Apresentação

Koga, Natália Massaco; Palotti, Pedro Lucas de Moura; Mello, Janine; Pinheiro, Maurício Mota Saboya

doi:10.38116/978-65-5635-032-5/apresentacao

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…For Furtado and Lassance (40), taking a new decision, or maintaining (or not) the same in public policy, often involves questions of criticality, urgency, dimension, multi-causality, political-institutional multiplicity, and implementation deadline limitations. For the authors, computational models contribute to evaluation structures through inductive and deductive processes based on algorithms.…”

Section: Discussion and Implementation Considerationmentioning

confidence: 99%

Application of natural language processing to predict final recommendation of Brazilian health technology assessment reports

Cardoso,

Machado-Rugolo,

Thabane

et al. 2024

Int J Technol Assess Health Care

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Introduction Health technology assessment (HTA) plays a vital role in healthcare decision-making globally, necessitating the identification of key factors impacting evaluation outcomes due to the significant workload faced by HTA agencies. Objectives The aim of this study was to predict the approval status of evaluations conducted by the Brazilian Committee for Health Technology Incorporation (CONITEC) using natural language processing (NLP). Methods Data encompassing CONITEC’s official report summaries from 2012 to 2022. Textual data was tokenized for NLP analysis. Least Absolute Shrinkage and Selection Operator, logistic regression, support vector machine, random forest, neural network, and extreme gradient boosting (XGBoost), were evaluated for accuracy, area under the receiver operating characteristic curve (ROC AUC) score, precision, and recall. Cluster analysis using the k-modes algorithm categorized entries into two clusters (approved, rejected). Results The neural network model exhibited the highest accuracy metrics (precision at 0.815, accuracy at 0.769, ROC AUC at 0.871, and recall at 0.746), followed by XGBoost model. The lexical analysis uncovered linguistic markers, like references to international HTA agencies’ experiences and government as demandant, potentially influencing CONITEC’s decisions. Cluster and XGBoost analyses emphasized that approved evaluations mainly concerned drug assessments, often government-initiated, while non-approved ones frequently evaluated drugs, with the industry as the requester. Conclusions NLP model can predict health technology incorporation outcomes, opening avenues for future research using HTA reports from other agencies. This model has the potential to enhance HTA system efficiency by offering initial insights and decision-making criteria, thereby benefiting healthcare experts.

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Section: Discussion and Implementation Considerationmentioning

confidence: 99%