Computational Intelligence Based Electronic Healthcare Data Analytics Using Feature Selection with Classification by Deep Learning Architecture

EHRs (Electronic health records) are a source of big data that offer a wealth of clinical patient health data. However, because these notes are free-form texts, writing formats and styles range greatly amongst various records, text data from eHRs, such as discharge rapid notes, provide analysis challenges. This research proposed novel technique in electronic healthcare data analysis based on feature selection and classification utilizingDL methods. here the input is collected as input EH data, is processed for dimensionality reduction, noise removal. A public data pre-processing method for dealing with HD-EHR data is dimensionality reduction, which tries to minimize amount of EHR representational features while enhancing effectiveness of following data analysis, such as classification. The processed data features has been selected utilizingweighted curvature based feature selection with support vector machine. Then this selected deep features has been classified using sparse encoder transfer learning. the experimental analysis has been carried out for various EH datasets in terms of accuracy of 96%, precision of 92%, recall of 77%, F-1 score of 72%, MAP of 65

Technology in Practice (Section 2.31 of the Comprehensive Clinical Psychology: Vol. 2. Professional Issues)

The contemporary practice of psychology requires a prudent balance of traditional and emerging communication methods. Interpersonal interactions in the context of human relationship (e.g., speech, emotional expressions, and nonverbal gestures) have been a vital part of emotional healing throughout many centuries, and research findings in the 1990s underscore the importance of relational factors in effective psychological interventions (Whiston & Sexton, 1993). In addition to the time honored interpersonal communication methods of professional psychology, rapid technological advances have propelled psychologists into another sphere of communication. Today\u27s professional psychologist is increasingly expected to attain mastery in both of these communication methods-the very old and the very new

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Identifying common problems in the acquisition and deployment of large-scale software projects in the US and UK healthcare systems

Public and private organizations are investing increasing amounts into the development of healthcare information technology. These applications are perceived to offer numerous benefits. Software systems can improve the exchange of information between healthcare facilities. They support standardised procedures that can help to increase consistency between different service providers. Electronic patient records ensure minimum standards across the trajectory of care when patients move between different specializations. Healthcare information systems also offer economic benefits through efficiency savings; for example by providing the data that helps to identify potential bottlenecks in the provision and administration of care. However, a number of high-profile failures reveal the problems that arise when staff must cope with the loss of these applications. In particular, teams have to retrieve paper based records that often lack the detail on electronic systems. Individuals who have only used electronic information systems face particular problems in learning how to apply paper-based fallbacks. The following pages compare two different failures of Healthcare Information Systems in the UK and North America. The intention is to ensure that future initiatives to extend the integration of electronic patient records will build on the ‘lessons learned’ from previous systems