PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data

• Published in: SN computer science Vol. 2; no. 6; p. 477
• Main Authors: Rao, A. Ravishankar, Garai, Subrata, Dey, Soumyabrata, Peng, Hang
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.11.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Availability; Big Data; Coders; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data analysis; Data Structures and Information Theory; Datasets; Economic analysis; Feature selection; Health care; Information Systems and Communication Service; Machine learning; Mortality; Open data; Original Research; Outliers (statistics); Parameters; Pattern Recognition and Graphics; Policies; Public health; Researchers; Software Engineering/Programming and Operating Systems; Source code; Trends; Vision; New York; United States > US; California; Policy making; Exploratory data analysis; Machine learning; Trend analysis; Open healthcare data; Big data analytics; Outlier detection; Unsupervised clustering
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-021-00871-7

With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education, and healthcare. We focus on healthcare due to its economic importance worldwide. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysis of trends and the detection of outliers. This allows policies to be rapidly adapted to changing circumstances. We present an efficient outlier detection technique, termed PIKS (Pruned iterative-k means searchlight), which combines an iterative k-means algorithm with a pruned searchlight based scan. We apply this technique to identify outliers in two publicly available healthcare datasets from the New York Statewide Planning and Research Cooperative System, and California’s Office of Statewide Health Planning and Development. We provide a comparison of our technique with three other existing outlier detection techniques, consisting of auto-encoders, isolation forests, and feature bagging. We identified outliers in conditions including suicide rates, immunity disorders, social admissions, cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the PIKS technique produces results consistent with other techniques such as the auto-encoder. However, the auto-encoder needs to be trained, which requires several parameters to be tuned. In comparison, the PIKS technique has far fewer parameters to tune. This makes it advantageous for fast, “out-of-the-box” data exploration. The PIKS technique is scalable and can readily ingest new datasets. Hence, it can provide valuable, up-to-date insights to citizens, patients, and policy-makers. We have made our code open source, and with the availability of open data, other researchers can easily reproduce and extend our work. This will help promote a deeper understanding of healthcare policies and public health issues.