Fig 1 - uploaded by Surya Prasath
Content may be subject to copyright.
An approximately 1 kg baby with a PICC. The yellow arrow points to the tip of the upper extremity PICC in an appropriate position in the SVC. The yellow boxed area represents the location where the tip position would be considered appropriate (SVC and the SVC/RA junction). PICC, peripherally inserted central catheter; RA, right atrium; SVC, superior vena cava.
Source publication
Background In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedsid...
Context in source publication
Context 1
... also eventually hope to create a reverse feed into the EHR to record the data in a structured format where it can be verified and used for clinical decision support and documentation. We have developed a web site (https://www.picclocation.com) that demonstrates our algorithm's performance in analyzing a new chunk of text (►Supplementary Figure S1, available in the online version). ...
Similar publications
Background:
In today's industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution...
Citations
... Furthermore, the intra-atrial positioning of the PICC can trigger arrhythmia, pericardial effusion, cardiac tamponade and death. (5,6) More accurate technologies to determine the correct location of the PICC tip have been used in clinical practice, such as real-time ultrasound, which, although more reliable, is not always available. For this reason, radiography is the method commonly used in NICUs. ...
... Furthermore, the intra-atrial positioning of the PICC can trigger arrhythmia, pericardial effusion, cardiac tamponade and death. (5,6) More accurate technologies to determine the correct location of the PICC tip have been used in clinical practice, such as real-time ultrasound, which, although more reliable, is not always available. For this reason, radiography is the method commonly used in NICUs. ...
Resumo Objetivo Sumarizar as evidências científicas sobre os marcos anatômicos, na radiografia, utilizados para avaliar a ponta distal do cateter central de inserção periférica em recém-nascidos. Métodos Trata-se de uma revisão integrativa da literatura realizada nas bases de dados Web of Science, National Library of Medicine (PubMed/MEDLINE) e Scopus, entre os anos de 2017 e 2021. Resultados Dos 263 documentos encontrados nas bases, 13 preencheram os critérios de elegibilidade e foram selecionados para análise. Destes, 12 eram estudos observacionais e um experimental. Quanto ao país de origem, apenas um estudo foi publicado no Brasil, cinco na China, dois nos Estados Unidos da América e um estudo na Itália, Alemanha, Canadá, Irã e Índia. As evidências encontradas demonstraram a existência de sete marcos anatômicos utilizados na radiografia para visualização da ponta do cateter percutâneo em neonatos, sendo a unidade vertebral o referencial norteador predominante. Conclusão Para cateteres inseridos em membros superiores, os marcos anatômicos que mais se aproximam da junção cavo-atrial são a quinta e sétima vértebra torácica, ou duas unidades vertebrais abaixo da carina. Para os membros inferiores, o posicionamento da ponta do cateter deve estar entre a nona e décima vértebra torácica ou acima da quarta vértebra lombar.
Background:
Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.
Objective:
This study describes the evaluation of a machine learning (ML)-based decision support tool (DST) to assist injury surveillance departments in the validation, coding, and use of their data, comparing outcomes in coding time, and accuracy pre- and postimplementations.
Methods:
Manually coded injury surveillance data have been used to develop, train, and iteratively refine a ML-based classifier to enable semiautomated coding of injury narrative data. This paper describes a trial implementation of the ML-based DST in the Queensland Injury Surveillance Unit (QISU) workflow using a major pediatric hospital's ED data comparing outcomes in coding time and pre- and postimplementation accuracies.
Results:
The study found a 10% reduction in manual coding time after the DST was introduced. The Kappa statistics analysis in both DST-assisted and -unassisted data shows increase in accuracy across three data fields, that is, injury intent (85.4% unassisted vs. 94.5% assisted), external cause (88.8% unassisted vs. 91.8% assisted), and injury factor (89.3% unassisted vs. 92.9% assisted). The classifier was also used to produce a timely report monitoring injury patterns during the novel coronavirus disease 2019 (COVID-19) pandemic. Hence, it has the potential for near real-time surveillance of emerging hazards to inform public health responses.
Conclusion:
The integration of the DST into the injury surveillance workflow shows benefits as it facilitates timely reporting and acts as a DST in the manual coding process.
Background:
Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions.
Objective:
The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome.
Methods:
In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence.
Results:
According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice.
Conclusion:
Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.
Objective As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations.
Methods We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded.
Results From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population.
Discussion With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training.
Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.
