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Cardoso MMDA, Machado-Rugolo J, Thabane L, da Rocha NC, Barbosa AMP, Komoda DS, de Almeida JTC, Curado DDSP, Weber SAT, de Andrade LGM. Application of natural language processing to predict final recommendation of Brazilian health technology assessment reports. Int J Technol Assess Health Care 2024; 40:e19. [PMID: 38605654 DOI: 10.1017/s0266462324000163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
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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Affiliation(s)
- Marilia Mastrocolla de Almeida Cardoso
- Health Technology Assessment Unit, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
- Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
| | - Juliana Machado-Rugolo
- Health Technology Assessment Unit, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
- Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
| | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Biostatistics Unit, St Joseph's Healthcare Hamilton, Hamilton, ON, Canada
- Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
| | - Naila Camila da Rocha
- Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
| | - Abner Mácula Pacheco Barbosa
- Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
- Department of Ophthalmology, Otorhinolaryngology and Head and Neck Surgery, Medical School (FMB) of São Paulo State University, Botucatu, Brazil
| | | | - Juliana Tereza Coneglian de Almeida
- Health Technology Assessment Unit, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
- Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
| | - Daniel da Silva Pereira Curado
- Department of Management and Incorporation of Health Technologies, Ministry of Health, Brasilia, Distrito Federal, Brazil
| | - Silke Anna Theresa Weber
- Health Technology Assessment Unit, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
- Department of Ophthalmology, Otorhinolaryngology and Head and Neck Surgery, Medical School (FMB) of São Paulo State University, Botucatu, Brazil
| | - Luis Gustavo Modelli de Andrade
- Laboratory of Data Science and Predictive Analysis in Health, Hospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, Brazil
- Department of Internal Medicine, Medical School (FMB) of São Paulo State University, Botucatu, Brazil
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Tyagi N, Bhushan B. Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:857-908. [PMID: 37168438 PMCID: PMC10019426 DOI: 10.1007/s11277-023-10312-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.
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Affiliation(s)
- Nemika Tyagi
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
| | - Bharat Bhushan
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
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Multitask Learning Based on Improved Uncertainty Weighted Loss for Multi-Parameter Meteorological Data Prediction. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
With the exponential growth in the amount of available data, traditional meteorological data processing algorithms have become overwhelmed. The application of artificial intelligence in simultaneous prediction of multi-parameter meteorological data has attracted much attention. However, existing single-task network models are generally limited by the data correlation dependence problem. In this paper, we use a priori knowledge for network design and propose a multitask model based on an asymmetric sharing mechanism, which effectively solves the correlation dependence problem in multi-parameter meteorological data prediction and achieves simultaneous prediction of multiple meteorological parameters with complex correlations for the first time. The performance of the multitask model depends largely on the relative weights among the task losses, and manually adjusting these weights is a difficult and expensive process, which makes it difficult for multitask learning to achieve the expected results in practice. In this paper, we propose an improved multitask loss processing method based on the assumptions of homoscedasticity uncertainty and the Laplace loss distribution and validate it using the German Jena dataset. The results show that the method can automatically balance the losses of each subtask and has better performance and robustness.
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Şenel LK, Şahinuç F, Yücesoy V, Schütze H, Çukur T, Koç A. Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Lage-Freitas A, Allende-Cid H, Santana O, Oliveira-Lage L. Predicting Brazilian Court Decisions. PeerJ Comput Sci 2022; 8:e904. [PMID: 35494851 PMCID: PMC9044329 DOI: 10.7717/peerj-cs.904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Predicting case outcomes is useful for legal professionals to understand case law, file a lawsuit, raise a defense, or lodge appeals, for instance. However, it is very hard to predict legal decisions since this requires extracting valuable information from myriads of cases and other documents. Moreover, legal system complexity along with a huge volume of litigation make this problem even harder. This paper introduces an approach to predicting Brazilian court decisions, including whether they will be unanimous. Our methodology uses various machine learning algorithms, including classifiers and state-of-the-art Deep Learning models. We developed a working prototype whose F1-score performance is ~80.2% by using 4,043 cases from a Brazilian court. To our knowledge, this is the first study to present methods for predicting Brazilian court decision outcomes.
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Affiliation(s)
- André Lage-Freitas
- Universidade Federal de Alagoas, Maceió, Brazil
- JusPredict, Salvador, Brazil
| | - Héctor Allende-Cid
- JusPredict, Salvador, Brazil
- Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Orivaldo Santana
- JusPredict, Salvador, Brazil
- Universidade Federal do Rio Grande do Norte, Natal, Brazil
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