Patient Benefactor Linker
Patient Benefactor Linker
Keywords:Artificial Neural Network, RGB color sensor module, Infrared spectroscopy, MATLAB Neural Network Toolbox, Optimal benefactor-donor match
This paper discusses a new evolution in the healthcare sector through a device by investigating the principle application of Artificial Neural Networks (ANN) for the selection of an optimal benefactor-donor match in organ transplantation. The device aims to correlate ABO blood type, age and bone density of healthy subjects. Firstly, linker phase integrates a light intensity(lux)
meter and an RGB Color Sensor module to perform an experimental observation of agglutination of RBC's which is measured through a halogen illumination source that measures the light intensity which is displayed on a screen through the icroprocessor interface. Secondly, we aim to study the possibility of calcium quantification via near-infrared spectroscopy to estimate bone density which involves the use of an emitting source and a photodiode as a detector/receiver. At last the device involves designing an Artificial Neural Network (ANN) model through the Neural Network Toolbox of MATLAB software to get the optimal network architecture suitable for the analysis. This architecture is achieved by simulating different Artificial Neural Network (ANN) configurations. We used a non-linear ANN which can predict benefactor and patient organ matches, while measuring ABO blood typing and calcium density of the donors in real time and for recognizing mapping functions for which there is no requirement for a particular basis of functions. A database was created through an intensive survey of benefactor profiles. The results generated by ANN are promising for identifying optimal benefactor and patient matches. This approach has potential benefits as an increase in the number of input and parameters will provide better matches and risk associated with human error are reduced. The network can further be modelled to predict survival rates.
Simon, DM.Levin, S., 2001. Infectious Complications of Solid Organ Transplantations.
Infectious Disease Clinics of North America, 15(2), 521-549
Nilsson, J. Ohlsson, M. Höglund, P. Ekmehag, B. Koul, B. Andersson, B., 2015. The
International Heart Transplant Survival Algorithm (IHTSA): A New Model to Improve
Organ Sharing and Survival. PLoS ONE-Public Library of Science, [online] Available at
<https://doi.org/10.1371/journal.pone.0118644> [Accessed 10 October 2017]
Opelz, G. Wujciak, T. 1994. The influence of HLA compatibility on graft survival after
heart transplantation. The Collaborative Transplant Study. The New England Journal of
Medicine, 330(12), 816-819.
Costanzo, MR. Costanzo, MR. Dipchand, A. Starling, R. Anderson, A. Chan, M et al .2010.The
International Society of Heart and Lung Transplantation Guidelines for the care of heart
transplant recipients. J Heart Lung Transplant, 29(8), 914–956
Russo, MJ. Iribarne, A. Hong, KN. Ramlawi, B. Chen, JM. Takayama, H et al., 2010. Factors
associated with primary graft failure after heart transplantation. Transplantation, 90(4),
West, LJ.Karamlou, T. Dipchand, A. Pollock-Barziv, SM. Coles, JG. McCrindle, BW. 2006.
Impact on outcomes after listing and transplantation, of a strategy to accept ABO blood
group-incompatible donor hearts for neonates and infants. The Journal of Thoracic and
Cardiovascular Surgery, 131(2), 455-461
Cooper, D.K, 1990. Clinical survey of heart transplantation between ABO blood groupincompatible
recipients and donors. The Journal of Heart Transplantation, 9(4), 376-381.
West, LJ. Pollock-Barziv, SM. Dipchand, AI. Lee, KJ. Cardella, CJ.Benson, LN et al. 2001. ABO-incompatible heart transplantation in infants. The New England Journal of Medicine,
Mutimer, DJ. Gunson, B. Chen, J. Berenguer, J. Neuhaus, P. Castaing, D. Garcia-Valdecasas,
J C. Salizzoni, M M. G E. Mirza, D., 2006.Impact of Donor Age and Year of Transplantation
on Graft and Patient Survival Following Liver Transplantation for Hepatitis C Virus.
Transplantation, 81(1), 7-14.
Torres, A. Lorenzo, V. Salido, E. 2002.Calcium Metabolism and Skeletal Problems after
Transplantation. Journal of the American Society of Nephrology, 13(2), 551-558.
Foundation, NK. 2003. K/DOQI clinical practice guidelines for bone metabolism and
disease in chronic kidney disease. American Journal of Kidney Diseases, 42(4 Suppl 3):
Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford: Clarendon
Heden B, Ohlin H, Rittner R, Edenbrandt L.1997. Acute myocardial infarction detected in
the 12-lead ECG by artificial neural networks. Circulation. 96(6), 1798-1802
Silipo R, Gori M, Taddei A, Varanini M, Marchesi C.1995. Classification of arrhythmic
events in ambulatory electrocardiogram, using artificial neural networks. Computers and
Biomedical Research. 28,305–318.
Ashizawa K, ET al.1999. Artificial neural networks in chest radiography: application to the
differential diagnosis of interstitial lung disease. Academic Radiology, 6, 2–9.
Abdolmaleki P, ET al.1997. Neural network analysis of breast cancer from MRI findings.
Radiation Medicine.15, 283–293.
Chang, YJ. Chang, W. Lin, YT.2014. Detection of RBC agglutination in blood typing test using
integrated Light-Eye-Technology (iLeyeT).International Symposium on Bioelectronics
and Bioinformatics, IEEE.[e-journal] http://dx.doi.org/ 10.1109/ISBB.2014.6820952
Niemann, C. Divol, L. Froula, D. Glanzer, S. Gregori, G. Kirkwood, R. Mackinnon, A. Meezan,
N. Moody, J. Source, C. Bahr, R. Seka, W.2005.IEEE International Conference on Plasma
Weiss ET al.2009.Impact of recipient body mass index on organ allocation and mortality
in orthotopic heart transplantation. Journal of Heart and Lung Transplantation. 28(11),
Russo, M. J. et al. 2007. The effect of ischemic time on survival after heart transplantation
varies by donor age: an analysis of the United Network for Organ Sharing database. The
Journal of Thoracic and Cardiovascular Surgery.133, 554-559.
Smith, J. D., Rose, M. L., Pomerance, A., Burke, M. Yacoub, M.H.1995. Reduction of cellular
rejection and increase in longer-term survival after heart transplantation after HLA-DR
matching. Lancet 346, 1318–1322
Kilic, A. et al. 2012. What predicts long-term survival after heart transplantation? An
analysis of 9,400 ten-year survivors. The Annals of Thoracic Surgery.93, 699–704
Winslow, R. L., Trayanova, N., Geman, D. Miller, M. I.2012. Computational medicine:
translating models to clinical care. Science Translational Medicine, 4(158) [e-journal]
Albanese, CV. Diessel, E. Genant, HK.2003. Clinical applications of body composition
measurements using DXA. Journal of Clinical Densitometry.6 (2), 75-85.
Chaichanakol, S. Tanaka, SM. Khantachawana, A. 2016. Quantitative Detection of Calcium
using Near Infrared Spectroscopy for apply in Bone Densitometry. International Journal of
Mechanical and Production Engineering.4 (4).
Padalkara, M.V. Pleshkoa, N. 2015. Wavelength-Dependent Penetration Depth of Near
Infrared Radiation into Cartilage.Analyst.140 (7), 2093-2100.
Branch, D.R. “Anti-A and anti-B: what are they and where do they come from?” Transfusion,
vol. 55, pp.S74-S79, July 2015.