Pankush Kalgotra

Predictive Modeling in Sports Leagues: An Application in Indian Premier League

The purpose of this article is to develop models that can help team selectors build talented teams with minimum possible spending. In this study, we build several predictive models for predicting the selection of a player in the Indian Premier League, a cricket league, based on each player’s past performance. The models are developed using SAS® Enterprise Miner™ 7.1. The best-performing model in the study is selected based on the validation data misclassification rate. The selected model provides us with the probability measure of the selection of each player, which can be used as a valuation factor in the bidding equation. The models that are developed can help decision makers during auction set salaries for the players.

Take Control of Interruptions in Your Life: Lessons from Routine Activity Theory of Criminology

Steeped among the items on the dark side of information technology are personal technology interruptions. Past research has examined the negative impact of technology interruptions; however, the factors that are responsible for the increasing rate of interruptions are rarely discussed. In this study, by adapting the criminology theory of Routine Activity Theory (RAT), we propose three factors that lead to an interruption: number of interruption sources, absence of guardians, and individual targetness. Results from a survey of mobile users show that combinations of these factors have increased the interruption rate in our lives. Interestingly, just having more apps on the phones does not increase interruptions; it is a combination of the factors noted above.

Progression analysis of signals: Extending CRISP-DM to stream analytics

Stream analytics focuses on analysis of signals generated simultaneously and over time. The specific patterns in the signals can indicate some of the outcomes such as failure of a device, etc. Therefore, novel ways to find specific patterns in the signals generated by many sources are required. In this paper, we extend the CRISP-DM process to include data preparation approaches for sequence mining. We present progression analysis, an approach for converting streams of records to be able to detect useful signals for analysis. To illustrate the process, we present a healthcare example where patients diagnosed with Tobacco Use Disorder develop multiple other diseases over multiple hospital visits. The common sequences of the diseases diagnosed in the TUD patients over multiple hospital visits are presented and discussed. Finally, the generalizability of the progression analysis is discussed.

Understanding the Impact of Interruptions on Knowledge Work: An Exploratory Neuroimaging Study

This paper explores how technologies can interrupt concentration, focus and attention of knowledge workers. The mechanisms by which an interruption takes attention away and the task performance decreases are unknown. The paper explores this impact of interruptions through neuroimaging. Subjects were given a reading task and subjected to a series of randomly timed audio interruptions. Using an EEG measurement device, we recorded their brain waves. Consistent with the literature, we found interruptions significantly increased task completion time and decreased task performance. We found activities in the frontal and temporal lobes of the participants changed or increased due to interruptions. In addition, increased activities in the frontal regions after an interruption appear to lead to better performance. The results suggest application developers to consider underlying mechanisms of processing interruptions.

Time-Based Comorbidity in Patients Diagnosed with Tobacco Use Disorder

Healthcare is one of the promising fields where Big Data tools and techniques can have the highest impact. One of the key problems in the healthcare sector is to analyze impact of comorbidities. Comorbidity is a medical condition when a patient develops multiple diseases simultaneously. The research on finding comorbidities over time is rare. In this paper, our focus is to find time-based comorbidities in the patients diagnosed with Tobacco Use Disorder (TUD). First, we explain a generalized process to find chronological comorbidities. Then, we analyze electronic medical records of patients diagnosed with Tobacco Use Disorder from the hospitals in the West South-Central region of United States (1999–2013). Specifically, we discover comorbidities in the TUD patients across three hospital visits. We also compare the results with the patients who never developed TUD.

Health: A Game of Disease Progression

A patient develops different diseases over time. There is a lack of research looking into the development of diseases over hospital visits. In this paper, we present a step-by-step process to find the sequences of diseases developed over hospital visits. In addition, we applied game theory lens to analyze the decisions made by physicians while prescribing the drugs. We found interesting results showing physicians taking preemptive actions to reduce the mortality rate among the heart failure patients. Finally, we built classification models to diagnose all the give diseases using lab reports. We were able to classify all the diseases seven out of ten times correctly. Our process and interesting findings can help medical community in improve their decision making related to the health outcomes.