Supporting Telecommunication Alarm Management System with Trouble Ticket Prediction

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule- based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies has become imperative to enhance efficiency. In this paper, a data-driven Trouble Ticket prediction models are proposed to leverage Alarm Management Systems. To improve performance, feature extraction, using a sliding time-window and feature engineering, from related history alarm streams is also introduced. The models were trained and validated with a data-set provided by the largest telecommunication provider in Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with Trouble Ticket prediction

PICT-DPA: A Quality-Compliance Data Processing Architecture to Improve the Performance of Integrated Emergency Care Clinical Decision Support System

Emergency Care System (ECS) is a critical component of health care systems by providing acute resuscitation and life-saving care. As a time-sensitive care operation system, any delay and mistake in the decision-making of these EC functions can create additional risks of adverse events and clinical incidents. The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the aforementioned EC functions. However, the literature is scarce on how to implement and evaluate the EC-CDSS with regard to the improvement of PHOs, which is the ultimate goal of ECS. The reasons are twofold: 1) lack of clear connections between the implementation of EC-CDSS and PHOs because of unknown quality attributes; and 2) lack of clear identification of stakeholders and their decision processes. Both lead to the lack of a data processing architecture for an integrated EC-CDSS that can fulfill all quality attributes while satisfying all stakeholders’ information needs with the goal of improving PHOs. This dissertation identified quality attributes (PICT: Performance of the decision support, Interoperability, Cost, and Timeliness) and stakeholders through a systematic literature review and designed a new data processing architecture of EC-CDSS, called PICT-DPA, through design science research. The PICT-DPA was evaluated by a prototype of integrated PICT-DPA EC-CDSS, called PICTEDS, and a semi-structured user interview. The evaluation results demonstrated that the PICT-DPA is able to improve the quality attributes of EC-CDSS while satisfying stakeholders’ information needs. This dissertation made theoretical contributions to the identification of quality attributes (with related metrics) and stakeholders of EC-CDSS and the PICT Quality Attribute model that explains how EC-CDSSs may improve PHOs through the relationships between each quality attribute and PHOs. This dissertation also made practical contributions on how quality attributes with metrics and variable stakeholders could be able to guide the design, implementation, and evaluation of any EC-CDSS and how the data processing architecture is general enough to guide the design of other decision support systems with requirements of the similar quality attributes

Designing a framework for data populating alarms based on MITRE techniques

In this paper we aim to develop a proof of concept framework as a step-by-step process for identifying what type of information and log types a SOC analyst needs to analyze and handle an alarm based on the alarms MITRE technique. To solve this, it was decided that using both theoretical and experimental research methodologies could be advantageous. Hence we first used a Systematic Literature Review to search, screen, and select relevant literature. Followed by the usage of Design Science Research method for conducting the research based upon a theoretical basis, and an experimental process. To develop a framework consisting of an easy to understand and independent step-by-step process. The proof of concept framework introduced in this paper, is an eight step process describing how one may proceed when gathering data needed for automating information gathering based on alarms MITRE techniques. In these eight steps it revolves around three main concepts, which are gathering a theoretical foundation by research and discussion, improving the theoretical foundation by testing and adjusting, and ends with a continuous process of maintaining the constructed automations when used in a production setting. This framework produced accurate results when tested during research, and we believe it should be further explored and tested in a larger scale. Also it should be considered a stepping stone into further automating the whole alarm handling process, from gathering data to response

Designing a framework for data populating alarms based on mitre techniques

In this paper we aim to develop a proof of concept framework as a step-by-step process for identifying what type of information and log types a SOC analyst needs to analyze and handle an alarm based on the alarms MITRE technique. To solve this, it was decided that using both theoretical and experimental research methodologies could be advantageous. Hence we first used a Systematic Literature Review to search, screen, and select relevant literature. Followed by the usage of Design Science Research method for conducting the research based upon a theoretical basis, and an experimental process. To develop a framework consisting of an easy to understand and independent step-by-step process. The proof of concept framework introduced in this paper, is an eight step process describing how one may proceed when gathering data needed for automating information gathering based on alarms MITRE techniques. In these eight steps it revolves around three main concepts, which are gathering a theoretical foundation by research and discussion, improving the theoretical foundation by testing and adjusting, and ends with a continuous process of maintaining the constructed automations when used in a production setting. This framework produced accurate results when tested during research, and we believe it should be further explored and tested in a larger scale. Also it should be considered a stepping stone into further automating the whole alarm handling process, from gathering data to response

Influence of artificial intelligence on the work design of emergency department clinicians:a systematic literature review

Objective: This systematic literature review aims to demonstrate how Artificial Intelligence (AI) is currently used in emergency departments (ED) and how it alters the work design of ED clinicians. AI is still new and unknown to many healthcare professionals in emergency care, leading to unfamiliarity with its capabilities. Method: Various criteria were used to establish the suitability of the articles to answer the research question. This study was based on 34 selected peer-reviewed papers on the use of Artificial Intelligence (AI) in the Emergency Department (ED), published in the last five years. Drawing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, all articles were scanned, read full-text, and analyzed afterward. Results: The majority of the AI applications consisted of AI-based tools to aid with clinical decisions and to relieve overcrowded EDs of their burden. AI support was mostly offered during triage, the moment that sets the patient trajectory. There is ample evidence that AI-based applications could improve the clinical decision-making process. Conclusion: The use of AI in EDs is still in its nascent stages. Many studies focus on the question of whether AI has clinical utility, such as decision support, improving resource allocation, reducing diagnostic errors, and promoting proactivity. Some studies suggest that AI-based tools essentially have the ability to outperform human skills. However, it is evident from the literature that current technology does not have the aims or power to do so. Nevertheless, AI-based tools can impact clinician work design in the ED by providing support with clinical decisions, which could ultimately help alleviate a portion of the increasing clinical burden

VIPE: A NEW INTERACTIVE CLASSIFICATION FRAMEWORK FOR LARGE SETS OF SHORT TEXTS - APPLICATION TO OPINION MINING

International audienceThis paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. From a manual multi-label pre-classification of a very limited text subset, a learning algorithm predicts the labels of the remaining texts of the corpus and the texts most likely associated to a selected label. Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly. Experimental results on classical datasets of various sizes and feedbacks of users from marketing services of the telecommunication company Orange confirm the quality of the obtained results

Contributions from computational intelligence to healthcare data processing

80 p.The increasing ability to gather, store and process health care information, through the electronic health records and improved communication methods opens the door for new applications intended to improve health care in many different ways. Crucial to this evolution is the development of new computational intelligence tools, related to machine learning and statistics. In this thesis we have dealt with two case studies involving health data. The first is the monitoring of children with respiratory diseases in the pediatric intensive care unit of a hospital. The alarm detection is stated as a classification problem predicting the triage selected by the nurse or medical doctor. The second is the prediction of readmissions leading to hospitalization in an emergency department of a hospital. Both problems have great impact in economic and personal well being. We have tackled them with a rigorous methodological approach, obtaining results that may lead to a real life implementation. We have taken special care in the treatment of the data imbalance. Finally we make propositions to bring these techniques to the clinical environment

2022 - The Third Annual Fall Symposium of Student Scholars

The full program book from the Fall 2022 Symposium of Student Scholars, held on November 17, 2022. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1026/thumbnail.jp