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Journal articles on the topic 'Informatics knowledge'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Bullard, M. J., S. D. Emond, T. A. D. Graham, K. Ho, and B. R. Holroyd. "Informatics and Knowledge Translation." Academic Emergency Medicine 14, no. 11 (2007): 996–1002. http://dx.doi.org/10.1197/j.aem.2007.06.032.

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2

Meyer, Eric T., Kalpana Shankar, Matthew Willis, Sarika Sharma, and Steve Sawyer. "The social informatics of knowledge." Journal of the Association for Information Science and Technology 70, no. 4 (2019): 307–12. http://dx.doi.org/10.1002/asi.24205.

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3

Dalrymple, Prudence W. "Data, information, knowledge: The emerging field of health informatics." Bulletin of the American Society for Information Science and Technology 37, no. 5 (2011): 41–44. http://dx.doi.org/10.1002/bult.2011.1720370512.

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4

Humphreys, Betsy L., and Donald A. B. Lindberg. "Preface - Access to Knowledge Revisited." Yearbook of Medical Informatics 25, S 01 (2016): S18—S20. http://dx.doi.org/10.15265/iys-2016-s026.

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Summary Objective: To review and update the Preface to the 1998 Yearbook of Medical Informatics, which had as its Special Topic “Health Informatics and the Internet”. Method: Assessment of the accuracy of predictions made in 1998 and consideration of key developments in informatics since that time. Results: Predictions made in 1998 were generally accurate regarding reduced dependence on keyboards, expansion of multimedia, medical data privacy policy development, impact of molecular biology on knowledge and treatment of neoplasms, and use of imaging and informatics to advance understanding of brain structure and function. Key developments since 1998 include the huge increase in publicly available electronic information; acknowledgement by leaders in government and science of the importance of biomedical informatics to societal goals for health, health care, and scientific discovery; the influence of the public in promoting clinical research transparency and free access to government-funded research results; the long-awaited arrival of electronic health records; and the “Cloud” as a 21st century reformulation of contracting out the computer center. Conclusions: There are many challenging and important problems that deserve the attention of the informatics community. Informatics researchers will be best served by embracing a very broad definition of medical informatics and by promoting public understanding of the field.

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Ronquillo, Charlene, Leanne M. Currie, and Paddy Rodney. "The Evolution of Data-Information-Knowledge-Wisdom in Nursing Informatics." Advances in Nursing Science 39, no. 1 (2016): E1—E18. http://dx.doi.org/10.1097/ans.0000000000000107.

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6

Lewis, Alex. "Health informatics: information and communication." Advances in Psychiatric Treatment 8, no. 3 (2002): 165–71. http://dx.doi.org/10.1192/apt.8.3.165.

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In health care, the word ‘communication’ covers a wide range of interactions, including interpersonal communication, communication technology, medical education, health policy and mass communication. It takes many forms, from a brief informal talk between colleagues to formalised written documents between professionals. The essence of this verbal and written communication is the sharing of information. To make our information exchange more useful and to give it more meaning, the information communicated needs an appropriate framework. For example, the meaning of the diagnosis ‘schizophrenia’ is greatly enhanced by knowledge of the individual patient within the context (the framework) of his or her past history and family background.

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van Bemmel, Jan H. "Knowledge for Medicine and Health Care." Methods of Information in Medicine 44, no. 04 (2005): 596–600. http://dx.doi.org/10.1055/s-0038-1634012.

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SummaryDr. Donald A. B. Lindberg, Director of the U.S. National Library of Medicine, received an honorary doctorate from UMIT, the University for Health Sciences, Medical Informatics and Technology in Innsbruck, Tyrol. The celebration took place on September 28, 2004 at an academic event during a conference of the Austrian, German, and Swiss Societies of Medical Informatics, GMDS2004. Dr. Lindberg has been a pioneer in the field of computers in health care from the early 1960s onwards. In 1984 he became the Director of the National Library of Medicine in Bethesda, the world’s largest fully computerized biomedical library. Dr. Lind-berg has been involved in the early activities of the International Medical Informatics Association (IMIA), among others being the chair of the Organizing Committee for MEDINFO 86 in Washington D.C. He was elected the first president of the American Medical Informatics Association (AMIA), and served as an editor of Methods of Information in Medicine.

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Matney, Susan, Philip J. Brewster, Katherine A. Sward, Kristin G. Cloyes, and Nancy Staggers. "Philosophical Approaches to the Nursing Informatics Data-Information-Knowledge-Wisdom Framework." Advances in Nursing Science 34, no. 1 (2011): 6–18. http://dx.doi.org/10.1097/ans.0b013e3182071813.

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9

Zhu, Yongjun, Chao Che, Bo Jin, Ningrui Zhang, Chang Su, and Fei Wang. "Knowledge-driven drug repurposing using a comprehensive drug knowledge graph." Health Informatics Journal 26, no. 4 (2020): 2737–50. http://dx.doi.org/10.1177/1460458220937101.

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Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.

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Hume, Margee, Jeffrey Soar, S. Jonathan Whitty, Craig Hume, Faeka El Sayed, and Paul Johnston. "Aged Care Informatics." International Journal of Enterprise Information Systems 10, no. 2 (2014): 1–20. http://dx.doi.org/10.4018/ijeis.2014040101.

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Aged care is projected to be the fastest-growing sector within health and community care industries Strengthening the care-giving workforce, compliance, delivery and technology is not only vital to our social infrastructure and improving the quality of care, but also has the potential to drive long-term economic growth and contribute to the GDP. This paper examines the role of knowledge management (KM) in aged care organizations to assist in the delivery of aged care. With limited research related to KM in aged care, this paper advances knowledge and offers a unique view of KM from the perspective of 22 aged care stakeholders. Using in-depth interviewing, this paper explores the definition of knowledge in aged care facilities, the importance of knowledge planning, capture and diffusion for accreditation purposes and offers recommendations for the development of sustainable knowledge management practice and development. The paper culminates in an offering a checklist for aged care facilities and advances the discourse in this sector.

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11

Vimarlund, Vivian, Toomas Timpka, and Niklas Hallberg. "Healthcare professional’s demand for knowledge in informatics." International Journal of Medical Informatics 53, no. 2-3 (1999): 107–14. http://dx.doi.org/10.1016/s1386-5056(98)00151-8.

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12

Medford, Andrew J., M. Ross Kunz, Sarah M. Ewing, Tammie Borders, and Rebecca Fushimi. "Extracting Knowledge from Data through Catalysis Informatics." ACS Catalysis 8, no. 8 (2018): 7403–29. http://dx.doi.org/10.1021/acscatal.8b01708.

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13

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

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14

Scherffius, Jo Ann. "Nursing Informatics and the Foundation of Knowledge." AORN Journal 89, no. 4 (2009): 777–78. http://dx.doi.org/10.1016/j.aorn.2009.03.014.

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15

Wang, Yingxu, Gabriele Fariello, Marina L. Gavrilova, et al. "Perspectives on Cognitive Computers and Knowledge Processors." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (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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16

van Bemmel, Jan H., Erik M. van Mulligen, Barend Mons, Marc van Wijk, Jan A. Kors, and Johan van der Lei. "Databases for knowledge discovery." International Journal of Medical Informatics 75, no. 3-4 (2006): 257–67. http://dx.doi.org/10.1016/j.ijmedinf.2005.08.012.

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17

Househ, M. S. "Leading Knowledge Producers Within the Biomedical Informatics Community." Journal of Medical Systems 30, no. 4 (2006): 237–40. http://dx.doi.org/10.1007/s10916-005-9002-z.

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18

ISHII, Hirotoyo. "A prospect of knowledge informatics through redefinition of library and information science." Journal of Information Processing and Management 54, no. 7 (2011): 387–99. http://dx.doi.org/10.1241/johokanri.54.387.

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19

Virapongse, Arika, Ruth Duerr, and Elizabeth Covelli Metcalf. "Knowledge mobilization for community resilience: perspectives from data, informatics, and information science." Sustainability Science 14, no. 4 (2018): 1161–71. http://dx.doi.org/10.1007/s11625-018-0612-z.

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20

Westra, Bonnie L., Lisiane Pruinelli, and Connie W. Delaney. "Nursing Knowledge." CIN: Computers, Informatics, Nursing 33, no. 10 (2015): 427–31. http://dx.doi.org/10.1097/cin.0000000000000191.

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21

Kalra, Dipak. "Health informatics 3.0." Yearbook of Medical Informatics 20, no. 01 (2011): 8–14. http://dx.doi.org/10.1055/s-0038-1638730.

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SummaryWeb 3.0 promises us smart computer services that will interact with each other and leverage knowledge about us and our immediate context to deliver prioritised and relevant information to support decisions and actions. Healthcare must take advantage of such new knowledge-integrating services, in particular to support better cooperation between professionals of different disciplines working in different locations, and to enable well-informed co-operation between clinicians and patients. To grasp the potential of Web 3.0 we will need well-harmonised semantic resources that can richly connect virtual teams and link their strategies to real-time and tailored evidence. Facts, decision logic, care pathway steps, alerts, education need to be embedded within components that can interact with multiple EHR systems and services consistently. Using Health Informatics 3.0 a patient’s current situation could be compared with the outcomes of very similar patients (from across millions) to deliver personalised care recommendations. The integration of EHRs with biomedical sciences (‘omics) research results and predictive models such as the Virtual Physiological Human could help speed up the translation of new knowledge into clinical practice. The mission, and challenge, for Health Informatics 3.0 is to enable healthy citizens, patients and professionals to collaborate within a knowledge-empowered social network in which patient specific information and personalised real-time evidence are seamlessly interwoven.

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22

Doyle, Jacqueline D. "Knowledge-Based Information Management." Medical Reference Services Quarterly 13, no. 2 (1994): 85–97. http://dx.doi.org/10.1300/j115v13n02_08.

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23

Fox, Brent I., Allen J. Flynn, Christopher R. Fortier, and Kevin A. Clauson. "Knowledge, Skills, and Resources for Pharmacy Informatics Education." American Journal of Pharmaceutical Education 75, no. 5 (2011): 93. http://dx.doi.org/10.5688/ajpe75593.

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24

Lushington, Gerald, Yinghua Dong, and Bhargav Theertham. "Chemical Informatics and the Drug Discovery Knowledge Pyramid." Combinatorial Chemistry & High Throughput Screening 16, no. 10 (2013): 764–76. http://dx.doi.org/10.2174/1386207311301010006.

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25

Heathfield, H., and G. Louw. "New challenges for clinical informatics: knowledge management tools." Health Informatics Journal 5, no. 2 (1999): 67–73. http://dx.doi.org/10.1177/146045829900500203.

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26

Buchan, I. E., and R. Hanka. "Exchanging clinical knowledge via Internet." International Journal of Medical Informatics 47, no. 1-2 (1997): 39–41. http://dx.doi.org/10.1016/s1386-5056(97)00084-1.

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27

Ginman, Mariam, Stefan Ek, Kristina Eriksson-Backa, et al. "Health Communication and Knowledge Construction." Health Informatics Journal 9, no. 4 (2003): 301–13. http://dx.doi.org/10.1177/1460458203094007.

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28

Gu, Dongxiao, Xuejie Yang, Shuyuan Deng, et al. "Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis." Journal of Medical Internet Research 22, no. 2 (2020): e15142. http://dx.doi.org/10.2196/15142.

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Background With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. Objective Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. Methods A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. Results The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. Conclusions Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud–based mobile health, cloud–based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

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29

Schulz, Stefan, and Udo Hahn. "Medical knowledge reengineering—converting major portions of the UMLS into a terminological knowledge base." International Journal of Medical Informatics 64, no. 2-3 (2001): 207–21. http://dx.doi.org/10.1016/s1386-5056(01)00201-5.

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30

Rosse, C., J. L. Mejino, B. R. Modayur, R. Jakobovits, K. P. Hinshaw, and J. F. Brinkley. "Motivation and Organizational Principles for Anatomical Knowledge Representation: The Digital Anatomist Symbolic Knowledge Base." Journal of the American Medical Informatics Association 5, no. 1 (1998): 17–40. http://dx.doi.org/10.1136/jamia.1998.0050017.

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31

Lanzola, G., S. Quaglini, and M. Stefanelli. "Knowledge-Acquisition Tools for Medical Knowledge-Based Systems." Methods of Information in Medicine 34, no. 01/02 (1995): 25–39. http://dx.doi.org/10.1055/s-0038-1634578.

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Abstract:Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

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32

Lane, Joseph P., and Vathsala I. Stone. "Level Of Knowledge Use Survey (LOKUS) instrument: Documenting knowledge use by stakeholders." Technology and Disability 28, no. 1,2 (2016): 13–18. http://dx.doi.org/10.3233/tad-160440.

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33

Chang, I.-Chiu, Chih-Yu Lin, Hsiao-Ting Tseng, and Wen-Yu Ho. "Health Knowledge Effects." CIN: Computers, Informatics, Nursing 34, no. 3 (2016): 137–42. http://dx.doi.org/10.1097/cin.0000000000000207.

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34

Crowell, Karen, and Julia Shawkokot. "Extending the Hand of Knowledge." Medical Reference Services Quarterly 22, no. 1 (2003): 1–9. http://dx.doi.org/10.1300/j115v22n01_01.

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35

Dwyer, Patricia, Valerie Hagerman, Chris-Anne Ingram, Ron MacFarlane, and Sherry McCourt. "Atlantic Telehealth Knowledge Exchange." Telemedicine Journal and e-Health 10, no. 1 (2004): 93–101. http://dx.doi.org/10.1089/153056204773644634.

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36

Sinha, Saurabh, Jun Song, Richard Weinshilboum, Victor Jongeneel, and Jiawei Han. "KnowEnG: a knowledge engine for genomics." Journal of the American Medical Informatics Association 22, no. 6 (2015): 1115–19. http://dx.doi.org/10.1093/jamia/ocv090.

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Abstract We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of “Knowledge Engine for Genomics” (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the “Knowledge Network” that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.

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37

Seeley, Helen, and Christine Urquhart. "Action research in developing knowledge networks." Health Informatics Journal 14, no. 4 (2008): 279–96. http://dx.doi.org/10.1177/1460458208096557.

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38

Stefanelli, M. "Knowledge Management to Support Performance-based Medicine." Methods of Information in Medicine 41, no. 01 (2002): 36–43. http://dx.doi.org/10.1055/s-0038-1634311.

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Summary Objectives: To discuss research issues for medical informatics in order to support the further development of health information systems, exploiting knowledge management and information and communication technology to increase the performance of Health Care Organizations (HCOs). Methods: Analyze the potential of exploiting knowledge management technology in medicine. Results and conclusions: The increasing pressure on HCOs to ensure efficiency and cost-effectiveness, balance the quality of care, and contain costs will drive them towards more effective management of medical knowledge derived from biomedical research. Knowledge management technology may provide effective methods and tools in speeding up the diffusion of innovative medical procedures. Reviews of the effectiveness of various methods of best practice dissemination show that the greatest impact is achieved when such knowledge is made accessible through the health information system at the moment it is required by care providers at their work sites. There is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support medical work. Thus, the great challenge for medical informatics is represented by the effective exploitation of the astonishing capabilities of new technologies to assure the conditions of knowledge management and organizational learning within HCOs.

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39

Brennan, Patricia Flatley, and Joyce J. Fitzpatrick. "On the Essential Integration of Nursing and Informatics." AACN Advanced Critical Care 3, no. 4 (1992): 797–803. http://dx.doi.org/10.4037/15597768-1992-4008.

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This paper asserts that nursing knowledge is fundamentally inseparable from the strategies and structures that represent it. Nursing informatics comprises a new disciplinary focus that results from a blend of nursing and informatics. The technologies of informatics, communications, computer science, decision science, human information processing, and knowledge engineering, provide critical care nurses with the support necessary for contemporary nursing practice. Informatics technologies enable nurses to communicate, process knowledge in new and more efficient ways, and better understand the nature of nursing thinking

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MURAKAWA, Takehiko. "Study Group of Informatics on Knowledge, Art, and Culture." Joho Chishiki Gakkaishi 28, no. 5 (2019): 350–51. http://dx.doi.org/10.2964/jsik_2019_011.

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41

Frenklach, Michael. "Transforming data into knowledge—Process Informatics for combustion chemistry." Proceedings of the Combustion Institute 31, no. 1 (2007): 125–40. http://dx.doi.org/10.1016/j.proci.2006.08.121.

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42

Parr, Cynthia S., Robert Guralnick, Nico Cellinese, and Roderic D. M. Page. "Evolutionary informatics: unifying knowledge about the diversity of life." Trends in Ecology & Evolution 27, no. 2 (2012): 94–103. http://dx.doi.org/10.1016/j.tree.2011.11.001.

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43

Embi, Peter J., Courtney Hebert, Gayle Gordillo, Kelly Kelleher, and Philip R. O. Payne. "Knowledge Management and Informatics Considerations for Comparative Effectiveness Research." Medical Care 51 (August 2013): S38—S44. http://dx.doi.org/10.1097/mlr.0b013e31829b1de1.

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44

Stroz, Edward M., and D. Frank Hsu. "Justice Informatics: Improving Knowledge Discovery for the Justice System." IT Professional 14, no. 5 (2012): 47–52. http://dx.doi.org/10.1109/mitp.2012.22.

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45

Brooijmans, Natasja, Dominick Mobilio, Gary Walker, et al. "A structural informatics approach to mine kinase knowledge bases." Drug Discovery Today 15, no. 5-6 (2010): 203–9. http://dx.doi.org/10.1016/j.drudis.2009.11.005.

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46

Reddington, Fiona, J. Max Wilkinson, Robin Clark, Helen Parkinson, Peter Kerr, and Richard Begent. "Cancer Informatics in the U.K.: The NCRI Informatics Initiative." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200027.

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The arrival of high-throughput technologies in cancer science and medicine has made the possibility for knowledge generation greater than ever before. However, this has brought with it real challenges as researchers struggle to analyse the avalanche of information available to them. A unique U.K.-based initiative has been established to promote data sharing in cancer science and medicine and to address the technical and cultural issues needed to support this.

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47

Tuttle, M. S., W. G. Cole, D. D. Sherertz, and S. J. Nelson. "Navigating to Knowledge." Methods of Information in Medicine 34, no. 01/02 (1995): 214–31. http://dx.doi.org/10.1055/s-0038-1634582.

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Abstract:One way to fulfill point-of-care knowledge needs is to present caregivers with a visual representation of the available “answers”. Using such a representation, caregivers can recognize what they want, rather than have to recall what they need, and then navigate to an appropriate answer. Given selected pieces of information from a computer-based patient record, an interface can anticipate certain knowledge needs by initializing caregiver navigation in a semantic neighborhood of answers likely to be relevant to the patient at hand. These notions draw heavily on two collaborative projects – the U.S. National Library of Medicine Unified Medical Language System® and the U.S. National Cancer Institute Knowledge Server. Both of these projects support navigation because they make the structure of medical knowledge explicit in a way that can be exploited by human interfaces.

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48

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 (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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49

Wylde, Margaret A. "Consumer knowledge of home modifications." Technology and Disability 8, no. 1-2 (1998): 51–68. http://dx.doi.org/10.3233/tad-1998-81-205.

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50

Lane, Joseph P. "The “Need to Knowledge” Model: An operational framework for knowledge translation and technology transfer." Technology and Disability 24, no. 3 (2012): 187–92. http://dx.doi.org/10.3233/tad-2012-0346.

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