impairment, Alzheimer's disease or ADRD, to forecasting stages of ADRD. Only seven studies used a valid reference diagnostic method. Nine PDMs used only structured EHR data, and five studies provided complete information on the race and ethnicity of its population. The number of features included in the PDMs ranges from 10 to 853, and the PMDs used a variety of statistical and machine learning algorithms with various time-at-risk windows. The area under the curve (AUC) for the PDMs varied from 0.67 to 0.97. CONCLUSION Although we noted heterogeneity in the PDMs development and performance, there is evidence that these PDMs have the potential to detect ADRD at earlier stages.
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Affiliation(s)
- Britain Taylor
- Department of Intelligent Systems Engineering, School of Informatics, Computing, and Engineering. Indiana University, Bloomington, Indiana, USA
| | - Cristina Barboi
- Department of Epidemiology, School of Public Health. Indiana University, Indianapolis, Indiana, USA
| | - Malaz Boustani
- Center for Health Innovation and Implementation Science, Department of Medicine, School of Medicine, Indiana University, Indianapolis, Indiana, USA
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Quek LJ, Heikkonen MR, Lau Y. Use of artificial intelligence techniques for detection of mild cognitive impairment: A systematic scoping review. J Clin Nurs 2023; 32:5752-5762. [PMID: 37032649 DOI: 10.1111/jocn.16699] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/10/2022] [Accepted: 02/28/2023] [Indexed: 04/11/2023]
Abstract
AIMS AND OBJECTIVES The objective of this scoping review is to explore the types and mechanisms of Artificial intelligence (AI) techniques for detecting mild cognitive impairment (MCI). BACKGROUND Early detection of MCI is crucial because it may progress to Alzheimer's disease. DESIGN A systematic scoping review. METHODS Five-step framework of Arksey and O'Malley was used following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews checklist. A total of 11 databases (PubMed, EMBASE, CINAHL, Cochrane Library, Scopus, Web of Science, IEEE Explore, Science.gov, ACM digital library, arXIV and ProQuest) was used to search from inception till 17th December 2021. Grey literature and reference list were searched. Articles screening and data charting were conducted by two independent reviewers. RESULTS There were a total of 70 articles included from 2011 to 2022 across 16 countries. Four types of AI techniques were found, namely machine learning (ML), deep learning (DL), fuzzy logic (FL) and technique combinations. Herein, ML detects similar pattern within preselected data to classify subjects into non-MCI or MCI groups. Meanwhile, DL performs classification based on data patterns and data analyses are performed by themselves. Furthermore, FL utilises human-defined rules to decide the degree to which a person has MCI. A combination of AI techniques enhances the feature preparation phase for ML or DL to perform accurate classification. CONCLUSION Although AI-based MCI detection tool is critical for healthcare decision-making, clinical utility and risks remain underexplored. Hopefully, this review equips clinicians with background AI knowledge to address these clinical concerns. Hence, future research should explore more techniques and representative datasets to improve AI development. RELEVANCE TO CLINICAL PRACTICE Results of this review can increase the knowledge of AI-based MCI detection tools. REVIEW REGISTRATION This study protocol was registered in the Open Science Framework Registries (https://osf.io/45rdt).
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Affiliation(s)
- Li JuanVivian Quek
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore
| | - Maria Rosaliini Heikkonen
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore
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Intrinsic Capacity to Predict Future Adverse Health Outcomes in Older Adults: A Scoping Review. Healthcare (Basel) 2023; 11:healthcare11040450. [PMID: 36832984 PMCID: PMC9957180 DOI: 10.3390/healthcare11040450] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/24/2022] [Accepted: 01/10/2023] [Indexed: 02/09/2023] Open
Abstract
OBJECTIVE Intrinsic capacity is recognized as an important determinant of healthy aging and well-being of older adults; however, relatively little is known about the intrinsic capacity of older adults to predict adverse health outcomes. The study aimed to examine which adverse health outcomes of older adults can be predicted by intrinsic capacity. METHODS The study was conducted using the scoping review methodological framework of Arksey and O'Malley. A systematic literature search of nine electronic databases (i.e., Pubmed, Embase, Cochrane library, Web of science, CINAHL, China National Knowledge Infrastructure, VIP, Wanfang, and the Chinese Biological Medical Literature Database) were performed from the database's inception to 1 March 2022. RESULTS Fifteen longitudinal studies were included. A series of adverse health outcomes were assessed, including physical function (n = 12), frailty (n = 3), falls (n = 3), mortality (n = 6), quality of life (n = 2) and other adverse health outcomes (n = 4). CONCLUSIONS Intrinsic capacity could predict some adverse health outcomes of different follow-up times for older adults; however, due to the small number of studies and sample size, more high-quality studies are necessary to explore the longitudinal relationships between intrinsic capacity and adverse health outcomes in the future.
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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Camino-Pontes B, Gonzalez-Lopez F, Santamaría-Gomez G, Sutil-Jimenez AJ, Sastre-Barrios C, de Pierola IF, Cortes JM. One-year prediction of cognitive decline following cognitive-stimulation from real-world data. J Neuropsychol 2023. [PMID: 36727214 DOI: 10.1111/jnp.12307] [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: 11/26/2021] [Revised: 01/09/2023] [Accepted: 01/17/2023] [Indexed: 02/03/2023]
Abstract
Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.
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Affiliation(s)
| | | | | | | | | | | | - Jesus M Cortes
- Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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Chen T, Su P, Shen Y, Chen L, Mahmud M, Zhao Y, Antoniou G. A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia. Front Neurosci 2022; 16:867664. [PMID: 35979331 PMCID: PMC9376621 DOI: 10.3389/fnins.2022.867664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.
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Affiliation(s)
- Tianhua Chen
- Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Pan Su
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
| | - Yinghua Shen
- School of Economics and Business Administration, Chongqing University, Chongqing, China
| | - Lu Chen
- Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Grigoris Antoniou
- Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
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Ibrahim OA, Fu S, Vassilaki M, Mielke MM, St Sauver J, Petersen RC, Sohn S. Detection of Dementia Signals from Longitudinal Clinical Visits Using One-Class Classification. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS 2022; 2022:211-216. [PMID: 36484060 PMCID: PMC9728104 DOI: 10.1109/ichi54592.2022.00040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Dementia is one of the major health challenges in aging populations, with 50 million people diagnosed worldwide. However, dementia is often underdiagnosed or delayed resulting in missed opportunities for appropriate care plans. Identifying early signs of dementia is essential for better life quality of aging populations. Monitoring early signs of individual health changes could help clinicians diagnose dementia in its early stages with more effective treatment plans. However, rare data for dementia cases compared to the normal (i.e., imbalance class distribution) make it challenging to develop robust supervised learning models. In order to alleviate this issue, we investigated one-class classification (OCC) techniques, which use only majority class (i.e., normal cases) in model development to detect dementia signals from older adult clinical visits. The OCC models identify abnormality of older adults' longitudinal health conditions to predict incident dementia. The predictive performance of the OCC was compared with a recent streaming clustering-based technique and demonstrated higher predictive power. Our analysis showed that OCC has a promising potential to increase power in predicting dementia.
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Affiliation(s)
- Omar A. Ibrahim
- Department of Artificial Intelligence and Informatics Mayo Clinic Rochester, MN, USA
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics Mayo Clininc Rochester, MN, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences Mayo Clinic Rochester, MN, USA
| | - Michelle M. Mielke
- Department of Quantitative Health Sciences / Neurology Mayo Clinic Rochester, MN, USA
| | - Jennifer St Sauver
- Department of Quantitative Health Sciences Mayo Clinic Rochester, MN, USA
| | | | - Sunghwan Sohn
- Department of Artificial Intelligence and Informatics Mayo Clinic Rochester, MN, USA
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Similarity-based second chance autoencoders for textual data. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03100-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Ibrahim OA, Fu S, Vassilaki M, Petersen RC, Mielke MM, St Sauver J, Sohn S. Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:905-912. [PMID: 35237461 PMCID: PMC8883577 DOI: 10.1109/bibm52615.2021.9669672] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
more than 44 million people have been diagnosed with dementia worldwide, and this number is estimated to triple by next three decades. Given this increasing trend of older adults with cognitive impairment (CI; dementia and mild cognitive impairment) and its significant underdiagnosis, early identification of CI and understanding its progression is a critical step towards a better quality of life for the aging population. Early alert of individual health changes could facilitate better ways for clinicians to diagnose CI in its early stages and come up with more effective treatment plans. However, there is a lack of approaches to characterize patient health conditions accounting for temporal information in an unsupervised manner. Limited CI cases and its costly ascertainment in clinical settings also make unsupervised learning more promising in CI research. In this paper, a streaming clustering model was used to determine distinct patterns of older adults' health changes from their clinical visits in Mayo Clinic Study of Aging. The streaming clustering was also examined to study its ability to generate early alerts for potential incidents of CI. Our analysis demonstrated that temporal characteristics incorporated in a streaming clustering model has a promising potential to increase power in predicting CI.
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Affiliation(s)
- Omar A. Ibrahim
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ronald C. Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michelle M. Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer St Sauver
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunghwan Sohn
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
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