Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories
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In order to prevent iron deficiency, Sanquin—the national blood bank in the Netherlands—measures a blood donor’s hemoglobin (Hb) level before each donation and only allows a donor to donate blood if their Hb is above a certain threshold. In around 6.5% of blood bank visits by women, the donor’s Hb is too low and the donor is deferred from donation. For visits by men, this occurs in 3.0% of cases. To reduce the deferral rate and keep donors healthy and motivated, we would like to identify donors that are at risk of having a low Hb level. To this end we have historical Hb trajectories at our disposal, i.e., time series consisting of Hb measurements recorded for individual donors.
As a first step towards our long-term goal, in this paper we investigate the use of time series clustering. Unfortunately, existing methods have limitations that make them suboptimal for our data. In particular, Hb trajectories are of unequal length and have measurements at irregular intervals. We therefore experiment with two different data representations. That is, we apply a direct clustering method using dynamic time warping, and a trend clustering method using model-based feature extraction. In both cases the clustering algorithm used is k-means.
Both approaches result in distinct clusters that are well-balanced in size. The clusters obtained using direct clustering have a smaller mean within-cluster distance, but those obtained using the model-based features show more interesting trends. Neither approach results in ideal clusters though. We therefore conclude with an elaborate discussion on challenges and limitations that we hope to address in the near future.
KeywordsTime series Clustering Blood donation
- 2.Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359–370. AAAI Press (1994)Google Scholar
- 4.Eckner, A.: Algorithms for unevenly-spaced time series: moving averages and other rolling operators. In: Working Paper (2012)Google Scholar
- 6.Gropper, S.S., Smith, J.L.: Advanced Nutrition and Human Metabolism. Cengage Learning, Boston (2012)Google Scholar
- 10.Neil, D., Pfeiffer, M., Liu, S.C.: Phased LSTM: accelerating recurrent network training for long or event-based sequences. In: Advances in Neural Information Processing Systems, pp. 3882–3890 (2016)Google Scholar
- 11.Rani, S., Sikka, G.: Recent techniques of clustering of time series data: a survey. Int. J. Comput. Appl. 52(15), 1–9 (2012)Google Scholar
- 12.Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: INTERSPEECH (2014)Google Scholar
- 13.Zhu, Y., et al.: What to do next: modeling user behaviors by time-LSTM. In: IJCAI, pp. 3602–3608 (2017)Google Scholar