血液管理是健康照護的重要一環，也是醫療管理領域關注的重點。縱然領域中的學者已針對捐血行為進行廣泛的研究，而且提出的管理策略往往亦著重於如何留住首次捐血者，但多數研究並未深入分析捐血者之捐血行為軌跡。因此本研究使用隱馬可夫模型分析首次捐血者之回返行為模式，本研究以2010年至2014年台灣血液基金會台北捐血中心的2,000位捐血者之行為軌跡數據做為分析樣本。研究結果指出，運用隱馬可夫模型擷取每位捐血者之行為特徵，並採集群分析技術中的分割環繞物件法（partition around medoids，PAM），將捐血者拆解成四個群集，分別為：一次捐血者、持續捐血者、中途退出捐血者與消逝捐血者。此外，決策樹演算法之分類結果發現，管理者可以使用較短的監視週期對捐血者進行分類，訓練數據的準確性為87%，測試數據的準確度亦達85%。研究結果除在方法上提出一些創見，亦可在實務運用上提出一些重要建議。

Engaging voluntarily non-remunerated donors as an essential part of blood management has always been put into focus in medical science despite the advances of technology in the medical world. The most crucial element of these management strategies comes from retaining first-time donors. Although extensive studies regarding blood donation have been conducted, most of the studies did not consider using cluster analysis on data containing individual trajectories of donation sequence. This study aims to discover the overall blood donation pattern of the first-time donor who turns to become the committed donor using sequence clustering analysis. In particular, data from Taipei Blood Donation Center from the year 2010 to 2014 was used, and 2,000 trajectories of individual donors were sampled. Furthermore, instead of using raw sequence data for the analysis, the Hidden Markov Model (HMM) was utilized in converting trajectories first before clustering was conducted. Results from Partition Around Medoids (PAM) of the HMM parameters show that there are four clusters — one-time donor, committed donor, drop-out donor, and lapsed donor. Furthermore, the decision tree classifier shows that committed donors could be classified using shorter surveillance period with an accuracy of 87% in training data and 85% in testing data. Some implications for both research and practice fields are also present.

Aassve, A., Billari, F. C., & Piccarreta, R. (2007). Strings of adulthood: A sequence analysis of Young British Women’s work-family trajectories. European Journal of Population, 23(3–4), 369–388. https://doi.org/10.1007/s10680-007-9134-6
Abbott. (1995). Sequence Analysis: New Methods for Old Ideas. Annual Review of Sociology, 21, 93–113. https://doi.org/https://doi.org/10.1146/annurev.so.21.080195.000521
Abbott, A, & Hrycak, A. (1990). Measuring Resemblance in Sequence Data: An Optimal Matching Analysis of Musician Careers. American Journal of Sociology, 96(1), 144–185. https://doi.org/https://doi.org/10.1086/229495
Abbott, Andrew, & Tsay, A. (2000). Sequence analysis and optimal matching methods in sociology: Review and prospect. In Sociological Methods and Research (Vol. 29). https://doi.org/10.1177/0049124100029001001
Abdullah, Z., Herawan, T., Ahmad, N., & Deris, M. M. (2011). Extracting highly positive association rules from students’ enrollment data. Procedia-Social and Behavioral Sciences, 28, 107–111. https://doi.org/https://doi.org/10.1016/j.sbspro.2011.11.022
Aggarwal, S., & Sharma, V. (2012). Attitudes and problems related to voluntary blood donation in India: A short communication. Ann Trop Med Public Health, 5(1), 50–52. Retrieved from http://www.atmph.org/text.asp?2012/5/1/50/92885
Aisenbrey, S., & Fasang, A. E. (2010). New Life for Old Ideas: The “Second Wave” of Sequence Analysis Bringing the “Course” Back Into the Life Course. Sociological Methods and Research. https://doi.org/https://doi.org/10.1177%2F0049124109357532
American Red Cross. (2019). Blood Needs & Blood Supply. Retrieved October 21, 2019, from https://www.redcrossblood.org/donate-blood/how-to-donate/how-blood-donations-help/blood-needs-blood-supply.html
Amponsah-Afuwape, S. A., Myers, L. B., & Newman, S. P. (2002). Cognitive predictors of ethnic minorities’ blood donation intention. Psychology, Health and Medicine, 7(3), 357–361. https://doi.org/10.1080/13548500220139359
Batagelj, V. (1988). Generalized Ward and Related Clustering Problems. In H. H. Bock (Ed.), Classification and related methods of data analysis. Retrieved from http://vlado.fmf.uni-lj.si/pub/preprint/ward.pdf
Bellazi, R., & Zupan, B. (2008). Predictive Data Mining in Clinical Medicine: Current Issues and Guidlines. International Journal of Medical Informatics, 77(2), 81–97. https://doi.org/10.1016/j.ijmedinf.2006.11.006
Bergroth, L., Hakonen, H., & Raita, T. (2000). A survey of longest common subsequences algorithms. Proceeding Seventh International Symposium on String Processing and Information Retrieval, 39–48. https://doi.org/https://doi.org/10.1109/SPIRE.2000.878178
Berndt, D., & Clifford, J. (1994). DTW(dynamic time warping ). Workshop on Knowledge Knowledge Discovery in Databases, 398, 359–370.
Besse, P., Guillouet, B., Loubes, J.-M., & François, R. (2015). Review and Perspective for Distance Based Trajectory Clustering. 1–10.
Bhat, A. (2014). K-Medoids Clustering using Partitioning Around Medoids for Performing Face Recognition. International Journal of Soft Computing, Mathematics and Control (IJSCMC), 3, 1–12. https://doi.org/10.14810/ijscmc.2014.3301
Blasiak, S., & Rangwala, H. (2011). A hidden markov model variant for sequence classification. IJCAI International Joint Conference on Artificial Intelligence, 1192–1197. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-203
Bolano, D. (2014). Hidden Markov Models : An Approach to Sequence Analysis in Population Studies. Pennsylvania Pharmacists Association - Annual Meeting, 1–22.
Bousquet, O., & Elisseeff, A. (2002). Stability and Generalization. Journal of Machine Learning Research, 2, 499–526. Retrieved from http://www.jmlr.org/papers/volume2/bousquet02a/bousquet02a.pdf
Brooke, M. (2019). The Importance of Donating Blood. Retrieved October 21, 2019, from https://www.crispregional.org/the-importance-of-donating-blood/
Cadez, I. V, Gaffney, S., & Smyth, P. (2000). A general probabilistic framework for clustering individuals and objects. Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 140–149. https://doi.org/https://doi.org/10.1145/347090.347119
Charles, K. S., Hughes, P., Gadd, R., Bodkyn, C. J., & Rodriguez, M. (2010). Evaluation of blood donor deferral causes in the Trinidad and Tobago national blood transfusion service. Journal of the British Blood Transfusion Society, 20(1), 11–14. https://doi.org/10.1111/j.1365-3148.2009.00968.x
Chen, L., & Ng, R. (2004). On The Marriage of Lp-norms and Edit Distance. Proceedings 2004 VLDB Conference, 792–803. https://doi.org/10.1016/b978-012088469-8/50070-x
Chun, Y. H., & Platt, M. (1992). On the Sequential Selection Problem. Decision Science, 23(5), 1–12. https://doi.org/https://doi.org/10.1111/j.1540-5915.1992.tb00447.x
Clegg, B. A., DiGirolamo, G. J., & Keele, S. W. (1998). Sequence Learning. Trends in Cognitive Sciences, 2(8), 275–281. https://doi.org/https://doi.org/10.1016/S1364-6613(98)01202-9
Coates, A., & Ng, A. Y. (2012). Learning Feature Representations with K-means. In NN: Tricks of the Trade (2nd ed., pp. 561–580). https://doi.org/10.1007/978-3-642-35289-8
Cohen, A., Ivry, R. ., & Keele, S. . (1990). Attention and Structure in Sequence Learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(1), 17–30. Retrieved from https://psycnet.apa.org/doiLanding?doi=10.1037%2F0278-7393.16.1.17
Cornwell, B. (2019). Social Sequence Analysis Method and Applications. In M. Granovetter (Ed.), Journal of Chemical Information and Modeling (37th ed., Vol. 53). https://doi.org/10.1017/CBO9781107415324.004
DeAeth, D. (2019, August 17). Blood banks across Taiwan running low, donations needed. Taiwan News. Retrieved from https://www.taiwannews.com.tw/en/news/3761565
Drackley, A. (2010). How Generous Are We? Forecasting and Demographic Correlates of Blood Donation (McMaster University). Retrieved from https://macsphere.mcmaster.ca/handle/11375/9460
Drackley, A., Newbold, K. B., Paez, A., & Heddle, N. (2012). Forecasting Ontario’s blood supply and demand. Transfusion, 52(2), 366–374. https://doi.org/10.1111/j.1537-2995.2011.03280.x
Du, W., Du, W., Zhan, Z., & Zhan, Z. (2002). Building decision tree classifier on private data. Proceedings of the IEEE International Conference on Privacy, Security and Data Mining-Volume 14, 1–8. Retrieved from http://portal.acm.org/citation.cfm?id=850784
Dufwenberg, M., & Kirchsteiger, G. (2004). A theory of sequential reciprocity. Games and Economic Behavior, 47(2), 268–298. https://doi.org/https://doi.org/10.1016/j.geb.2003.06.003
Dunn, J. . (1974). Well-Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics, 4(1), 95204. https://doi.org/https://doi.org/10.1080/01969727408546059
Elzinga, C. H., & Studer, M. (2015). Spell Sequences, State Proximities, and Distance Metrics. Sociological Methods and Research, 44(1), 3–47. https://doi.org/10.1177/0049124114540707
Filho, O. S. S., Cezarino, W., & Salviano, G. R. (2012). A decision-making tool for demand forecasting of blood components. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 14). https://doi.org/10.3182/20120523-3-RO-2023.00201
Fritsch, J., Finke, M., & Waibel, A. (1997). Context-dependent hybrid HME/HMM speech recognition using polyphone clustering decision trees. 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, 1759–1762. https://doi.org/10.1109/ICASSP.1997.598867
Fung, P., Ngai, G., & Cheung, C.-S. (2003). Combining optimal clustering and Hidden Markov models for extractive summarization. (1991), 21–28. https://doi.org/10.3115/1119312.1119315
Gauthier, J.-A., Widmer, E. D., Bucher, P., & Notredame, C. (2010). Multichannel Sequence Analysis Applied to Social Science Data. Sociological Methodology, 40(1), 1–38. https://doi.org/https://doi.org/10.1111%2Fj.1467-9531.2010.01227.x
Gauthier, J. ., Widmer, E. ., Bucher, P., & Notredame, C. (2009). How Much does It Cost? Sociological Methods & Research, 38(1), 197–231. https://doi.org/https://doi.org/10.1177%2F0049124109342065
Germain, M., Glynn, S. A., Schreiber, G. B., Gelinas, S., King, M., Jones, M., … Tu, Y. (2007). Determinants of return behaviour: a comparison of current and lapsed donors. Transfusion, 47, 1862. https://doi.org/https://doi.org/10.1111/j.1537-2995.2007.01409.x
Ghassempour, S. (2014). Clustering Longitudinal Health Data Using Hidden Markov Models by (University of Western Sydney). Retrieved from http://handle.uws.edu.au:8081/1959.7/uws:29977
Ghassempour, S., & Girosi, F. (2013). Clustering Health Trajectories Using Hidden Markov Models. 1–6. Retrieved from papers2://publication/uuid/61979624-E1D2-4495-8CCF-14AB1AB107C6
Ghassempour, S., Girosi, F., & Maeder, A. (2014). Clustering multivariate time series using Hidden Markov Models. International Journal of Environmental Research and Public Health, 11(3), 2741–2763. https://doi.org/10.3390/ijerph110302741
Gibbons, R. (1992). Game Theory for Applied Economists. New Jersey: Princeton University.
Glynn, S. A., Kleinman, S. H., Schreiber, G. B., Busch, M. P., Wright, D. J., Smith, J. W., … Williams, A. E. (2000). Trends in incidence and prevalence of major transfusion-transmissible viral infections in US blood donors, 1991 to 1996. Journal of the American Medical Association, 284(2), 229–235. https://doi.org/10.1001/jama.284.2.229
Guo, F. C., & Lin, S. W. (2018). Time Series and Sequence Analysis of Blood Donation Behavior. National Taiwan University of Science and Management.
Gusfield, D. (1997). Algorithms on strings, trees, and sequences: computer science and computational biology. Retrieved from https://dl.acm.org/citation.cfm?id=262228
Hamming, R. . (1950). Error Detecting and Error Correcting Codes. Bell System Technical Journal, 29(2), 1–14. https://doi.org/https://doi.org/10.1002/j.1538-7305.1950.tb00463.x
Han, S.-K., & Moen, P. (1999). Work and Family Over Time: A Life Course Approach. The ANNALS of the American Academy of Political and Social Science, 562(1), 98–110. https://doi.org/https://doi.org/10.1177%2F000271629956200107
Helske, S. (2016). Statistical analysis of life sequence data (University of Jyvaskyla). Retrieved from https://www.researchgate.net/publication/308794297_Statistical_analysis_of_life_sequence_data
Helske, S., Helske, J., & Eerola, M. (2016). Analysing Complex Life Sequence Data with Hidden Markov Modelling. LaCOSA II : Proceedings of the International Conference on Sequence Analysis and Related Methods, (June), 209–240.
Helske, S., Helske, J., & Eerola, M. (2018). Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data. 185–200. https://doi.org/10.1007/978-3-319-95420-2_11
Jain, A. K. (2010). Data Clustering: 50 years beyond K-means. Pattern Recognition Letters, 31, 651–666. https://doi.org/http://dx.doi.org/10.1016/j.patrec.2009.09.011
Jeebun, S., Ballgobin, R. R., & Al-ani, T. (2015). Optimal Number of States in Hidden Markov Models and its Application to the Detection of Human Movement. University of Mauritius Research Journal, 21, 438–469. Retrieved from https://www.ajol.info/index.php/umrj/article/download/125170/114702
Jeffrey, H. (1946). An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 186(1007), 453–461. https://doi.org/10.1098/rspa.1946.0056
Johnson, B. (2013). Blood Donor Recruitment Tips: Effectively Segmenting Your Donor Base. Retrieved June 1, 2020, from Incept Saves website: https://www.inceptsaves.com/blog/2016/7/16/blood-donor-recruitment-tips-effectively-segmenting-your-donor-base
Kasraian, L. (2010). Causes of discontinuity of blood donation among donors in Shiraz, Iran: cross-sectional study. Sao Paulo Medical Journal, 128(5), 272–275. https://doi.org/10.1590/s1516-31802010000500006
Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert System with Applications, 37(1), 479489. https://doi.org/https://doi.org/10.1016/j.eswa.2009.05.044
Kleywegt, A. J., & Papastavrou, J. D. (1998). The Dynamic and Stochastic Knapsack Problem. Operation Research, 46(1), 17–35. https://doi.org/https://doi.org/10.1287/opre.46.1.17
Klinkenberg, E. F., Huis In’t Veld, E. M. J., de Wit, P. D., van Dongen, A., Daams, J. G., de Kort, W. L. A. M., & Fransen, M. P. (2018). Blood donation barriers and facilitators of Sub-Saharan African migrants and minorities in Western high-income countries: a systematic review of the literature. Journal of the British Blood Transfusion Society, 29(S1), 28–41. https://doi.org/10.1111/tme.12517
Lavanya, D., & Rani, U. K. (2012). Ensemble Decision Tree Classifier for Breast Cancer Data. International Journal of Information Technology Convergence and Services, 2(1). Retrieved from https://www.researchgate.net/profile/K_Rani/publication/267424175_Ensemble_Decision_Tree_Classifier_For_Breast_Cancer_Data/links/56e8f83f08aea51e7f3b6bac/Ensemble-Decision-Tree-Classifier-For-Breast-Cancer-Data.pdf
Le Meur, N., Gao, F., & Bayat, S. (2015). Mining care trajectories using health administrative information systems: The use of state sequence analysis to assess disparities in prenatal care consumption. BMC Health Services Research, 15(1), 1–10. https://doi.org/10.1186/s12913-015-0857-5
Liao, T. W. (2005). Clustering of time series data-a survey. The Journal of The Pattern Recognition Society, 38, 1857–1874. https://doi.org/:10.1016/j.patcog.2005.01.02
Liu, Y., Li, Z., Xiong, H., Gao, X., & Wu, J. (2010). Understanding of internal clustering validation measures. Proceedings - IEEE International Conference on Data Mining, ICDM, 911–916. https://doi.org/10.1109/ICDM.2010.35
Luo, X., & Jelinek, F. (1999). Probabilistic classification of HMM states for large vocabulary continuous speech recognition. 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, 353–356. https://doi.org/10.1109/ICASSP.1999.758135
Maruotti, A., & Rocci, R. (2012). A mixed non-homogeneous hidden Markov model for categorical data, with application to alcohol consumption. Statistics in Medicine, 31(9), 871–886. https://doi.org/10.1002/sim.4478
MEDICA. (2019). World Blood Donation Day: A small drop saves lives. Retrieved November 1, 2019, from MEDICA Magazine website: https://www.medica-tradefair.com/en/News/News_from_the_Editors/World_Blood_Donation_Day:_A_small_drop_saves_lives
Merugu, A. B. S., & Dhillon, I. S. (2005). Clustering time series with clipped data. Machine Learning, 58(2–3), 1705–1749. https://doi.org/10.1007/s10994-005-5825-6
Misje, A., Bosnes, V., & Heier, H. . (2010). Gender Differences in Presentation Rates, Deferrals and Return Behavior among Norwegian Blood Donors. Vox Sanguinis, 98(31), 241–248. https://doi.org/10.1111/j.1423-0410.2009.01267.x.
Nair, S. C., & Mammen, J. J. (2015). Repeat voluntary non-remunerated blood donor is the best quality indicator for blood safety. Indian Journal of Medical Research, 141(6), 749–752. https://doi.org/10.4103/0971-5916.160687
Neto, de A. C. (2011). Retention of blood donors: Strategies to fulfill the requirements of blood centers. Revista Brasileira de Hematologia e Hemoterapia, 33(3), 174. https://doi.org/10.5581/1516-8484.20110046
Nikolaus, T., & George, T. (2008). Document classification system based on HMM word map. Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, 7–12. https://doi.org/10.1145/1456223.1456229
Notari Iv, E. P., Zou, S., Fang, C. T., Eder, A. F., Benjamin, R. J., & Dodd, R. Y. (2009). Age-related donor return patterns among first-time blood donors in the United States. Transfusion, 49(10), 2229–2236. https://doi.org/10.1111/j.1537-2995.2009.02288.x
Öhrner, C., Kvist, M., Blom Wiberg, K., & Diedrich, B. (2019). Why do young men lapse from blood donation? Vox Sanguinis, 114, 566–575. https://doi.org/10.1111/vox.12796
Ou-Yang, J., Bei, C. H., He, B., & Rong, X. (2017). Factors influencing blood donation: a cross-sectional survey in Guangzhou, China. Transfusion Medicine, 27(4), 256–267. https://doi.org/10.1111/tme.12410
Ownby, H. E., Kong, F., Watanabe, K., Tu, Y., & Nass, C. C. (1999). Analysis of donor return behavior. Transfusion, 39(10), 1128–1135. https://doi.org/10.1046/j.1537-2995.1999.39101128.x
Panuccio, A., Bicego, M., & Murino, V. (2002). A Hidden Markov Model-Based Approach to Sequential Data Clustering. In International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntatic Pattern Recognition (SSPR) (pp. 734–743). https://doi.org/https://doi.org/10.1007/3-540-70659-3_77
Pereira, A. (2004). Performance of Time Series Methods in Forecasting The Demand for Red Blood Cell Transfusion. Transfusion, 44, 739–746. https://doi.org/https://doi.org/10.1111/j.1537-2995.2004.03363.x
Petzold, P., & Haubensak, G. (2001). Higher Order Sequential Effects in Psychophysical Judgments. Perception and Psychophysics, 63(6), 969–978. https://doi.org/https://doi.org/10.3758/BF03194516
Policker, S., & Geva, A. B. (2000). Nonstationary time series analysis by temporal clustering. EEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 30(2), 339–343. https://doi.org/doi: 10.1109/3477.836381.
Rabiner, L. ., Lee, C. ., Juang, B. ., & Wilpon, J. . (1989). HMM clustering for connected word recognition. International Conference on Acoustics, Speech, and Signal Processing, 405–408. https://doi.org/10.1109/ICASSP.1989.266451
Reeb, P. D., Bramardi, S. J., & Steibel, J. P. (2015). Assessing dissimilarity measures for sample-based hierarchical clustering of RNA sequencing data using plasmode datasets. PLoS ONE, 10(7), 1–18. https://doi.org/10.1371/journal.pone.0132310
Rendón, E., Abundez, I., Arizmendi, A., & Quiroz, E. M. (2011). Internal versus External cluster validation indexes. International Journal of Computers and Communications, 5(1), 27--34. Retrieved from http://w.naun.org/multimedia/UPress/cc/20-463.pdf
Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. In PLoS ONE (Vol. 14). https://doi.org/10.1371/journal.pone.0210236
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/https://doi.org/10.1016/0377-0427(87)90125-7
Roux, J., Grimaud, O., & Leray, E. (2019). Use of state sequence analysis for care pathway analysis: The example of multiple sclerosis. Statistical Methods in Medical Research, 28(6), 1651–1663. https://doi.org/10.1177/0962280218772068
Santhanam, T., & Sundaram, S. (2010). Application of CART algorithm in blood donors classification. Journal of Computer Science, 6(5), 548–552. https://doi.org/10.3844/jcssp.2010.548.552
Saraswathi, S., & Sheela, M. I. (2014). Croydon’s water supply: Safeguarding measures. International Journal of Computer Science and Mobile Computing, 3(11), 422–428. https://doi.org/10.1136/bmj.1.4037.1119
Schmidt, D., Shupp, R., Walker, J. M., & Ostrom, E. (2003). Playing safe in coordination games:: the roles of risk dominance, payoff dominance, and history of play. Games and Economic Behavior, 42(2), 281–299. https://doi.org/https://doi.org/10.1016/S0899-8256(02)00552-3
Schreiber, G. B., Sharma, U. K., Wright, D. J., Glynn, S. A., Ownby, H. E., Tu, Y., … Gilcher, R. (2005). First year donation patterns predict long-term commitment for first-time donors. Vox Sanguinis, 88(2), 114–121. https://doi.org/10.1111/j.1423-0410.2005.00593.x
Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2), 461464. Retrieved from https://projecteuclid.org/euclid.aos/1176344136
Shakeri, M. T., Vafaee, A., Esmaeily, H., Shafiei, N., Bazargani, R., & Khayamy, M. E. (2012). The Causes for Lack of Interest to Blood Donation in Eligible Individuals , Mashhad , Northeastern Iran. 14(1), 37–40.
Sharma, P. (2019). The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need. Retrieved November 5, 2019, from Analythics Vidya website: https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/
Silva Filho, O. S., Carvalho, M. A., Cezarino, W., Silva, R., & Salviano, G. (2013). Demand forecasting for blood components distribution of a blood supply chain. IFAC Proceedings Volumes (IFAC-PapersOnline), 6(PART 1), 565–571. https://doi.org/10.3182/20130911-3-BR-3021.00092
Siromani, U., Rita Isaac, T. T., Daniel, D., Selvaraj, K. G., Mammen, J. J., & Nair, S. C. (2013). Recruitment and retention of voluntary blood donors through electronic communication. Acta Informatica Medica, 21(2), 142. https://doi.org/10.5455/aim.2013.21.142-142
Spekman, M. L. C., van Tilburg, T. G., & Merz, E. M. (2019). Do deferred donors continue their donations? A large-scale register study on whole blood donor return in the Netherlands. Transfusion, 59(December 2019). https://doi.org/10.1111/trf.15551
Stamp, D. I., & Wu, Q. H. (1998). Prediction of chaotic time series using hidden Markov models. IEE Conference Publication, 33(455), 1420–1425.
Steele, R. S., Schreiber, G. B., Guiltinan, A., Nass, C., Glynn, S. A., Wright, D. J., … Garraty, G. (2008). The role of altruistic behavior, emphatetic concern, and social responsibility motivation in blood donation behavior. Practical Transfusion Medicine: Third Edition, 48(January), 43–54. https://doi.org/10.1002/9781444311761.ch19
Stewart, N., & Gordon, D. B. (2004). Sequence Effects in the Categorization ofTones Varying in Frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 416–430. Retrieved from https://psycnet.apa.org/buy/2004-11031-011
Studer, M., & Ritschard, G. (2014). A Comparative Review of Sequence Dissimilarity Measures. https://doi.org/https://doi.org/10.12682/lives.2296-1658.2014.33
Studer, M., & Ritschard, G. (2016). What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures. Journal of the Royal Statistical Society. Series A: Statistics in Society, 179(2), 481–511. https://doi.org/10.1111/rssa.12125
Studer, M., Ritschard, G., Gabadinho, A., & Muller, N. S. (2011). Discrepancy Analysis of State Sequences. Sociological Methods & Research, 40(3), 471–510. https://doi.org/10.1177/0049124111415372
Sun, R., & Giles, C. (2001). Sequence learning: Paradigm, algorithms, and applications. In Sequence Learning (p. 396). https://doi.org/10.1007/3-540-44565-X
Swarndepp, S. J., & Pandya, S. (2016). An Overview of Partitioning Algorithms in Clustering Techniques. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 5(6), 1943–1946. https://doi.org/10.1017/CBO9781107415324.004
TBSF. (2014). About. Retrieved November 4, 2019, from Taiwan Blood Services Foundation website: http://www.blood.org.tw/Internet/english/docDetail.aspx?uid=7677&pid=6501&docid=36070
TBSF. (2019). Annual Report 2018. In Taiwan Blood Services Foundation (Vol. 4). https://doi.org/10.3934/math.2019.1.166
The American National Red Cross. (2019). Blood Needs & Blood Supply. Retrieved November 1, 2019, from The American National Red Cross website: https://www.redcrossblood.org/donate-blood/how-to-donate/how-blood-donations-help/blood-needs-blood-supply.html
Tsai, S. L. (2009). Challenge of donor recruitment. International Society of Blood Transfusion, ISBT Science Series, 4, 302–306. https://doi.org/10.1111/j.1751-2824.2009.01259.x
van Dongen, A. (2015). Easy come, easy go: Retention of blood donors. Transfusion Medicine, 25(4), 227–233. https://doi.org/10.1111/tme.12249
Visser, I., & Speekenbrink, M. (2010). depmixS4: An R package for hidden markov models. Journal of Statistical Software, 36(7), 1–21. https://doi.org/10.18637/jss.v036.i07
Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. Proceedings - International Conference on Data Engineering, 673–684. https://doi.org/10.1109/ICDE.2002.994784
Vogt, V., Scholz, S. M., & Sundmacher, L. (2018). Applying sequence clustering techniques to explore practice-based ambulatory care pathways in insurance claims data. European Journal of Public Health, 28(2), 214–219. https://doi.org/10.1093/eurpub/ckx169
Waller, D., Thijsen, A., Garradd, A., Hayman, J., & Smith, G. (2017). Donating blood for research: A potential method for enhancing customer satisfaction of permanently deferred blood donors. Blood Transfusion, 15(1), 13–19. https://doi.org/10.2450/2015.0142-15
Waller, R. A. (2010). Reliability Analysis of Complex Series / Parallel Systems of Binomial Subsystems and. Quality, 32(4), 407–416.
Ward, J., & Jr. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/https://doi.org/10.1080/01621459.1963.10500845
Wenliang, D., & Zhan, Z. (2002). Building Decision Tree Classifier on Private Data. Proceeding of the IEEE International Conference on Privacy, Security and Data Mining, 1–8. Retrieved from https://dl.acm.org/doi/10.5555/850782.850784
WHO. (2017). 2016 Global Status Report on Blood Safety and Availability. Retrieved from http://apps.who.int/bookorders.
WHO and International Federation of Red Cross and Red Crescent Societies. (2010). Towards 100 % Voluntary Blood Donation A Global Framework for Action. In World Health Organization. Geneva.
Williamson, L. M., & Devine, D. V. (2013). Challenges in the management of the blood supply. The Lancet, 381(9880), 1866–1875. https://doi.org/10.1016/S0140-6736(13)60631-5
Wu, T. J., Hsieh, Y. C., & Li, L. A. (2001). Statistical measures of DNA sequence dissimilarity under Markov chain models of base composition. Biometrics, 57(2), 441–448. https://doi.org/10.1111/j.0006-341X.2001.00441.x
Wu, X., Kumar, V., Ross, Q. J., Ghosh, J., Yang, Q., Motoda, H., … Steinberg, D. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. https://doi.org/10.1007/s10115-007-0114-2
Wylie, B. (1993). Which methods of donor recruitment give the safest donors? The Malaysian Journal of Pathology, 15(2), 99–103.
Xiong, Y., & Yeung, D.-Y. (2004). Time Series Clustering with ARMA mixtures. Pattern Recognition, 37(8), 1675–1689. https://doi.org/https://doi.org/10.1016/j.patcog.2003.12.018
Xu, R., & Wunsch, D. C. (2008). Sequential Data Clustering. In Clustering (pp. 179–212). https://doi.org/10.1002/9780470382776
Yu, P. L. H., Chung, K. H., Lin, C. K., Chan, J. S. K., & Lee, C. K. (2007). Predicting potential drop-out and future commitment for first-time donors based on first 1.5-year donation patterns: The case in Hong Kong Chinese donors. Vox Sanguinis, 93(1), 57–63. https://doi.org/10.1111/j.1423-0410.2007.00905.x