Slicing the Past to Predict Future

In this research, a heuristic approach is proposed and experimented with to detect warning signs based on historical data. The paper makes use of COVID-19 positive count reported by different states of the USA for illustration of the methods. These are data in the form of several single variable time series related to a concept. The basic idea is to divide the total time period into smaller segments and examine changes within and between series in the segments. Algorithms for clustering series and concordance are used as tools of the heuristic. The approach is to observe the behavior of members of clusters in the segments and predictions are formulated based on observed changes which is heuristic part. Hidden Markov Model is used for change detection and series clustering. Concordance is used for comparing similarity in behavior of series in segments.

Data Reduction with Distance Correlation

Data reduction is a technique used in big data applications. Volume, velocity, and variety of data bring in time and space complexity problems to computation. While there are several approaches used for data reduction, dimension reduction and redundancy removal are among common approaches. In those approaches, data are treated as points in a large space. This paper considers the scenario of analyzing a topic for which similar multi-dimensional data are available from different sources. The problem can be stated as data reduction by source selection. This paper examines distance correlation (DC) as a technique for determining similar data sources. For demonstration, COVID-19 in the United States of America (US) is considered as the topic of analysis as it is a topic of considerable interest. Data reported by the states of US are considered as data sources. We define and use a variation of concordance for validation analysis. © 2021, Springer Nature Singapore Pte Ltd.

Diagnosis of a chronic spinal cord injury via televisit in a patient from an underserved community

A 42-year-old woman from a medically underserved community in rural New England was referred by her primary care provider (PCP) for televisit during the coronavirus disease 2019 (COVID-19) pandemic following 2 years of chronic neck pain and numbness in her left hand that was initially concerning for demyelinating disease. Upon further evaluation, it was revealed that she had experienced a traumatic fall with a concussion and symptoms consistent with central cord syndrome but had refused magnetic resonance imaging (MRI) at her initial medical evaluation. On MRI conducted 1 month prior to neurosurgical evaluation she was found to have a disc bulge and 4-mm T2-hyperintense lesion at the C4-C5 level that was consistent with a chronic spinal cord injury secondary to spinal trauma with associated vertebrogenic injury. This televisit confirmed the diagnosis of chronic spinal cord injury for this patient and allowed for discussion of future interventions, avoided further unnecessary referrals, and increased access to care.