Journal articles: 'Cognitive informatics'

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Wang, Yingxu, Newton Howard, Janusz Kacprzyk, Ophir Frieder, Phillip Sheu, Rodolfo A. Fiorini, Marina L. Gavrilova, Shushma Patel, Jun Peng, and Bernard Widrow. "Cognitive Informatics." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 1 (January 2018): 1–13. http://dx.doi.org/10.4018/ijcini.2018010101.

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Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and cognitive computing.

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Wang, Yingxu. "Towards the Synergy of Cognitive Informatics, Neural Informatics, Brain Informatics, and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 1 (January 2011): 75–93. http://dx.doi.org/10.4018/jcini.2011010105.

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The contemporary wonder of sciences and engineering recently refocused on the starting point: how the brain processes internal and external information autonomously rather than imperatively as those of conventional computers? This paper explores the interplay and synergy of cognitive informatics, neural informatics, abstract intelligence, denotational mathematics, brain informatics, and computational intelligence. A key notion recognized in recent studies in cognitive informatics is that the root and profound objective in natural, abstract, and artificial intelligence, and in cognitive informatics and cognitive computing, is to seek suitable mathematical means for their special needs. A layered reference model of the brain and a set of cognitive processes of the mind are systematically developed towards the exploration of the theoretical framework of cognitive informatics. A wide range of applications of cognitive informatics and denotational mathematics are recognized in the development of highly intelligent systems such as cognitive computers, cognitive knowledge search engines, autonomous learning machines, and cognitive robots.

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Wang, Yingxu, George Baciu, Yiyu Yao, Witold Kinsner, Keith Chan, Bo Zhang, Stuart Hameroff, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (January 2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

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Hülse, Martin, and Manfred Hild. "Informatics for cognitive robots." Advanced Engineering Informatics 24, no. 1 (January 2010): 2–3. http://dx.doi.org/10.1016/j.aei.2009.08.001.

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Wang, Yingxu, Bernard Carlos Widrow, Bo Zhang, Witold Kinsner, Kenji Sugawara, Fuchun Sun, Jianhua Lu, Thomas Weise, and Du Zhang. "Perspectives on the Field of Cognitive Informatics and its Future Development." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 1 (January 2011): 1–17. http://dx.doi.org/10.4018/jcini.2011010101.

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The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how the brain processes internal and external information autonomously and cognitively rather than imperatively like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of eight position statements presented in the plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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Taylor, J. M., and V. Raskin. "Towards the Cognitive Informatics of Natural Language." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (July 2013): 25–45. http://dx.doi.org/10.4018/ijcini.2013070102.

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This paper deals with a contribution of computational analysis of verbal humor to natural language cognition. After a brief introduction to the growing area of computational humor and of its roots in humor theories, it describes and compares the results of a human-subject and computer experiment. The specific interest is to compare how well the computer, equipped with the resources and methodologies of the Ontological Semantic Technology, a comprehensive meaning access approach to natural language processing, can model several aspects of the cognitive behaviors of humans processing jokes from the Internet. The paper, sharing several important premises with cognitive informatics, is meant as a direct contribution to this rapidly developing transdisciplinary field, and as such, it bears on cognitive computing as well, especially at the level of implementation of computational humor in non-toy systems and the relationship to human cognitive processes of understanding and producing humor.

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Shivhare, Radhika, Aswani Kumar Cherukuri, and Jinhai Li. "Establishment of Cognitive Relations Based on Cognitive Informatics." Cognitive Computation 9, no. 5 (August 1, 2017): 721–29. http://dx.doi.org/10.1007/s12559-017-9498-9.

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Wang, Yingxu, Robert C. Berwick, Simon Haykin, Witold Pedrycz, Witold Kinsner, George Baciu, Du Zhang, and C. Bhavsar. "Cognitive Informatics and Cognitive Computing in Year 10 and Beyond." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (October 2011): 1–21. http://dx.doi.org/10.4018/jcini.2011100101.

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Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers (CogC) that perceive, infer, and learn. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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Wang, Yingxu, Edmund T. Rolls, Newton Howard, Victor Raskin, Witold Kinsner, Fionn Murtagh, Virendrakumar C. Bhavsar, Shushma Patel, Dilip Patel, and Duane F. Shell. "Cognitive Informatics and Computational Intelligence." International Journal of Software Science and Computational Intelligence 7, no. 2 (April 2015): 50–69. http://dx.doi.org/10.4018/ijssci.2015040103.

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Cognitive Informatics (CI) is a contemporary multidisciplinary field spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, cognitive linguistics, and cognitive philosophy. Cognitive Computing (CC) is a novel paradigm of intelligent computing methodologies and systems based on CI that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This paper reports a set of position statements presented in the plenary panel of IEEE ICCI*CC'14 on Cognitive Informatics and Cognitive Computing. The summary is contributed by invited panelists who are part of the world's renowned researchers and scholars in the transdisciplinary field of cognitive informatics and cognitive computing.

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Jin, Yinzi, Mingxia Jing, and Xiaochen Ma. "Effects of Digital Device Ownership on Cognitive Decline in a Middle-Aged and Elderly Population: Longitudinal Observational Study." Journal of Medical Internet Research 21, no. 7 (July 29, 2019): e14210. http://dx.doi.org/10.2196/14210.

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Background Cognitive decline is a major risk factor for disability and death and may serve as a precursor of dementia. Digital devices can provide a platform of cognitively stimulating activities which might help to slow cognitive decline during the process of normal aging. Objective This longitudinal study aimed to examine the independent protective factors of desktop and cellphone ownership against cognitive decline in mid-life and older adulthood and to examine the combined effect of desktop and cellphone ownership on the same outcome. Methods Data was obtained from a China Health and Retirement Longitudinal Studies (CHARLS) cohort made up of 13,457 community-dwelling adults aged 45 years or above in 2011-2012. They were followed for 4 years, with baseline measurements taken as well as 2 two-year follow-up visits. Cognitive function was tested during the baseline test and follow-up visits. A global cognition z-score was calculated based on two domains: word recall and mental intactness. The key independent variables were defined as: whether one had desktops with internet connection at home and whether one had a cellphone. An additional categorical variable of three values was constructed as: 0 (no desktop or cellphone), 1 (desktop or cellphone alone), and 2 (desktop and cellphone both). Mixed-effects regression was adjusted for demographic and health behavior as well as health condition risk factors. Results Adjusted for demographic and health behavior as well as health condition risk factors, desktop and cellphone ownership were independently associated with subsequent decreased cognitive decline over the four-year period. Participants without a desktop at home had an adjusted cognitive decline of –0.16 standard deviations (95% CI –0.18 to –0.15), while participants with a desktop at home had an adjusted cognitive decline of –0.10 standard deviations (95% CI –0.14 to –0.07; difference of –0.06 standard deviations; P=.003). A similar pattern of significantly protective association of 0.06 standard deviations (95% CI 0.03-0.10; P<.001) between cellphone ownership and cognitive function was observed over the four-year period. Additionally, a larger longitudinal protective association on cognitive decline was observed among those with both of the digital devices, although the 95% CIs for the coefficients overlapped with those with a single digital device alone. Conclusions Findings from this study underscored the importance of digital devices as platforms for cognitively stimulating activities to delay cognitive decline. Future studies focusing on use of digital devices are warranted to investigate their longitudinal protective factors against cognitive decline at mid- and later life.

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Wang, Yingxu, Witold Kinsner, James A. Anderson, Du Zhang, Yiyu Yao, Philip Sheu, Jeffrey Tsai, et al. "A Doctrine of Cognitive Informatics (CI)." Fundamenta Informaticae 90, no. 3 (2009): 203–28. http://dx.doi.org/10.3233/fi-2009-0015.

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Targowski, Andrew. "The Cognitive Informatics Approach towards Wisdom." Dialogue and Universalism 20, no. 9 (2010): 51–82. http://dx.doi.org/10.5840/du2010209/1015.

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Wang, Yingxu. "The Theoretical Framework of Cognitive Informatics." International Journal of Cognitive Informatics and Natural Intelligence 1, no. 1 (January 2007): 1–27. http://dx.doi.org/10.4018/jcini.2007010101.

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Wang, Yingxu, and W. Kinsner. "Editorial Recent Advances in Cognitive Informatics." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 2 (March 2006): 121–23. http://dx.doi.org/10.1109/tsmcc.2006.871120.

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Yingxu Wang and Ying Wang. "Cognitive informatics models of the brain." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 2 (March 2006): 203–7. http://dx.doi.org/10.1109/tsmcc.2006.871151.

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Nakata, Keiichi. "A cognitive perspective on social informatics." International Journal of Social and Humanistic Computing 1, no. 1 (2008): 28. http://dx.doi.org/10.1504/ijshc.2008.020478.

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Patel, Vimla L., and Thomas G. Kannampallil. "Cognitive informatics in biomedicine and healthcare." Journal of Biomedical Informatics 53 (February 2015): 3–14. http://dx.doi.org/10.1016/j.jbi.2014.12.007.

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Declerck, G., and X. Aimé. "Why Medical Informatics (still) Needs Cognitive and Social Sciences." Yearbook of Medical Informatics 22, no. 01 (August 2013): 86–92. http://dx.doi.org/10.1055/s-0038-1638837.

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Summary Objectives: To summarize current excellent medical informatics research in the field of human factors and organizational issues. Methods: Using PubMed, a total of 3,024 papers were selected from 17 journals. The papers were evaluated on the basis of their title, keywords, and abstract, using several exclusion and inclusion criteria. 15 preselected papers were carefully evaluated by six referees using a standard evaluation grid. Results: Six best papers were selected exemplifying the central role cognitive and social sciences can play in medical informatics research. Among other contributions, those studies: (i) make use of the distributed cognition paradigm to model and understand clinical care situations; (ii) take into account organizational issues to analyse the impact of HIT on information exchange and coordination processes; (iii) illustrate how models and empirical data from cognitive psychology can be used in medical informatics; and (iv) highlight the need of qualitative studies to analyze the unexpected side effects of HIT on cognitive and work processes. Conclusion: The selected papers demonstrate that paradigms, methodologies, models, and results from cognitive and social sciences can help to bridge the gap between HIT and end users, and contribute to limit adoption failures that are reported regularly.

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Wang, Yingxu. "Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing." Fundamenta Informaticae 90, no. 3 (2009): 283–303. http://dx.doi.org/10.3233/fi-2009-0019.

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Chua, Sean Ing Loon, Ngiap Chuan Tan, Wei Teen Wong, John Carson Allen Jr, Joanne Hui Min Quah, Rahul Malhotra, and Truls Østbye. "Virtual Reality for Screening of Cognitive Function in Older Persons: Comparative Study." Journal of Medical Internet Research 21, no. 8 (August 1, 2019): e14821. http://dx.doi.org/10.2196/14821.

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Background The prevalence of dementia, which presents as cognitive decline in one or more cognitive domains affecting function, is increasing worldwide. Traditional cognitive screening tools for dementia have their limitations, with emphasis on memory and, to a lesser extent, on the cognitive domain of executive function. The use of virtual reality (VR) in screening for cognitive function in older persons is promising, but evidence for its use is sparse. Objective The primary aim was to examine the feasibility and acceptability of using VR to screen for cognitive impairment in older persons in a primary care setting. The secondary aim was to assess the module’s ability to discriminate between cognitively intact and cognitively impaired participants. Methods A comparative study was conducted at a public primary care clinic in Singapore, where persons aged 65-85 years were recruited based on a cut-off score of 26 on the Montreal Cognitive Assessment (MoCA) scale. They participated in a VR module for assessment of their learning and memory, perceptual-motor function, and executive function. Each participant was evaluated by the total performance score (range: 0-700) upon completion of the study. A questionnaire was also administered to assess their perception of and attitude toward VR. Results A total of 37 participants in Group 1 (cognitively intact; MoCA score≥26) and 23 participants in Group 2 (cognitively impaired; MoCA score<26) were assessed. The mean time to completion of the study was 19.1 (SD 3.6) minutes in Group 1 and 20.4 (3.4) minutes in Group 2. Mean feedback scores ranged from 3.80 to 4.48 (max=5) in favor of VR. The total performance score in Group 1 (552.0, SD 57.2) was higher than that in Group 2 (476.1, SD 61.9; P<.001) and exhibited a moderate positive correlation with scores from other cognitive screening tools: Abbreviated Mental Test (0.312), Mini-Mental State Examination (0.373), and MoCA (0.427). A receiver operating characteristic curve analysis for the relationship between the total performance score and the presence of cognitive impairment showed an area under curve of 0.821 (95% CI 0.714-0.928). Conclusions We demonstrated the feasibility of using a VR-based screening tool for cognitive function in older persons in primary care, who were largely in favor of this tool.

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Hu, Kai, Yingxu Wang, and Yousheng Tian. "A Web Knowledge Discovery Engine Based on Concept Algebra." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (January 2010): 80–97. http://dx.doi.org/10.4018/jcini.2010010105.

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Autonomous on-line knowledge discovery and acquisition play an important role in cognitive informatics, cognitive computing, knowledge engineering, and computational intelligence. On the basis of the latest advances in cognitive informatics and denotational mathematics, this paper develops a web knowledge discovery engine for web document restructuring and comprehension, which decodes on-line knowledge represented in informal documents into cognitive knowledge represented by concept algebra and concept networks. A visualized concept network explorer and a semantic analyzer are implemented to capture and refine queries based on concept algebra. A graphical interface is built using concept and semantic models to refine users’ queries. To enable autonomous information restructuring by machines, a two-level knowledge base that mimics human lexical/syntactical and semantic cognition is introduced. The information restructuring model provides a foundation for automatic concept indexing and knowledge extraction from web documents. The web knowledge discovery engine extends machine learning capability from imperative and adaptive information processing to autonomous and cognitive knowledge processing with unstructured documents in natural languages.

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Wang, Yingxu, James A. Anderson, George Baciu, Gerhard Budin, D. Frank Hsu, Mitsuru Ishizuka, Witold Kinsner, et al. "Perspectives on eBrain and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 4 (October 2012): 1–21. http://dx.doi.org/10.4018/jcini.2012100101.

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Cognitive Informatics (CI) is a discipline spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, and cognitive linguistics. CI aims to investigate the internal information processing mechanisms and processes of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing and computational intelligence. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’12 on eBrain and Cognitive Computers contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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Wang, Yingxu. "On Abstract Intelligence and Brain Informatics." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 4 (October 2012): 54–80. http://dx.doi.org/10.4018/jcini.2012100103.

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A key notion in abstract intelligence and cognitive informatics is that the brain and natural intelligence may only be explained by a hierarchical and reductive theory that maps the brain through the embodied neurological, physiological, cognitive, and logical levels from bottom-up induction and top-down deduction. This paper presents an abstract intelligence framework for modeling the structures and functions of the brain across these four levels. A set of abstract intelligent model, cognitive functional model, and neurophysiological model of the brain is systematically developed. On the basis of the abstract intelligent models of the brain at different levels, the conventionally highly overlapped, redundant, and even contradicted empirical observations in brain studies and cognitive psychology may be rigorously clarified and neatly explained. The improved understanding about the brain has led to the development of a wide range of novel technologies and systems such as cognitive computers, cognitive robots, and other applied cognitive systems.

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Wang, Yingxu, Du Zhang, and Shusaku Tsumoto. "Preface: Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)." Fundamenta Informaticae 90, no. 3 (2009): i—vii. http://dx.doi.org/10.3233/fi-2009-0014.

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Wang, Yingxu, Du Zhang, and Shusaku Tsumoto. "Preface: Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)." Fundamenta Informaticae 90, no. 4 (2009): i—vii. http://dx.doi.org/10.3233/fi-2009-0022.

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Wang, Yingxu, Gabriele Fariello, Marina L. Gavrilova, Witold Kinsner, Fumio Mizoguchi, Shushma Patel, Dilip Patel, et al. "Perspectives on Cognitive Computers and Knowledge Processors." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (July 2013): 1–24. http://dx.doi.org/10.4018/ijcini.2013070101.

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Cognitive Informatics (CI) is a contemporary multidisciplinary field spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, cognitive linguistics, and cognitive philosophy. CI aims to investigate the internal information processing mechanisms and processes of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing and computational intelligence. This paper reports a set of eleven position statements presented in the plenary panel of IEEE ICCI*CC’13 on Cognitive Computers and Knowledge Processors contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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Gadhok, N., and W. Kinner. "Robust Independent Component Analysis for Cognitive Informatics." International Journal of Cognitive Informatics and Natural Intelligence 2, no. 4 (October 2008): 44–54. http://dx.doi.org/10.4018/jcini.2008100104.

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Yingxu Wang, W. Kinsner, and Du Zhang. "Special Issue on Cybernetics and Cognitive Informatics." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 4 (August 2009): 818–22. http://dx.doi.org/10.1109/tsmcb.2009.2017294.

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Wang, Yingxu. "On the Latest Development in Cognitive Informatics." ACM SIGSOFT Software Engineering Notes 28, no. 2 (March 2003): 2. http://dx.doi.org/10.1145/638750.638771.

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Holmboe, Christian. "A cognitive framework for knowledge in informatics." ACM SIGCSE Bulletin 31, no. 3 (September 1999): 17–20. http://dx.doi.org/10.1145/384267.305833.

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Kannampallil, Thomas, and Vimla L. Patel. "Cognitive informatics methods for interactive clinical systems." Journal of Biomedical Informatics 60 (April 2016): 197–98. http://dx.doi.org/10.1016/j.jbi.2016.02.001.

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Yuan, Jing, David J. Libon, Cody Karjadi, Alvin F. A. Ang, Sherral Devine, Sanford H. Auerbach, Rhoda Au, and Honghuang Lin. "Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study)." Journal of Medical Internet Research 23, no. 6 (June 8, 2021): e27407. http://dx.doi.org/10.2196/27407.

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Background The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function. Objective We aimed to investigate the association between dCDT features and cognitive performance across multiple domains. Methods Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition). Results The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment. Conclusions The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations.

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Hsu, Wan-Yu, William Rowles, Joaquin A. Anguera, Chao Zhao, Annika Anderson, Amber Alexander, Simone Sacco, Roland Henry, Adam Gazzaley, and Riley Bove. "Application of an Adaptive, Digital, Game-Based Approach for Cognitive Assessment in Multiple Sclerosis: Observational Study." Journal of Medical Internet Research 23, no. 1 (January 20, 2021): e24356. http://dx.doi.org/10.2196/24356.

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Background Cognitive impairment is one of the most debilitating manifestations of multiple sclerosis. Currently, the assessment of cognition relies on a time-consuming and extensive neuropsychological examination, which is only available in some centers. Objective To enable simpler, more accessible cognitive screening, we sought to determine the feasibility and potential assessment sensitivity of an unsupervised, adaptive, video game–based digital therapeutic to assess cognition in multiple sclerosis. Methods A total of 100 people with multiple sclerosis (33 with cognitive impairment and 67 without cognitive impairment) and 24 adults without multiple sclerosis were tested with the tablet game (EVO Monitor) and standard measures, including the Brief International Cognitive Assessment for Multiple Sclerosis (which included the Symbol Digit Modalities Test [SDMT]) and Multiple Sclerosis Functional Composite 4 (which included the Timed 25-Foot Walk test). Patients with multiple sclerosis also underwent neurological evaluations and contributed recent structural magnetic resonance imaging scans. Group differences in EVO Monitor performance and the association between EVO Monitor performance and standard measures were investigated. Results Participants with multiple sclerosis and cognitive impairment showed worse performance in EVO Monitor compared with participants without multiple sclerosis (P=.01) and participants with multiple sclerosis without cognitive impairment (all P<.002). Regression analyses indicated that participants with a lower SDMT score showed lower performance in EVO Monitor (r=0.52, P<.001). Further exploratory analyses revealed associations between performance in EVO Monitor and walking speed (r=–0.45, P<.001) as well as brain volumetric data (left thalamic volume: r=0.47, P<.001; right thalamic volume: r=0.39, P=.002; left rostral middle frontal volume: r=0.28, P=.03; right rostral middle frontal volume: r=0.27, P=.03). Conclusions These findings suggest that EVO Monitor, an unsupervised, video game–based digital program integrated with adaptive mechanics, is a clinically valuable approach to measuring cognitive performance in patients with multiple sclerosis. Trial Registration ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618

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Borycki, E. M., and A. W. Kushniruk. "Towards an Integrative Cognitive-Socio-Technical Approach in Health Informatics: Analyzing Technology-Induced Error Involving Health Information Systems to Improve Patient Safety." Open Medical Informatics Journal 4, no. 1 (September 15, 2010): 181–87. http://dx.doi.org/10.2174/1874431101004010181.

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The purpose of this paper is to argue for an integration of cognitive and socio-technical approaches to assessing the impact of health information systems. Historically, health informatics research has examined the cognitive and socio-technical aspects of health information systems separately. In this paper we argue that evaluations of health information systems should consider aspects related to cognition as well as socio-technical aspects including impact on workflow (i.e. an integrated view). Using examples from the study of technology-induced error in healthcare, we argue for the use of simulations to evaluate the cognitive-socio-technical impacts of health information technology [36]. Implications of clinical simulations and analysis of cognitive-social-technical impacts are discussed within the context of the system development life cycle to improve health information system design, implementation and evaluation.

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Hu, Mingyue, Xinhui Shu, Gang Yu, Xinyin Wu, Maritta Välimäki, and Hui Feng. "A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study." Journal of Medical Internet Research 23, no. 2 (February 24, 2021): e20298. http://dx.doi.org/10.2196/20298.

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Background Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging. Objective We aimed to build a prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition. Methods A prospective cohort of 6718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) register, followed between 2008 and 2011, was used to develop and validate the prediction model. Participants were included if they were aged 60 years or above, were community-dwelling elderly people, and had a cognitive Mini-Mental State Examination (MMSE) score ≥18. They were excluded if they were diagnosed with a severe disease (eg, cancer and dementia) or were living in institutions. Cognitive impairment was identified using the Chinese version of the MMSE. Several machine learning algorithms (random forest, XGBoost, naïve Bayes, and logistic regression) were used to assess the 3-year risk of developing cognitive impairment. Optimal cutoffs and adjusted parameters were explored in validation data, and the model was further evaluated in test data. A nomogram was established to vividly present the prediction model. Results The mean age of the participants was 80.4 years (SD 10.3 years), and 50.85% (3416/6718) were female. During a 3-year follow-up, 991 (14.8%) participants were identified with cognitive impairment. Among 45 features, the following four features were finally selected to develop the model: age, instrumental activities of daily living, marital status, and baseline cognitive function. The concordance index of the model constructed by logistic regression was 0.814 (95% CI 0.781-0.846). Older people with normal cognitive functioning having a nomogram score of less than 170 were considered to have a low 3-year risk of cognitive impairment, and those with a score of 170 or greater were considered to have a high 3-year risk of cognitive impairment. Conclusions This simple and feasible cognitive impairment prediction model could identify community-dwelling elderly people at the greatest 3-year risk for cognitive impairment, which could help community nurses in the early identification of dementia.

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Wang, Yingxu, Bernard Widrow, Lotfi A. Zadeh, Newton Howard, Sally Wood, Virendrakumar C. Bhavsar, Gerhard Budin, et al. "Cognitive Intelligence." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 4 (October 2016): 1–20. http://dx.doi.org/10.4018/ijcini.2016100101.

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The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

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Timpka, T., and M. Bång. "Cognitive Tools in Medical Teamwork: The Spatial Arrangement of Patient Records." Methods of Information in Medicine 42, no. 04 (2003): 331–36. http://dx.doi.org/10.1055/s-0038-1634226.

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Summary Objectives: As a preliminary for the design of Computer-Based Patient Records, the aim of this paper is to build an understanding of the roles physical artifacts like paper-based patient records play in supporting cognition and collaboration in the health-care settings. Method: A small ethnographically-informed study was conducted in the emergency room at a 250-bed hospital in Sweden from the perspective of Distributed Cognition. Results: To track work-in-progress, clinicians placed patient records on a desk to form a shared public display that represented the current problem state for the health-care team. The results of the study suggest that the patient records and other physical artifacts are used by clinicians in different ways to form cognitive tools that offload memory tasks and support joint attention and collaboration. Conclusion: To design Computer-Based Patient Records that more appropriately support cognition and teamwork, it is important to investigate how clinicians make use of the paper-based patient records. Practitioners take advantage of existing tools frequently to deal with cognitively demanding tasks and collaboration issues.

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Kang, Jae Myeong, Nambeom Kim, Sook Young Lee, Soo Kyun Woo, Geumjin Park, Byeong Kil Yeon, Jung Woon Park, et al. "Effect of Cognitive Training in Fully Immersive Virtual Reality on Visuospatial Function and Frontal-Occipital Functional Connectivity in Predementia: Randomized Controlled Trial." Journal of Medical Internet Research 23, no. 5 (May 6, 2021): e24526. http://dx.doi.org/10.2196/24526.

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Background Cognitive training can potentially prevent cognitive decline. However, the results of recent studies using semi-immersive virtual reality (VR)-assisted cognitive training are inconsistent. Objective We aimed to examine the hypothesis that cognitive training using fully immersive VR, which may facilitate visuospatial processes, could improve visuospatial functioning, comprehensive neuropsychological functioning, psychiatric symptoms, and functional connectivity in the visual brain network in predementia. Methods Participants over 60 years old with subjective cognitive decline or mild cognitive impairment from a memory clinic were randomly allocated to the VR (n=23) or the control (n=18) group. The VR group participants received multidomain and neuropsychologist-assisted cognitive training in a fully immersive VR environment twice a week for 1 month. The control group participants did not undergo any additional intervention except for their usual therapy such as pharmacotherapy. Participants of both groups were evaluated for cognitive function using face-to-face comprehensive neuropsychological tests, including the Rey-Osterrieth Complex Figure Test (RCFT) copy task; for psychiatric symptoms such as depression, apathy, affect, and quality of life; as well as resting-state functional magnetic resonance imaging (rsfMRI) at baseline and after training. Repeated-measures analysis of variance was used to compare the effect of cognitive training between groups. Seed-to-voxel–based analyses were used to identify the cognitive improvement–related functional connectivity in the visual network of the brain. Results After VR cognitive training, significant improvement was found in the total score (F1,39=14.69, P=.001) and basic components score of the RCFT copy task (F1,39=9.27, P=.005) compared with those of the control group. The VR group also showed improvements, albeit not significant, in naming ability (F1,39=3.55, P=.07), verbal memory delayed recall (F1,39=3.03, P=.09), and phonemic fluency (F1,39=3.08, P=.09). Improvements in psychiatric symptoms such as apathy (F1,39=7.02, P=.01), affect (F1,39=14.40, P=.001 for positive affect; F1,39=4.23, P=.047 for negative affect), and quality of life (F1,39=4.49, P=.04) were found in the VR group compared to the control group. Improvement in the RCFT copy task was associated with a frontal-occipital functional connectivity increase revealed by rsfMRI in the VR group compared to the control group. Conclusions Fully immersive VR cognitive training had positive effects on the visuospatial function, apathy, affect, quality of life, and increased frontal-occipital functional connectivity in older people in a predementia state. Future trials using VR cognitive training with larger sample sizes and more sophisticated designs over a longer duration may reveal greater improvements in cognition, psychiatric symptoms, and brain functional connectivity. Trial Registration Clinical Research Information Service KCT0005243; https://tinyurl.com/2a4kfasa

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Hettinger, A. Zachary, Emilie M. Roth, and Ann M. Bisantz. "Cognitive engineering and health informatics: Applications and intersections." Journal of Biomedical Informatics 67 (March 2017): 21–33. http://dx.doi.org/10.1016/j.jbi.2017.01.010.

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Middleton, Rod M., Owen R. Pearson, Gillian Ingram, Elaine M. Craig, William J. Rodgers, Hannah Downing-Wood, Joseph Hill, et al. "A Rapid Electronic Cognitive Assessment Measure for Multiple Sclerosis: Validation of Cognitive Reaction, an Electronic Version of the Symbol Digit Modalities Test." Journal of Medical Internet Research 22, no. 9 (September 23, 2020): e18234. http://dx.doi.org/10.2196/18234.

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Background Incorporating cognitive testing into routine clinical practice is a challenge in multiple sclerosis (MS), given the wide spectrum of both cognitive and physical impairments people can have and the time that testing requires. Shortened paper and verbal assessments predominate but still are not used routinely. Computer-based tests are becoming more widespread; however, changes in how a paper test is implemented can impact what exactly is being assessed in an individual. The Symbol Digit Modalities Test (SDMT) is one validated test that forms part of the cognitive batteries used in MS and has some computer-based versions. We developed a tablet-based SDMT variant that has the potential to be ultimately deployed to patients’ own devices. Objective This paper aims to develop, validate, and deploy a computer-based SDMT variant, the Cognition Reaction (CoRe) test, that can reliably replicate the characteristics of the paper-based SDMT. Methods We carried out analysis using Pearson and intraclass correlations, as well as a Bland-Altman comparison, to examine consistency between the SDMT and CoRe tests and for test-retest reliability. The SDMT and CoRe tests were evaluated for sensitivity to disability levels and age. A novel metric in CoRe was found: question answering velocity could be calculated. This was evaluated in relation to disability levels and age for people with MS and compared with a group of healthy control volunteers. Results SDMT and CoRe test scores were highly correlated and consistent with 1-month retest values. Lower scores were seen in patients with higher age and some effect was seen with increasing disability. There was no learning effect evident. Question answering velocity demonstrated a small increase in speed over the 90-second duration of the test in people with MS and healthy controls. Conclusions This study validates a computer-based alternative to the SDMT that can be used in clinics and beyond. It enables accurate recording of elements of cognition relevant in MS but offers additional metrics that may offer further value to clinicians and people with MS.

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Cotelli, Maria, Rosa Manenti, Michela Brambilla, Elena Gobbi, Clarissa Ferrari, Giuliano Binetti, and Stefano F. Cappa. "Cognitive telerehabilitation in mild cognitive impairment, Alzheimer’s disease and frontotemporal dementia: A systematic review." Journal of Telemedicine and Telecare 25, no. 2 (November 8, 2017): 67–79. http://dx.doi.org/10.1177/1357633x17740390.

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Introduction Given the limited effectiveness of pharmacological treatments, non-pharmacological interventions in neurodegenerative diseases have gained increasing attention in recent years and telerehabilitation has been proposed as a cognitive rehabilitation strategy. The purpose of this systematic review is to examine the evidence for the efficacy of cognitive telerehabilitation interventions compared with face-to-face rehabilitation in patients with mild cognitive impairment, Alzheimer’s disease and frontotemporal dementia. Methods In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of the Medline database was conducted. Out of 14 articles assessed for eligibility, five studies were identified, three in participants with mild cognitive impairment or Alzheimer’s disease, two in patients with primary progressive aphasia. Results The Physiotherapy Evidence Database scale was used to assess the methodological quality of four out of five studies included in this systematic review, with only one report receiving a high-quality rating. Effect-size analysis evidenced positive effects of telerehabilitation interventions, comparable with those reported for face-to-face rehabilitation. Discussion The available evidence for the effectiveness of cognitive telerehabilitation is limited, and the quality of the evidence needs to be improved. The systematic review provides preliminary evidence suggesting that cognitive telerehabilitation for neurodegenerative disease may have comparable effects as conventional in-person cognitive rehabilitation.

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Robert, Philippe, Valeria Manera, Alexandre Derreumaux, Marion Ferrandez Y Montesino, Elsa Leone, Roxane Fabre, and Jeremy Bourgeois. "Efficacy of a Web App for Cognitive Training (MeMo) Regarding Cognitive and Behavioral Performance in People With Neurocognitive Disorders: Randomized Controlled Trial." Journal of Medical Internet Research 22, no. 3 (March 11, 2020): e17167. http://dx.doi.org/10.2196/17167.

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Background Cognitive and behavioral symptoms are the clinical hallmarks of neurocognitive disorders. Cognitive training may be offered to reduce the risks of cognitive decline and dementia and to reduce behavioral symptoms, such as apathy. Information and communication technology approaches, including serious games, can be useful in improving the playful aspect of computerized cognitive training and providing motivating solutions in elderly patients. Objective The objective of this study was to assess the effectiveness of employing the MeMo (Memory Motivation) Web app with regard to cognitive and behavioral symptoms in patients with neurocognitive disorders. Methods MeMo is a Web app that can be used on any Web browser (computer or tablet). The training activities proposed in MeMo are divided into the following two parts: memory and mental flexibility/attention. The study included 46 individuals (mean age 79.4 years) with a diagnosis of neurocognitive disorders at the Institut Claude Pompidou Memory Center in Nice. This randomized controlled study compared the evolution of cognition and behavior between patients not using MeMo (control group) and patients using MeMo (MeMo group) for 12 weeks (four sessions per week). Patients underwent memory and attention tests, as well as an apathy assessment at baseline, week 12 (end of the training period), and week 24 (12 weeks after the end of the training sessions). In addition, to assess the impact of high and low game uses, the MeMo group was divided into patients who used MeMo according to the instructions (about once every 2 days; active MeMo group) and those who used it less (nonactive MeMo group). Results When comparing cognitive and behavioral scores among baseline, week 12, and week 24, mixed model analysis for each cognitive and behavioral score indicated no significant interaction between testing time and group. On comparing the active MeMo group (n=9) and nonactive MeMo group (n=13), there were significant differences in two attention tests (Trial Making Test A [P=.045] and correct Digit Symbol Substitution Test items [P=.045]) and in the Apathy Inventory (AI) (P=.02). Mixed analysis (time: baseline, week 12, and week 24 × number of active days) indicated only one significant interaction for the AI score (P=.01), with a significant increase in apathy in the nonactive MeMo group. Conclusions This study indicates that the cognitive and behavioral efficacies of MeMo, a Web-based training app, can be observed only with regular use of the app. Improvements were observed in attention and motivation. Trial Registration ClinicalTrials.gov NCT04142801; https://clinicaltrials.gov/ct2/show/NCT04142801

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Chan, Joyce Y. C., Adrian Wong, Brian Yiu, Hazel Mok, Patti Lam, Pauline Kwan, Amany Chan, Vincent C. T. Mok, Kelvin K. F. Tsoi, and Timothy C. Y. Kwok. "Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study." Journal of Medical Internet Research 22, no. 12 (December 18, 2020): e17332. http://dx.doi.org/10.2196/17332.

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Background A digital cognitive test can be a useful and quick tool for the screening of cognitive impairment. Previous studies have shown that the diagnostic performance of digital cognitive tests is comparable with that of conventional paper-and-pencil tests. However, the use of commercially available digital cognitive tests is not common in Hong Kong, which may be due to the high cost of the tests and the language barrier. Thus, we developed a brief and user-friendly digital cognitive test called the Electronic Cognitive Screen (EC-Screen) for the detection of mild cognitive impairment (MCI) and dementia of older adults. Objective The aim of this study was to evaluate the performance of the EC-Screen for the detection of MCI and dementia in older adults. Methods The EC-Screen is a brief digital cognitive test that has been adapted from the Rapid Cognitive Screen test. The EC-Screen uses a cloud-based platform and runs on a tablet. Participants with MCI, dementia, and cognitively healthy controls were recruited from research clinics and the community. The outcomes were the performance of the EC-Screen in distinguishing participants with MCI and dementia from controls, and in distinguishing participants with dementia from those with MCI and controls. The cohort was randomly split into derivation and validation cohorts based on the participants’ disease group. In the derivation cohort, the regression-derived score of the EC-Screen was calculated using binomial logistic regression. Two predictive models were produced. The first model was used to distinguish participants with MCI and dementia from controls, and the second model was used to distinguish participants with dementia from those with MCI and controls. Receiver operating characteristic curves were constructed and the areas under the curves (AUCs) were calculated. The performances of the two predictive models were tested using the validation cohorts. The relationship between the EC-Screen and paper-and-pencil Montreal Cognitive Assessment-Hong Kong version (HK-MoCA) was evaluated by the Pearson correlation coefficient. Results A total of 126 controls, 54 participants with MCI, and 63 participants with dementia were included in the study. In differentiating participants with MCI and dementia from controls, the AUC of the EC-Screen in the derivation and validation cohorts was 0.87 and 0.84, respectively. The optimal sensitivity and specificity in the derivation cohorts were 0.81 and 0.80, respectively. In differentiating participants with dementia from those with MCI and controls, the AUC of the derivation and validation cohorts was 0.90 and 0.88, respectively. The optimal sensitivity and specificity in the derivation cohort were 0.83 and 0.83, respectively. There was a significant correlation between the EC-Screen and HK-MoCA (r=–0.67, P<.001). Conclusions The EC-Screen is suggested to be a promising tool for the detection of MCI and dementia. This test can be self-administered or assisted by a nonprofessional staff or family member. Therefore, the EC-Screen can be a useful tool for case finding in primary health care and community settings.

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Chiriacescu, Vlad, Leen-Kiat Soh, and Duane F. Shell. "Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 1–25. http://dx.doi.org/10.4018/ijcini.2013100101.

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Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented.

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Zhong, Ning, Jiming Liu, and Yiyu Yao. "Introduction to brain informatics." Cognitive Systems Research 11, no. 1 (March 2010): 1–2. http://dx.doi.org/10.1016/j.cogsys.2008.08.008.

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Manchanda, M., and P. Mclaren. "Cognitive behaviour therapy via interactive video." Journal of Telemedicine and Telecare 4, no. 1_suppl (March 1998): 53–55. http://dx.doi.org/10.1258/1357633981931452.

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Interactive video has been identified as a potential delivery medium for psychotherapy. Interactive video may restrict the range of both verbal and non-verbal communication and consequently impede the development of a therapeutic relationship, thus influencing the process and outcome of therapy. A single case study explored the feasibility of the provision of cognitive behaviour therapy using interactive video with a client diagnosed a shaving mixed anxiety and depressive disorder. A range of outcome measures were included together with an independent psychiatric assessment prior to, and on completion of, therapy. Different levels of outcome were also examined: clinical, social, user views and administration. Outcome measures indicated a reduction in psychopathology and some modification of dysfunctional attitudes, with no apparent impairment of the working alliance.

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Gilmore-Bykovskyi, Andrea L., Laura M. Block, Lily Walljasper, Nikki Hill, Carey Gleason, and Manish N. Shah. "Unstructured clinical documentation reflecting cognitive and behavioral dysfunction: toward an EHR-based phenotype for cognitive impairment." Journal of the American Medical Informatics Association 25, no. 9 (June 27, 2018): 1206–12. http://dx.doi.org/10.1093/jamia/ocy070.

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Abstract Despite increased risk for negative outcomes, cognitive impairment (CI) is greatly under-detected during hospitalization. While automated EHR-based phenotypes have potential to improve recognition of CI, they are hindered by widespread under-diagnosis of underlying etiologies such as dementia—limiting the utility of more precise structured data elements. This study examined unstructured data on symptoms of CI in the acute-care EHRs of hip and stroke fracture patients with dementia from two hospitals. Clinician reviewers identified and classified unstructured EHR data using standardized criteria. Relevant narrative text was descriptively characterized and evaluated for key terminology. Most patient EHRs (90%) had narrative text reflecting cognitive and/or behavioral dysfunction common in CI that were reliably classified (κ 0.82). The majority of statements reflected vague descriptions of cognitive/behavioral dysfunction as opposed to diagnostic terminology. Findings from this preliminary derivation study suggest that clinicians use specific terminology in unstructured EHR fields to describe common symptoms of CI. This terminology can inform the design of EHR-based phenotypes for CI and merits further investigation in more diverse, robustly characterized samples.

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Wang, Yingxu, Victor Raskin, Julia Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, and Newton Howard. "Cognitive Computing." International Journal of Software Science and Computational Intelligence 10, no. 1 (January 2018): 1–14. http://dx.doi.org/10.4018/ijssci.2018010101.

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Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of cognitive computing.

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Mastrian, Kathleen, and Dee McGonigle. "Cognitive informatics: An essential component of nursing technology design." Nursing Outlook 56, no. 6 (November 2008): 332–33. http://dx.doi.org/10.1016/j.outlook.2008.09.010.

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Yiyu Yao. "Interpreting Concept Learning in Cognitive Informatics and Granular Computing." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 4 (August 2009): 855–66. http://dx.doi.org/10.1109/tsmcb.2009.2013334.

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Journal articles on the topic 'Cognitive informatics'

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