Pasi Fränti received his MSc and PhD degrees from the University of Turku, in 1991 and 1994 in Science. Since 2000, he has been a professor of Computer Science at the University of Eastern Finland. He has published in 99 journals and 175 peer review conference papers. Pasi Fränti is the head of the Machine Learning research group. His current research interests include clustering algorithms, location-based services, machine learning, web and text mining, and optimization of health care services. He has supervised 30 PhD graduates and is currently supervising nine more.
“Clustering Healthcare Data” will be presented November 7, at 11:00, in room I125 – participation is free, all are welcome.
Abstract: Clustering can a powerful tool in analyzing healthcare data. We show how clustering based on k-means and its variants can be used to extract new insight from various data with the aim to better optimize the health care system. We first show that simple variants of k-means and random swap algorithms can provide highly accurate clustering results. We demonstrate how k-means can be applied to categorical data, sets, and graphs. We model health care records of individual patients as a set of diagnoses. These can be used to cluster patients, and also create co-occurence graph of diagnoses depending on how often the same pair of diseases are diagnosed in the record of the same patient. Taking into account the order of the diagnoses, we can construct a predictor for the likely forthcoming diseases. We also provide a clustering algorithm to optimize the location of health care systems based on patient locations. As a case study, we consider coronary heart disease patients and analyze in what way the optimization of the locations can affect the expected time to reach the hospital within the given time. All the results can provide additional statistical information to healthcare planners and also medical doctors at the operational level to guide their efforts to provide better healthcare services.