Predictive Analytics: Predictive Modeling at the Micro Level

Brown, Donald E.; Abbasi, Ahmed; Lau, Raymond Y. K.

doi:10.1109/mis.2015.50

Cited by 27 publications

(16 citation statements)

References 9 publications

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“…28 Predictive analytics refers to the systematic use of statistical or machine learning methods to make predictions and support decision-making. 29 It uses computational techniques rooted in several domains including statistics, database management, artificial intelligence, machine learning, pattern recognition and data visualization. Currently, predictive analytics is being applied in many fields such as public safety 30 and security, 31 cybersecurity 32 and social media.…”

Section: Predictive Analytics In Healthcarementioning

confidence: 99%

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma

2016

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Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with wellestablished protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

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Section: Predictive Analytics In Healthcarementioning

confidence: 99%

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma

2016

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“…For each of the four aforementioned psychometric dimensions (HL, HN, TD, and AV), wellestablished survey items have been developed in the literature. These items can be used to compute individuals' scores on a fixed continuous scale (e.g., [1][2][3][4][5][6][7][8][9][10]. In order to construct our user-generated text datasets, we developed equivalent free response questions with accompanying text boxes that immediately followed the survey items.…”

Section: Testbedmentioning

confidence: 99%

“…Psychometrics is concerned with the measurement of knowledge, ability, attitudes, and personality traits. With the increased importance of analytics at the micro-level [10], including understanding and prediction of individuals' behaviors [4], accurate and timely measurement of psychometrics has become of paramount importance. In cybersecurity contexts, self-efficacy and threat perceptions are critical psychometric dimensions known to be strong indicators of end-user susceptibility to phishing attacks [5].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

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Psychometric measures reflecting people's knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this article, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multitask learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated, and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing 11 psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions. CCS Concepts: • Computing methodologies → Natural language processing;

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“…Predictive analytics have huge applications in various fields like Homeland security, Crime prevention, Infrastructure management, Cyber security, Intelligent Transportation, Health care and bioinformatics, Text mining, Fraud detection, Social media and decision support [1], Credit scores, Credit card, fraud detection, Mail Sorting, Weather prediction, Hot dogs and hamburgers [16], not only about resource allocation but also about where or how should to allocate resource, what to expect from outcomes of model, how to manage the key drivers of the economic model for better outcome? [19].…”

Section: Applications Of Predictive Analyticsmentioning

confidence: 99%

“…All authors read and approved the final manuscript. 1 Department of CSE, K L University, Guntur, Andhra Pradesh, India.…”

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confidence: 99%