An Augmented Reality-Based Mobile Application for Drug Prescribing Information System: ARPECTUS

2021 ◽  
Author(s):  
Nigar Kucuk ◽  
Sezin Barin ◽  
Gur Emre Guraksin
Keyword(s):  
Information System ◽  
Augmented Reality ◽  
Mobile Application ◽  
Drug Prescribing ◽  
Prescribing Information

Design features of the augmented reality client-server information system

2021 ◽  
Vol 6 ◽  
Author(s):  
M.G. Mezin ◽  
◽  
D.Yu. Syedin
Keyword(s):  
Information System ◽  
Information Systems ◽  
Augmented Reality ◽  
Mobile Application ◽  
Design Features ◽  
Client Server ◽  
Remote Server ◽  
Server Architecture ◽  
Client Server Architecture

This article is devoted to the issue of designing an augmented reality information system with a «client-server» architecture. The advantage of creating augmented reality information systems with type of architecture is the ability to quickly place content on a remote server and then display it through a client mobile application. Thus, a simplified user’s interaction with augmented reality content is provided. The work reflects the main stages of designing an information system with the specified type of architecture.

Design and Development of Augmented Reality (AR) Mobile Application for Malolos' Kameztizuhan (Malolos Heritage Town, Philippines)

2018 ◽  
Author(s):  
Dennis R. dela Cruz ◽  
Jerico S.A. Sevilla ◽  
Joshua Wilfred D. San Gabriel ◽  
Angelica Joyce P. Dela Cruz ◽  
Ella Joyce S. Caselis
Keyword(s):  
Augmented Reality ◽  
Mobile Application ◽  
Design And Development

scholarly journals Development of Mobile Augmented Reality Based Media for An Electrical Measurement Instrument

2021 ◽  
Vol 2111 (1) ◽  
pp. 012029
Author(s):  
Y M Nursita ◽  
S Hadi
Keyword(s):  
Augmented Reality ◽  
Mobile Application ◽  
Electrical Measuring ◽  
Measurement Instrument ◽  
Electrical Measurement ◽  
Measuring Instruments ◽  
Development Research ◽  
Measurement Instruments ◽  
Electrical Field ◽  
Virtual Objects

Abstract The research aims to develop mobile learning media with augmented reality for electrical measurement instruments. The learner can use this application to improve their skills and knowledge about using electrical measurement instruments correctly. One of the essential skills for electricians is using voltmeter, ammeter, and ohmmeter. The result of measuring they can do some analysis about an issue or troubleshooting the electrical field. From the development research, was produced learning media application product was named ARAVO. ARAVO is an abbreviation of augmented reality of Ammeter, Voltmeter, and Ohmmeter. ARAVO helps learners and even lecturers to simulate the use of electrical measuring instruments by combining virtual objects such as multimeter with the real world. Thus will provide a more visible visualization of how to use electrical measuring instruments before they practice directly with actual measuring instruments. ARAVO is a mobile application that can run on the smartphone platform mobile Android. Into the development of this application must go through several stages before it is ready for use.

scholarly journals Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Clifford Nangle ◽  
Stuart McTaggart ◽  
Margaret MacLeod ◽  
Jackie Caldwell ◽  
Marion Bennie
Keyword(s):  
Information System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Drug Exposure ◽  
Drug Dose ◽  
Free Text ◽  
Wide Range ◽  
The Impact ◽  
Prescribing Information

ABSTRACT ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.

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