Bio: Brijendra Singh is an academic researcher from VIT University. The author has contributed to research in topic(s): Information technology & Package development process. The author has an hindex of 3, co-authored 5 publication(s) receiving 37 citation(s).
01 Jan 2013
TL;DR: The use of Electronic Health Records and barriers in using it among nurses in private medium sized hospitals of Tamil Nadu, India are explored and the factors affecting nurses to adopt electronic health record are analyzed.
Abstract: Electronic Health Record has potential to improve patient care by managing patient's medical and personal information efficiently and effectively. It is easy to maintain patient information electronically compared to paper based records. Many studies have been done in other countries to study the effective use of Electronic Health Record, but a small number of studies exist in Indian situation. This study is a footstep in this route. This study has been done to know the use of electronic health records among nurses in private medium sized hospitals of Tamil Nadu, India. The objective of the study is to explore the use of Electronic Health Records and barriers in using it among nurses. This study also analyzes the factors affecting nurses to adopt electronic health record. Only a third of the nurses (33%) use electronic health record. Lack of training is the major hindrance in use electronic health record among nurses.
••01 Nov 2017
TL;DR: This paper is classifying sentiment of Twitter messages by exhibiting results of a machine learning algorithm using R and Rapid Miner and categorizing them in neutral, negative and positive sentiments finally summarising the results as a whole.
Abstract: Social Media has taken the world by surprise at a swift and commendable pace. With the advent of any kind of circumstances may it be related to social, political or current affairs the sentiments of people throughout the world are expressed through their help, making them suitable candidates for sentiment mining. Sentimental analysis becomes highly resourceful for any organization who wants to analyse and enhance their products and services. In the airline industries it is much easier to get feedback from astute data source such as Twitter, for conducting a sentiment analysis on their respective customers. The beneficial factors relating to twitter sentiment analysis cannot be impeded by the consumers who want to know the who's who and what's what in everyday life. In this paper we are classifying sentiment of Twitter messages by exhibiting results of a machine learning algorithm using R and Rapid Miner. The tweets are extracted and pre-processed and then categorizing them in neutral, negative and positive sentiments finally summarising the results as a whole. The Naive Bayes algorithm has been used for classifying the sentiments of recent tweets done on the different airlines.
01 Jan 2020-Health technology
TL;DR: This paper hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions of nurses using computers and information technology in Indian healthcare system.
Abstract: Computational intelligence innovation and the use of computers have changed the entire healthcare delivery system. Nurses are the leading crew of healthcare organization. But, these nurses are either lacking in computer usage or automated analysis generated by computers. Therefore, it motivates to study the use of computers and information technology by nurses in Indian healthcare system. Further, it is essential to identify the chief factors where these nurses are lacking while using computers and information technology. This will help the management to take necessary measure to train them and make the healthcare industry more productive in perception with usage of computer and information technology. To this end, data has collected from nurses in hospitals in the state of Tamilnadu, India. Data collection is not beneficial unless it is analyzed and meaningful information obtained from it. In this paper, we hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions. Rough set is used to analyze the data and to generate rules. These generated rules further passed into formal concept analysis to identify the chief characteristics affecting the decisions. This in turn help the organization to provide adequate training to the nurses and the healthcare system will move further to the next stage.
TL;DR: Assessment of the comfort level and frequency in the use of information technology among nurses and the relationship between various factors governing their use found the influence on use of computer hardware, communication and administrative related activities using computers was found.
Abstract: The aim of the study was to assess the comfort level and frequency in the use of information technology (IT) among nurses and analyze the relationship between various factors governing their use. A survey was conducted during 2013-14 at select Indian Hospitals of Tamil Nadu State. Correlation analysis and ANOVA test were used to analyze the data. The nurses working in the medium sized hospitals in the State are the study population. A random sample of 600 nurses was selected for the study. Gender, qualification and age level were analyzed to found the influence on use of computer hardware, communication and administrative related activities using computers. Effective and majority use of computers for information processing activities, communication, planning and policy development activities, finance, administration, education and research can lead healthcare organization to run their processes efficiently. Hospitals can provide appropriate computer training programs and computers access to nurses to realize the benefits from IT. Healthcare professionals may encourage nurses to use IT and computers at their work place by providing them attractive incentives. DOI: 10.5901/mjss.2015.v6n4s2p658
01 Jan 2013
TL;DR: This paper aims to provide an intuition on using Artificial Intelligence (AI) in the different phases of the software development lifecycle using a specific software development example for clarity and precision.
Abstract: Software development is a sequential process where the allied steps in the development lifecycle involve planning and modularization, requirement engineering, analysis of product viability, profits estimation, strategic decision making, maintenance strategies etc. Often, most of these phases are pretty complex and thereby, extremely difficult to handle solely through human intervention, mainly due to the size of the project, the number of factors to be taken into consideration at each modular level and the rapidly changing external environment. In this paper we aim to provide an intuition on using Artificial Intelligence (AI) in the different phases of the software development lifecycle. Our paper focuses on a specific software development example for clarity and precision, but most of the techniques are highly general and scalable to any software development process.
01 Feb 2018-Journal of Clinical Nursing
TL;DR: The study affirmed the poor quality of nursing documentation and lack of nurses' knowledge and skills in the nursing process and its application in both paper-based and electronic-based systems.
Abstract: Aim and Objective To assess and compare the quality of paper-based and electronic-based health records. The comparison examined three criteria: content, documentation process, and structure. Background Nursing documentation is a significant indicator of the quality of patient care delivery. It can be either paper-based or organized within the system known as the Electronic Health Records (EHRs). Nursing documentation must be completed at the highest standards, in order to ensure the safety and quality of health care services. However, the evidence is not clear on which one of the two forms of documentation (paper-based versus EHRs) is more qualified. Methods A retrospective, descriptive, comparative design was utilized to address the study's purposes. A convenient number of patients’ records, from two public hospitals, were audited using the Cat-ch-Ing Audit Instrument. The sample size consisted of 434 records for both paper-based health records and EHRs from medical and surgical wards. Results EHRs were better than paper-based health records in terms of process and structure. In terms of quantity and quality content, paper-based records were better than EHRs. The study affirmed the poor quality of nursing documentation and lack of nurses’ knowledge and skills in the nursing process and its application in both paper-based and electronic-based systems. Conclusion Both forms of documentation revealed drawbacks in terms of content, process, and structure. This study provided important information, which can guide policymakers and administrators in identifying effective strategies aimed at enhancing the quality of nursing documentation. Relevance to Clinical Practice Policies and actions to ensure quality nursing documentation at the national level should focus on improving nursing knowledge, competencies, practice in nursing process, enhancing the work environment and nursing workload, as well as strengthening the capacity building of nurses practice to improve the quality of nursing care and patients’ outcomes. This article is protected by copyright. All rights reserved.
01 Dec 2019-Journal of Big Data
TL;DR: This study presents a machine learning approach to analyze the tweets to improve the customer’s experience and found that convolutional neural network (CNN) outperformed SVM and ANN models.
Abstract: Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience.
TL;DR: Assessment of the attitude, use, and hindering factors of health professionals’ use of EMR in one referral hospital in Ethiopia found that majority of the respondents used the EMR system in their daily works and more than half of them had a good attitude towards EMR.
Abstract: Background and Purpose: Electronic medical record (EMR) systems are increasingly incorporated into the healthcare systems of developing countries to improve the effectiveness and efficiency of the healthcare institutions. Inaccuracy, non-timeliness, incompleteness and inconsistency of paper-based data are basic triggering points to adopt EMRs in developing countries. The purpose of this study was to assess the attitude, use, and hindering factors of health professionals’ use of EMR in one referral hospital in Ethiopia that has used the EMR for the last two years. Methods: An institutional based cross-sectional quantitative study was conducted in March 2014 among 501 health professionals. Self-administered questionnaire was used to collect data. Data were entered and analysed using Epi-Info version 7 and SPSS version 20 respectively. Descriptive statistics were computed to describe study variables. Bivariate and multivariate logistic regression analyses were used to show the presence of association between the study and outcome variables. Odds ratio at 95% confidence level was used to describe the strength of association. Results: A total of 428 health professionals participated in the study with a response rate of 86%. The majority, 318 (74.3%) were computer literate and more than half, 246 (57.5%) of them had computer access. A large number (71.0%) of respondents used EMR and more than half (56.1%) had a good attitude towards it. Health professionals’ age, computer literacy, computer assess, working experience, regular meeting and training on the EMR system were significant factors (p-value <0.05) to the attitude and use of EMR system. Educational level, knowledge on EMR and using EMR were also variables affecting users’ attitude towards EMR. Conclusions: Majority of the respondents used the EMR system in their daily works and more than half of them had a good attitude towards EMR. Technical (computer literacy, knowledge), organizational (computer access, infrastructure, training access, regular meeting, management support), and personal (age, working experience) variables are significant factors to develop a good attitude towards and high use of the system. Improving skills, awareness, infrastructure, management and resource allocation are important interventions to improve the EMR system performance and positive attitude towards health professionals in the study area. Keywords: Electronic Medical Record, Ethiopia, Attitude.
02 Dec 2017-Acta medica Iranica
TL;DR: The highest priority in strength analysis was related to timely and quick access to information, but lack of hardware and infrastructures was the most important weakness and the most substantial threats were the lack of strategic planning in the field of electronic health records.
Abstract: Electronic Health Record (EHR) is one of the most important achievements of information technology in healthcare domain, and if deployed effectively, it can yield predominant results. The aim of this study was a SWOT (strengths, weaknesses, opportunities, and threats) analysis in electronic health record implementation. This is a descriptive, analytical study conducted with the participation of a 90-member work force from Hospitals affiliated to Tehran University of Medical Sciences (TUMS). The data were collected by using a self-structured questionnaire and analyzed by SPSS software. Based on the results, the highest priority in strength analysis was related to timely and quick access to information. However, lack of hardware and infrastructures was the most important weakness. Having the potential to share information between different sectors and access to a variety of health statistics was the significant opportunity of EHR. Finally, the most substantial threats were the lack of strategic planning in the field of electronic health records together with physicians' and other clinical staff's resistance in the use of electronic health records. To facilitate successful adoption of electronic health record, some organizational, technical and resource elements contribute; moreover, the consideration of these factors is essential for HER implementation.
25 Jul 2020-ICT Express
TL;DR: It is demonstrated that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis, and deep learning algorithms are found to be better than machine learning algorithms.
Abstract: People nowadays use emoticons in their text in increasing order in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF-IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.