#MyIBDHistory on Twitter: Identifying Disease Characteristics Using Personal Tweets

Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) or ulcerative colitis (UC). Inconclusive cases are diagnosed with IBD unclassified (IBD-U). In 2018, IBD patients shared their disease history on Twitter and signed their tweets with #MyIBDHistory. In this research, we analyzed those tweets and built a logistic regression classifier that predicts patients' IBD type. We constructed tabular classification features and assessed their importance using the regression coefficients and association rules. We identified key features that distinguished CD from UC and used the classifier to predict the disease type of IBD-U patients. Our results correlated with IBD-related research. The two most prominent features that tilted the classification towards CD were suffering from fistulas or nutrient deficiencies. We identified gender differences in disease perspective prior to diagnosis. The research shows that the personal information shared by patients on Twitter can enhance existing medical knowledge regarding their disease

Usage Patterns of Internet-Based Banking and BI Services

This study explores the use of Internet-Banking (IB) services. The study analyzed usage patterns and trends by tracking certain IB activities for a large sample of the bank\u27s customers. The analysis highlights some significant usage characteristics and patterns that have evolved around the more traditional IB services, such as account-status inquiries and financial transactions. However, with a newly-developed Business-Intelligence (BI) application, such patterns have not evolved yet. The conclusions stemming from such analysis can help understanding the needs of the bank\u27s customers, detecting certain customer-segments that use the IB services differently than others, and help developing further and personalizing advanced IB services, such as the online BI tool

Investigating Physicians\u27 Compliance with Drug Prescription Notifications

The objective of this study was to investigate physicians\u27 compliance with recommendations for drug substitutes embedded within an electronic medical record, to assess factors affecting compliance, and to evaluate associated cost savings. An exploratory study of all physicians in all clinics operated by a large health maintenance organization (HMO) was conducted using a transparent computerized agent that collected 1.21 million prescriptions prescribed by 647 physicians. Compliance with HMO recommendations for substitute drugs reached a 70 percent rate. Substitute type, whether generic or therapeutic, was found to be the most significant factor affecting compliance, with physician workload and age second and third in effect magnitude, respectively. Compliance was found to be non-automatic and selective, following a thoughtful cognitive process. The HMO realized at least a 4 percent reduction in costs for prescribed drugs as a result of compliance with substitute recommendations. The results can be interpreted via the lens of Organizational Justice Theory, assuming that the broad compliance with generic substitutes was driven by perception of just procedures, whereas there was no such perception in the case of therapeutic substitutes. While more research is warranted for investigating the motivations driving physicians\u27 compliance, we strongly feel that the results can be generalized to other HMOs and healthcare settings

Inter-Observer Agreement Among Medical Professionals in Critical Care of Neonates and Children

ABSTRACT Inter-observer agreement is essential to medical staff members and has a major effect on communication. The goal of the study was to examine the way medical professionals evaluate the potential severity of Almost Adverse Events (AAEs) that were observed in two intensive care units (ICUs). One hundred and fourteen AAEs were observed and recorded in both units by engineering students. Each AAE was rated independently by five senior medical staff members from each ICU, chosen by the unit manager, on a three-point severity level scale. Statistical analysis (K statistic and Cohen's Kappa) yielded relatively low levels of agreement among raters in both ICUs (< 0.3), but significantly greater agreement was found among nurses than among physicians in both ICUs. Low levels of agreement are attributed to the nature of work and characteristics of each ICU. Recommendations for improving agreements including forming shared mental models are specified

Host Based Intrusion Detection using Machine Learning

Abstract—Detecting unknown malicious code (malcode) is a challenging task. Current common solutions, such as anti-virus tools, rely heavily on prior explicit knowledge of specific instances of malcode binary code signatures. During the time between its appearance and an update being sent to anti-virus tools, a new worm can infect many computers and cause significant damage. We present a new host-based intrusion detection approach, based on analyzing the behavior of the computer to detect the presence of unknown malicious code. The new approach consists on classification algorithms that learn from previous known malcode samples which enable the detection of an unknown malcode. We performed several experiments to evaluate our approach, focusing on computer worms being activated on several computer configurations while running several programs in order to simulate background activity. We collected 323 features in order to measure the computer behavior. Four classification algorithms were applied on several feature subsets. The average detection accuracy that we achieved was above 90 % and for specific unknown worms even above 99%. Keywords-component; Malicious code detection; worms; I

The effect of missing data on classification quality

The field of data quality management has long recognized the negative impact of data quality defects on decision quality. In many decision scenarios, this negative impact can be largely attributed to the mediating role played by decision-support models - with defected data, the estimation of such a model becomes less reliable and, as a result, the likelihood of flawed decisions increases. Drawing on that argument, this study presents a methodology for assessing the impact of quality defects on the likelihood of flawed decisions. The methodology is first presented at a high level, and then extended for analyzing the impact of missing values on binary Linear Discriminant Analysis (LDA) classifiers. To conclude, we discuss possible directions for extensions and future directions

A Framework for Identifying Patients on Twitter and Learning from Their Personal Experience

Social media serve as an alternate information source for patients, who use them to share information and provide social support. The aim of this research was to enable the analysis of patients’ tweets, by building a classifier of Twitter users that distinguishes patients from other entities. In the first stage of the research, a machine learning method, combining both social network analysis and natural language processing, was used to automatically classify users as patients or not. Three types of features were considered: (1) the user’s behavior on Twitter, (2) the content of the user’s tweets, and (3) the social structure of the user’s network. While different classification algorithms were considered, the best results (F1-score 0.808 and Precision 0.809) were achieved by a multiple-instance approach which constitute the novelty of this research. In the second stage of the research, the obtained classification methods were used to collect tweets of patients, in which they describe the different lifestyle changes they endure in order to deal with their disease. Using IBM Watson Service for entity sentiment analysis, frequently mentioned lifestyles were identified and their effectiveness on patients’ wellbeing was examined

A Methodology for Quantifying the Effect of Missing Data on Decision Quality in Classification Problems

Decision-making is often supported by decision models. This study suggests that the negative impact of poor data quality (DQ) on decision making is often mediated by biased model estimation. To highlight this perspective, we develop an analytical framework that links three quality levels – data, model, and decision. The general framework is first developed at a high-level, and then extended further toward understanding the effect of incomplete datasets on Linear Discriminant Analysis (LDA) classifiers. The interplay between the three quality levels is evaluated analytically - initially for a one-dimensional case, and then for multiple dimensions. The impact is then further analyzed through several simulative experiments with artificial and real-world datasets. The experiment results support the analytical development and reveal nearly-exponential decline in the decision error as the completeness level increases. To conclude, we discuss the framework and the empirical findings, elaborate on the implications of our model on the data quality management, and the use of data for decision-models estimation

Judging a socially assistive robot (SAR) by its cover; The effect of body structure, outline, and color on users' perception

Socially assistive robots (SARs) aim to provide assistance through social interaction. Previous studies contributed to understanding users` perceptions and preferences regarding existing commercially available SARs. Yet, very few studies regarding SARs' appearance used designated SAR designs, and even fewer evaluated isolated visual qualities (VQ). In this work, we aim to assess the effect of isolated VQs systematically. To achieve this, we first conducted market research and deconstructed the VQs attributed to SARs. Then, a reconstruction of body structure, outline, and color scheme was done, resulting in the creation of 30 new SAR models that differ in their VQs, allowing us to isolate one character at a time. We used these new designs to evaluate users' preferences and perceptions in two empirical studies. Our empirical findings link VQs with perceptions of SAR characteristics. These can lead to forming guidelines for the industrial design processes of new SARs to match user expectations.Comment: Submitted to Transactions on Human-Robot Interactio