Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support

Artificial Intelligence in Medicine - Tập 85 - Trang 28-42 - 2018
Daniel Brown1, Arantza Aldea1, Rachel Harrison1, Clare Martin1, Ian Bayley1
1Oxford Brookes University, Oxford, United Kingdom

Tài liệu tham khảo

Borus, 2010, Adherence challenges in the management of type 1 diabetes in adolescents: prevention and intervention, Curr Opin Pediatr, 22, 405, 10.1097/MOP.0b013e32833a46a7 Barnard, 2012, Use of an automated bolus calculator reduces fear of hypoglycemia and improves confidence in dosage accuracy in patients with type 1 diabetes mellitus treated with multiple daily insulin injections, J Diabetes Sci Technol, 6, 144, 10.1177/193229681200600117 Hall, 2009, The Weka data mining software: an update, ACM SIGKDD Explor Newsl, 11, 10, 10.1145/1656274.1656278 Richter, 2013 Kolodner, 1993 Schank, 1983 Hammond, 1986, CHEF: a model of case-based planning, 267 Kolodner, 1989, The MEDIATOR: analysis of an early case-based problem solver, Cogn Sci, 13, 507, 10.1207/s15516709cog1304_2 Koton, 1988, Reasoning about evidence in casual explanations, 256 Aamodt, 1994, Case-based reasoning: foundational issues, methodological variations, and system approaches, AI Commun, 7, 39, 10.3233/AIC-1994-7104 Jaczynski, 1997, A framework for the management of past experiences with time-extended situations, 32 Jære, 2002, Representing temporal knowledge for case-based prediction, 174 Sánchez-Marré, 2005, An approach for temporal case-based reasoning: episode-based reasoning, 465 Brown, 2013, Developing a mobile case-based reasoning application to assist type 1 diabetes management, 1 Brown, 2015 Martin, 2011, A systematic evaluation of mobile applications for diabetes management, 466 Kovatchev, 2009, In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes, J Diabetes Sci Technol, 3, 44, 10.1177/193229680900300106 Tukey, 1977 Witten, 2005 Campbell, 2012 Becton, Dickinson and Company, 2005 Davidson, 2003, Statistically based CSII parameters: correction factor, CF (1700 rule), carbohydrate-to-insulin ratio, CIR (2.8 rule), and basal-to-total ratio, Diabetes Technol Ther, 5, A237 University College London Hospitals, 2013 Davidson, 2008, Analysis of guidelines for basal-bolus insulin dosing: basal insulin, correction factor, and carbohydrate-to-insulin ratio, Endocr Pract, 14, 1095, 10.4158/EP.14.9.1095 King, 2007, A prospective evaluation of insulin dosing recommendations in patients with type 1 diabetes at near normal glucose control: bolus dosing, J Diabetes Sci Technol, 1, 42, 10.1177/193229680700100107 Guyon, 2003, An introduction to variable and feature selection, J Mach Learn Res, 3, 1157 Hall, 1999 Liu, 1995, Chi2: feature selection and discretization of numeric attributes, 388 Kerber, 1992, ChiMerge: discretization of numeric attributes, 123 Fayyad, 1993, Multi-interval discretization of continuous-valued attributes for classification learning, 1022 Rissanen, 1978, Modeling by shortest data description, Automatica, 14, 465, 10.1016/0005-1098(78)90005-5 Liu, 2002, Discretization: an enabling technique, Data Min Knowl Discov, 6, 393, 10.1023/A:1016304305535 Yu, 2003, Feature selection for high-dimensional data: a fast correlation-based filter solution, 856 Quinlan, 1993 Hall, 1999, Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper, 235 Mingers, 1989, An empirical comparison of selection measures for decision-tree induction, Mach Learn, 3, 319, 10.1007/BF00116837 Press, 1988 Holte, 1993, Very simple classification rules perform well on most commonly used dataset, Mach Learn, 11, 63, 10.1023/A:1022631118932 Nevill-Manning, 1995, The development of Holte's 1R classifier, 239 Kononenko, 1994, Estimating attributes: analysis and extensions of relief, 171 Kira, 1992, A practical approach to feature selection, 249 Farmer, 2008, The future of open and closed-loop insulin delivery for diabetes mellitus, J Pharmacy Pharmacol, 60, 1, 10.1211/jpp.60.1.0001 Roche, 2012 Clarke, 2009, Statistical tools to analyse continuous glucose monitor data, Diabetes Technol Ther, 11, S45, 10.1089/dia.2008.0138 Kovatchev, 1998, Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index, Diabetes Care, 21, 1870, 10.2337/diacare.21.11.1870 Kovatchev, 2006, Evaluation of a new measure of blood glucose variability in diabetes, Diabetes Care, 29, 2433, 10.2337/dc06-1085 Roche, 2015 Rodbard, 2009, Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technol Ther, 11, S55, 10.1089/dia.2008.0132 NICE, 2004 Bellazzi, 2002, A telemedicine support for diabetes management: the T-IDDM project, Comput Methods Programs Biomed, 69, 147, 10.1016/S0169-2607(02)00038-X Marling, 2008, Case-based decision support for patients with type 1 diabetes on insulin pump therapy Advances in case-based reasoning, Lect Notes Comput Sci, 5239, 325, 10.1007/978-3-540-85502-6_22 Herrero, 2015, Advanced insulin bolus advisor based on run-to-run control and case-based reasoning, IEEE J Biomed Health Inform, 19, 1087 Pesl, 2017, Case-based reasoning for insulin bolus advice: evaluation of case parameters in a six-week pilot study, J Diabetes Sci Technol, 11, 37, 10.1177/1932296816629986 Wess, 1993, Using k–d trees to improve the retrieval step in case-based reasoning, 167 Allen, 1983, Maintaining knowledge about temporal intervals, Commun ACM, 26, 832, 10.1145/182.358434 Branting, 1994, An empirical evaluation of model-based case matching and adaptation, 72