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Publications

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  • All HBS Web  (46)
    • Faculty Publications  (5)

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    • All HBS Web  (46)
      • Faculty Publications  (5)

      Event Detection Remove Event Detection →

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      • 2022
      • Working Paper

      Post-market Surveillance of Software Medical Devices: Evidence from Regulatory Data

      By: Alexander O. Everhart and Ariel D. Stern
      Medical devices increasingly include software components, which facilitate remote patient monitoring. The introduction of software into previously analog medical devices as well as innovation in software-driven devices may introduce new safety concerns—all the more so...  View Details
      Keywords: Technological Innovation; Safety; Governing Rules, Regulations, and Reforms; Health Care and Treatment; Medical Devices and Supplies Industry
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      Everhart, Alexander O., and Ariel D. Stern. "Post-market Surveillance of Software Medical Devices: Evidence from Regulatory Data." Harvard Business School Working Paper, No. 23-035, November 2022.
      • 2016
      • Article

      Penalized Fast Subset Scanning

      By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
      We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic....  View Details
      Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods
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      Speakman, Skyler, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. "Penalized Fast Subset Scanning." Journal of Computational and Graphical Statistics 25, no. 2 (2016): 382–404. (Selected for “Best of JCGS” invited session by the journal’s editor in chief.)
      • 2015
      • Article

      Scalable Detection of Anomalous Patterns With Connectivity Constraints

      By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
      We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and...  View Details
      Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic
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      Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Journal of Computational and Graphical Statistics 24, no. 4 (2015): 1014–1033.
      • Article

      Fast Subset Scan for Multivariate Spatial Biosurveillance

      By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
      We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time...  View Details
      Keywords: Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan
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      Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Statistics in Medicine 32, no. 13 (June 15, 2013): 2185–2208.
      • 2011
      • Article

      Scalable Detection of Anomalous Patterns With Connectivity Constraints

      By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
      We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and...  View Details
      Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic
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      Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Emerging Health Threats Journal 4 (2011): 11121.
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