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Rahman J, Brankovic A, Tracy M, Khanna S. Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review. Interact J Med Res 2024; 13:e46946. [PMID: 39163610 PMCID: PMC11372324 DOI: 10.2196/46946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/27/2024] [Accepted: 06/26/2024] [Indexed: 08/22/2024] Open
Abstract
BACKGROUND Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration. OBJECTIVE This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes. METHODS Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis. RESULTS Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis. CONCLUSIONS The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.
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Affiliation(s)
- Jessica Rahman
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Sydney, Australia
| | - Aida Brankovic
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Brisbane, Australia
| | - Mark Tracy
- Neonatal Intensive Care Unit, Westmead, Sydney, Australia
| | - Sankalp Khanna
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Brisbane, Australia
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Villaplana-Velasco A, Pigeyre M, Engelmann J, Rawlik K, Canela-Xandri O, Tochel C, Lona-Durazo F, Mookiah MRK, Doney A, Parra EJ, Trucco E, MacGillivray T, Rannikmae K, Tenesa A, Pairo-Castineira E, Bernabeu MO. Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes. Commun Biol 2023; 6:523. [PMID: 37188768 PMCID: PMC10185685 DOI: 10.1038/s42003-023-04836-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, Df, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of Df is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of Df and analyse its relationship with CAD. We replicated 5 Df loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to Df variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between Df and CAD, and between Df and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of Df loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, Df, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that Df provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of Df, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.
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Affiliation(s)
- Ana Villaplana-Velasco
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Marie Pigeyre
- Population Health Research Institute (PHRI), Department of Medicine, Faculty of Health Sciences, McMaster University, McMaster University, Hamilton, Ontario, Canada
| | - Justin Engelmann
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Claire Tochel
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | | | | | - Alex Doney
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Esteban J Parra
- University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Tom MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Kristiina Rannikmae
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Erola Pairo-Castineira
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Miguel O Bernabeu
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK.
- The Bayes Centre, The University of Edinburgh, Edinburgh, Scotland, UK.
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Silva Rocha ED, de Morais Melo FL, de Mello MEF, Figueiroa B, Sampaio V, Endo PT. On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Med Inform Decis Mak 2022; 22:334. [PMID: 36536413 PMCID: PMC9764498 DOI: 10.1186/s12911-022-02082-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models. METHODS We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source. RESULTS From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. CONCLUSION Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.
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Affiliation(s)
- Elisson da Silva Rocha
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | - Flavio Leandro de Morais Melo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | | | - Barbara Figueiroa
- Programa Mãe Coruja Pernambucana, Secretaria de Saúde do Estado de Pernambuco, Recife, Brazil
| | | | - Patricia Takako Endo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
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Lopes MLB, Barbosa RDM, Fernandes MAC. Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095596. [PMID: 35564992 PMCID: PMC9102534 DOI: 10.3390/ijerph19095596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 11/16/2022]
Abstract
Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also be associated with socioeconomic factors. Thereby, this article seeks to use unsupervised learning techniques to stratify PTB risk in Brazil using only socioeconomic data. Through the use of datasets made publicly available by the Federal Government of Brazil, a new dataset was generated with municipality-level socioeconomic data and a PTB occurrence rate. This dataset was processed using various unsupervised learning techniques, such as k-means, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). After validation, four clusters with high levels of PTB occurrence were discovered, as well as three with low levels. The clusters with high PTB were comprised mostly of municipalities with lower levels of education, worse quality of public services-such as basic sanitation and garbage collection-and a less white population. The regional distribution of the clusters was also observed, with clusters of high PTB located mostly in the North and Northeast regions of Brazil. The results indicate a positive influence of the quality of life and the offer of public services on the reduction in PTB risk.
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Affiliation(s)
- Márcio L B Lopes
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
| | - Raquel de M Barbosa
- Department of Pharmacy and Pharmaceutical Technology, University of Granada, 18071 Granada, Spain
| | - Marcelo A C Fernandes
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
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Metabolomic Profile of Young Adults Born Preterm. Metabolites 2021; 11:metabo11100697. [PMID: 34677412 PMCID: PMC8538752 DOI: 10.3390/metabo11100697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/03/2021] [Accepted: 10/05/2021] [Indexed: 11/30/2022] Open
Abstract
Prematurity is a risk factor for the development of chronic adult diseases. Metabolomics can correlate the biochemical changes to a determined phenotype, obtaining real information about the state of health of a subject at that precise moment. Significative differences in the metabolomic profile of preterm newborns compared to those born at term have been already identified at birth. An observational case–control study was performed at the University Hospital of Siena. The aim was to evaluate and compare the metabolomic profiles of young adults born preterm to those born at term. Urinary samples were collected from 67 young adults (18–23 years old) born preterm (mean gestational age of 30 weeks, n = 49), and at term of pregnancy (mean gestational age of 38 weeks, n = 18). The urinary spectra of young adults born preterm was different from those born at term and resembled what was previously described at birth. The Random Forest algorithm gave the best classification (accuracy 82%) and indicated the following metabolites as responsible for the classification: citrate, CH2 creatinine, fumarate and hippurate. Urine spectra are promising tools for the early identification of neonates at risk of disease in adulthood and may provide insight into the pathogenesis and effects of fetal programming and infants’ outcomes.
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