Feature-Based Learning in Drug Prescription System for Medical Clinics

Goh, Wee Pheng; Tao, Xiaohui; Zhang, Ji; Yong, Jianming

doi:10.1007/s11063-020-10296-7

Cited by 5 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The data obtained can be used for automated analysis of synthetic availability of OCs, data extraction on the potential biological activity of OCs (including drugs), molecular mechanisms, metabolism, and toxicity. These observations explain researchers’ interest in this topic − and the development of many methods aimed at extracting and linking chemical and biological data in recent years. − …”

Section: Introductionmentioning

confidence: 83%

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

Tarasova

Biziukova

Rudik

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rulebased algorithm and application of a machine-learning algorithm (conditional random fields).

show abstract

Section: Introductionmentioning

confidence: 83%

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

Tarasova

Biziukova

Rudik

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Alguns outros fatores que podem contribuir para os erros de prescrição, como desconhecimento do uso de tecnologias na elaboração da receita, falhas na linha de comunicação, fragmentando o sistema de saúde em resultados falsos para os pacientes e dificuldade em análise das causas da doença (GOH et al, 2020). Diante desse cenário, este presente estudo objetivou realizar uma revisão da literatura identificando os principais erros de prescrição hospitalares.…”

Section: Introductionunclassified

Percepção De Estudantes Universitários Sobre O Ensino À Distância Durante a Pandemia Do Covid-19

Oliveira

2023

Anais Do(a) Anais Do Congresso Brasileiro Interdisciplinar Em Ciência E Tecnologia

View full text Add to dashboard Cite

Introdução: Os erros que se produzem em uma receita médica podem ser potencialmente lesivos a saúde. Sendo frequentemente descritos como erros humanos na assistência à saúde. Podendo gerar acidentes terapêuticas que variam de efeitos colaterais leves a fatais. Contudo, os erros de prescrição e a gravidade destes estão sujeitas a debates e suas causas podem ser determinadas. Objetivo: Realizar uma revisão da literatura identificando os principais erros de prescrição hospitalares. Metodologia: Revisão bibliográfica de literatura, através de consulta às bases de dados Scielo, PubMed e Lilacs, tendo sido utilizados os Descritores em Ciência da Saúde (DeCS) de forma associada"Prescrição inadequada", "Erros" e "Hospital", no idioma português, e também os descritores: "Inappropriate Prescribing", "Errors" AND "Hospital" na língua inglesa. Resultados e Discussão: Foram encontrados 246 artigos resultados nas três bases de dados e foram definidas três etapas de análise de filtragem. Na primeira análise, a partir dos títulos dos materiais encontrados foram selecionados 51 títulos por se encaixarem nos critérios de exclusão estabelecidos. Na segunda etapa da análise, que se deu a partir da leitura dos resumos foram selecionados 6 artigos. Entre as principais evidências associadas aos erros de prescrições está o erro de dosagem onde é relatado nos estudos de Slight et al (2019), Mulac et al (2021), Lucchetta et al (2018 e Anzan et al (2021). Considerações finais: Com base na literatura analisada, conclui-se que os autores são congruentes em relação as principais evidências de erros de prescrições no ambiente hospitalar, sendo o erro de dosagem o mais relatado. A prevenção de erros de medicação requer esforços em todos os níveis da área da saúde.

show abstract

“…To generalize to any symptom set, the prescription generation task focuses on modeling the relationships between symptoms and herbs, which is analogous to the native recommendation task that models the interactions between users and items. Artificial Intelligence (AI) is an emerging technology that could be used in learning features or interactions among lots of relationship-unknown objects [10,11]. With the development of machine learning in AI, the task of recognizing the pattern of prescriptions and automatic generating prescriptions has recently received the attention of the research community.…”

Section: Introductionmentioning

confidence: 99%

TPGen: Prescription Generation Using Knowledge-guided Translator

Ruan¹,

Hua²,

Wu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Prescriptions contain a lot of clinical information and play a pivotal role in the clinical diagnosis of Traditional Chinese Medicine (TCM), which is a combination of herb to treat the symptoms of a patient from decision-making of doctors. In the process of clinical decision-making, a large number of prescriptions have been invented and accumulated based on TCM theories. Mining complex and the regular relationships between symptoms and herbs in the prescriptions are important for both clinical practice and novel prescription development. Previous work used several machine learning methods to discover regularities and generate prescriptions but rarely used TCM knowledge to guide prescription generation and described why each herb is predicted for treating a symptom. Methods: In this work, we employ a machine translation mechanism and propose a novel sequence-to-sequence (seq2seq) architecture termed TPGen to generate prescriptions. TPGen consisting of an encoder and a decoder is a well-known framework for resolving the machine translation problem in the natural language processing (NLP) domain. We use the lite transformer and Bi-directional Gate Recurrent Units(Bi-GRUS) as a fundamental model in TPGen, and integrate TCM clinical knowledge to guide the model improvement termed TPGen+. Results: We conduct extensive experiments on a public TCM dataset and clinical data. The experimental results demonstrate that our proposed model is effective and outperforms other state-of-the-art methods in TCM expert evaluation. The approach will be beneficial for clinical prescription discovery and diagnosis

show abstract

Feature-Based Learning in Drug Prescription System for Medical Clinics

Cited by 5 publications

References 33 publications

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

Percepção De Estudantes Universitários Sobre O Ensino À Distância Durante a Pandemia Do Covid-19

TPGen: Prescription Generation Using Knowledge-guided Translator

Contact Info

Product

Resources

About