Estimating concentrations for particle and gases in a mechanically ventilated building in Hong Kong: multivariate method and machine learning

2021 ◽  
Author(s):  
Wenwei Che ◽  
Alison T. Y. Li ◽  
Alexis K. H. Lau
Keyword(s):  
Machine Learning ◽  
Hong Kong ◽  
Mechanically Ventilated ◽  
Multivariate Method

scholarly journals Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043487
Author(s):  
Hao Luo ◽  
Kui Kai Lau ◽  
Gloria H Y Wong ◽  
Wai-Chi Chan ◽  
Henry K F Mak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Electronic Health Records ◽  
Case Control Study ◽  
Case Control ◽  
Dementia Diagnosis ◽  
Health Records ◽  
Electronic Health ◽  
Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

Airborne Bacteria in Outdoor Air and Air of Mechanically Ventilated Buildings at City Scale in Hong Kong across Seasons

2020 ◽  
Vol 54 (19) ◽  
pp. 11732-11743
Author(s):  
You Zhou ◽  
Yonghang Lai ◽  
Xinzhao Tong ◽  
Marcus H. Y. Leung ◽  
Jimmy C. K. Tong ◽  
...  
Keyword(s):  
Hong Kong ◽  
Airborne Bacteria ◽  
Outdoor Air ◽  
Mechanically Ventilated

scholarly journals Imputation of PaO2 from SpO2 values from the MIMIC-III Critical Care Database Using Machine-Learning Based Algorithms

2021 ◽  
Author(s):  
Shuangxia Ren ◽  
Jill Zupetic ◽  
Mehdi Nouraie ◽  
Xinghua Lu ◽  
Richard D. Boyce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Learning Models ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Mimic Iii ◽  
Ventilated Patients ◽  
Machine Learning Models

AbstractBackgroundThe partial pressure of oxygen (PaO2)/fraction of oxygen delivered (FIO2) ratio is the reference standard for assessment of hypoxemia in mechanically ventilated patients. Non-invasive monitoring with the peripheral saturation of oxygen (SpO2) is increasingly utilized to estimate PaO2 because it does not require invasive sampling. Several equations have been reported to impute PaO2/FIO2 from SpO2 /FIO2. However, machine-learning algorithms to impute the PaO2 from the SpO2 has not been compared to published equations.Research QuestionHow do machine learning algorithms perform at predicting the PaO2 from SpO2 compared to previously published equations?MethodsThree machine learning algorithms (neural network, regression, and kernel-based methods) were developed using 7 clinical variable features (n=9,900 ICU events) and subsequently 3 features (n=20,198 ICU events) as input into the models from data available in mechanically ventilated patients from the Medical Information Mart for Intensive Care (MIMIC) III database. As a regression task, the machine learning models were used to impute PaO2 values. As a classification task, the models were used to predict patients with moderate-to-severe hypoxemic respiratory failure based on a clinically relevant cut-off of PaO2/FIO2 ≤ 150. The accuracy of the machine learning models was compared to published log-linear and non-linear equations. An online imputation calculator was created.ResultsCompared to seven features, three features (SpO2, FiO2 and PEEP) were sufficient to impute PaO2/FIO2 ratio using a large dataset. Any of the tested machine learning models enabled imputation of PaO2/FIO2 from the SpO2/FIO2 with lower error and had greater accuracy in predicting PaO2/FIO2 ≤ 150 compared to published equations. Using three features, the machine learning models showed superior performance in imputing PaO2 across the entire span of SpO2 values, including those ≥ 97%.InterpretationThe improved performance shown for the machine learning algorithms suggests a promising framework for future use in large datasets.

scholarly journals MACHINE LEARNING CLUSTERING FOR BLOOD PRESSURE VARIABILITY: VALIDATION FROM THE SPRINT TO THE HONG KONG COMMUNITY COHORT

2021 ◽  
Vol 39 (Supplement 1) ◽  
pp. e69
Author(s):  
Kelvin Tsoi ◽  
Nicholas Chan ◽  
Karen Yiu ◽  
Simon Poon ◽  
Kendall Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Hong Kong ◽  
Blood Pressure Variability

Profiling Airborne Microbiota in Mechanically Ventilated Buildings Across Seasons in Hong Kong Reveals Higher Metabolic Activity in Low-Abundance Bacteria

2020 ◽  
Vol 55 (1) ◽  
pp. 249-259
Author(s):  
You Zhou ◽  
Marcus H. Y. Leung ◽  
Xinzhao Tong ◽  
Yonghang Lai ◽  
Jimmy C. K. Tong ◽  
...  
Keyword(s):  
Hong Kong ◽  
Metabolic Activity ◽  
Mechanically Ventilated

scholarly journals Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richard Du ◽  
Efstratios D. Tsougenis ◽  
Joshua W. K. Ho ◽  
Joyce K. Y. Chan ◽  
Keith W. H. Chiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hong Kong ◽  
High Accuracy ◽  
Rt Pcr ◽  
Blood Tests ◽  
Applied Machine Learning ◽  
Laboratory Markers ◽  
Machine Learning Model ◽  
Temporal Validation ◽  
Low Sensitivity

AbstractTriaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9–95.8%; Sensitivity: 55.5–77.8%; Specificity: 91.5–98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

scholarly journals Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

2021 ◽  
Vol 10 (10) ◽  
pp. 2172
Author(s):  
Jong Ho Kim ◽  
Young Suk Kwon ◽  
Moon Seong Baek
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Learning Models ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Ventilated Patients ◽  
Machine Learning Models

Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.

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