Experts’ Perspectives on Use of Fast Healthcare Interoperable Resources for Computerized Clinical Decision Support

Shear, Kristen; Horgas, Ann L.; Lucero, Robert

doi:10.1097/cin.0000000000001033

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address interoperability challenges, a crucial step is to develop standardized data and exchange protocols that are uniform across the healthcare industry. Adopting standards like Fast Healthcare Interoperability Resources (FHIR) can enable consistent data exchange that can be interpreted by various systems [115]- [117]. Furthermore, leveraging Application Programming Interfaces (APIs) can facilitate communication and data integration across different platforms [118], [119].…”

Section: E Interoperabilitymentioning

confidence: 99%

Application of Machine Learning in Healthcare and Medicine: A Review

Furizal,

Ma'arif,

Rifaldi

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.

show abstract

Section: E Interoperabilitymentioning

confidence: 99%

Application of Machine Learning in Healthcare and Medicine: A Review

Furizal,

Ma'arif,

Rifaldi

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

Processo de modelagem da informação em saúde: uma abordagem ao contexto brasileiro

De Souza-Zinader,

Leitão Junior,

De Oliveira

et al. 2024

J Health Inform

View full text Add to dashboard Cite

A informação em saúde para ser efetiva deve ser estruturada e codificada, tal que haja uma única interpretação, independentemente do local e do momento em que a mesma foi produzida e será consumida. A modelagem dessa informação é um ponto crucial para o alcance desse propósito, na medida em que sistematiza o emprego de métodos e padrões no desenvolvimento de modelos de informação, com a aquiescência dos atores da saúde comprometidos no processo. O presente estudo apresenta um processo completo de modelagem da informação em saúde, apropriada ao contexto brasileiro na medida em que adota os padrões da Rede Nacional de Dados em Saúde (RNDS). O processo está em fase de aplicação e validação, no contexto de uma cooperação entre uma Instituição Pública de Ensino Superior e uma Secretaria de Estado da Saúde, cuja finalidade é a padronização de informações em saúde.

show abstract

Experts’ Perspectives on Use of Fast Healthcare Interoperable Resources for Computerized Clinical Decision Support

Cited by 2 publications

References 17 publications

Application of Machine Learning in Healthcare and Medicine: A Review

Application of Machine Learning in Healthcare and Medicine: A Review

Processo de modelagem da informação em saúde: uma abordagem ao contexto brasileiro

Contact Info

Product

Resources

About