Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics

Goh, Wee Pheng; Tao, Xiaohui; Zhanga, Ji; Yong, Jianming; Qin, Yongrui; Goh, Elizabeth Zhixin; Hu, Aimin

doi:10.1109/besc.2018.8697814

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In addition, within the predictive layer, we propose multiple approaches to deduce the similarity ratio between drugs in a drug-pair to assist doctors in prescribing drugs at pointof-care, in order to find the combination of approaches that yields the best performance. This is in contrast with only a single network model approach in our previous work [11].…”

Section: Introductionmentioning

confidence: 85%

Feature-Based Learning in Drug Prescription System for Medical Clinics

Goh

Tao

Zhang

et al. 2020

Neural Process Lett

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Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered frameworkthe prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes. Keywords Feature vector • Similarity ratio • Word embedding • Adverse network model • Personalised drug prescription This work is partially supported by Glory Dental Surgery Pte Ltd and undertaken collaboratively with their panel of dentists. We would like to thank Dr. Xueling Oh and Dr. Elizabeth Goh for enriching the authors understanding of drug prescription within the medical domain.

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Section: Introductionmentioning

confidence: 85%

Feature-Based Learning in Drug Prescription System for Medical Clinics

Goh

Tao

Zhang

et al. 2020

Neural Process Lett

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“…Based on research findings that similar drugs has similar patterns from word embeddings associated with the textual description of the drugs [4], a prototype can be constructed which aligns with the presentation layer of the conceptual framework.…”

Section: Evolution Of Discovered Drug Interactionsmentioning

confidence: 99%

“…Research work has been done to observe the relationship between drug interactions and word embeddings from the textual data that describe the drugs [4,5]. As cited by Nguyen (2019) from linguist JR Firth that "you shall know a word by the company it keeps" [8], related words and hence similar drugs can be known by finding similar words that describe the drugs.…”

Section: Evolution Of Discovered Drug Interactionsmentioning

confidence: 99%

Personalised Drug Prescription for Dental Clinics Using Word Embedding

Goh

Tao

Zhang

et al. 2020

Communications in Computer and Information Science

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The number of drugs in drug databases is constantly expanding with novel drugs appearing on the market each year. A dentist cannot be expected to recall all the drugs available, let alone potential drug-drug interactions (DDI). This can be problematic when dispensing drugs to patients especially those with multiple medical conditions who often take a multiple medications. Any new medication prescribed must be checked against the patient's medical history, in order to avoid drug allergies and reduce the risk of adverse reactions. Current drug databases allowing the dentist to check for DDI are limited by the lack of integration of the patient's medical profile with the drug to be prescribed. Hence, this paper introduces a software which predicts the possible DDI of a new medication against the patient's medical profile, based on previous findings that associate similarity ratio with DDI. This system is based conceptually on a three-tier framework consisting of a knowledge layer, prediction layer and presentation layer. The novel approach of this system in applying feature vectors for drug prescription will be demonstrated during the conference (http://r.glory.sg/main.php). By engaging with the interactive demonstration, participants will gain first-hand experience in the process from research idea to implementation. Future work includes the extension of use from dental to medical institutions, and it is currently being enhanced to serve as a training tool for medical students.

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Smart Way of Prescribing Medication

Patil

Nillawar

Khatri

et al. 2023

IJARSCT

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This research deals with Implementation of digital signature technology for e-prescription to avoid cybercrime problems such as Theft, modification, unauthorized access, etc. In this research, 2048-bit RSA algorithm will be implemented in Java programming and Android based system. The design of the Secure Electronic Prescription (SEP) application is to combine given services such as confidentiality, authentication and non-repudiation. The Secure Electronic Prescription (SEP)application is intended to combine specific services such as confidentiality, authentication and non-repudiation. prescription details issued to pharmacists only are used to secure encrypted prescriptions and QR codes. There are two schemes in the application namely protection schemes and authentication scheme. This study uses black-box and white-box testing to test input values, code, and outputs without testing the processes and designs occurring in the system. Demonstrate implementation of encryption in Secure Electronic Prescription (SEP). The implementation of a digital signatures in this study can prevent archive theft, which is shown in the implementation and proven in the test.

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Exploring the Use of a Network Model in Drug Prescription Support for Dental Clinics

Cited by 3 publications

References 12 publications

Feature-Based Learning in Drug Prescription System for Medical Clinics

Feature-Based Learning in Drug Prescription System for Medical Clinics

Personalised Drug Prescription for Dental Clinics Using Word Embedding

Smart Way of Prescribing Medication

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