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Terabe ML, Massago M, Iora PH, Hernandes Rocha TA, de Souza JVP, Huo L, Massago M, Senda DM, Kobayashi EM, Vissoci JR, Staton CA, de Andrade L. Applicability of machine learning technique in the screening of patients with mild traumatic brain injury. PLoS One 2023; 18:e0290721. [PMID: 37616279 PMCID: PMC10449130 DOI: 10.1371/journal.pone.0290721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.
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Affiliation(s)
- Miriam Leiko Terabe
- Postgraduate Program in Management, Technology and Innovation in Urgency and Emergency, State University of Maringa, Maringa, Parana, Brazil
| | - Miyoko Massago
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
| | - Pedro Henrique Iora
- Department of Medicine, State University of Maringa, Maringa, Parana, Brazil
| | | | - João Vitor Perez de Souza
- Postgraduate Program in Biosciences and Physiopathology, State University of Maringa, Maringa, Parana, Brazil
| | - Lily Huo
- Duke Global Health Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Mamoru Massago
- Postgraduate Program in Computer Sciences, State University of Maringa, Maringa, Parana, Brazil
| | - Dalton Makoto Senda
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
| | | | - João Ricardo Vissoci
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
- Duke Global Health Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Catherine Ann Staton
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
- Duke Global Health Institute, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Luciano de Andrade
- Postgraduate Program in Management, Technology and Innovation in Urgency and Emergency, State University of Maringa, Maringa, Parana, Brazil
- Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil
- Department of Medicine, State University of Maringa, Maringa, Parana, Brazil
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Aldraimli M, Soria D, Grishchuck D, Ingram S, Lyon R, Mistry A, Oliveira J, Samuel R, Shelley LEA, Osman S, Dwek MV, Azria D, Chang-Claude J, Gutiérrez-Enríquez S, De Santis MC, Rosenstein BS, De Ruysscher D, Sperk E, Symonds RP, Stobart H, Vega A, Veldeman L, Webb A, Talbot CJ, West CM, Rattay T, Chaussalet TJ. A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. Comput Biol Med 2021; 135:104624. [PMID: 34247131 DOI: 10.1016/j.compbiomed.2021.104624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 11/20/2022]
Abstract
The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.
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Affiliation(s)
- Mahmoud Aldraimli
- The Health Innovation Ecosystem, University of Westminster, London, UK.
| | - Daniele Soria
- School of Computing, University of Kent, Canterbury, UK
| | | | - Samuel Ingram
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK
| | - Robert Lyon
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, UK
| | - Anil Mistry
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Robert Samuel
- University of Leeds, Leeds Cancer Centre, St. James's University Hospital, Leeds, UK
| | - Leila E A Shelley
- Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh, UK
| | - Sarah Osman
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Miriam V Dwek
- School of Life Sciences, University of Westminster, London, UK
| | | | - Jenny Chang-Claude
- German Cancer Research Center (DKFZ) Division of Cancer Epidemiology, Unit of Genetic Epidemiology, Heidelberg, Germany
| | | | - Maria Carmen De Santis
- Dept of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Dirk De Ruysscher
- Maastricht Radiation Oncology (MAASTRO Clinic) University Hospital Maastricht, the Netherlands
| | - Elena Sperk
- Department of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Germany
| | | | | | - Ana Vega
- Fundación Publica Galega Medicina Xenomica, Santiago de Compostela, Spain
| | - Liv Veldeman
- Department of Basic Medical Sciences, University Hospital Ghent, Belgium
| | - Adam Webb
- Department of Genetics and Genome Biology, University of Leicester, UK
| | | | - Catharine M West
- Institute of Cancer Sciences, Christie Hospital, Wilmslow Road, Manchester, UK
| | - Tim Rattay
- Cancer Research Centre, University of Leicester, Leicester, UK
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