Novel Machine Learning Can Predict Acute Asthma Exacerbation

Zein, Joe; Wu, Chuanshen; Attaway, Amy; Zhang, Peng; Nazha, Aziz

doi:10.1016/j.chest.2020.12.051

Cited by 44 publications

(57 citation statements)

References 24 publications

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“…Existing models for predicting an individual asthma or COPD patient's health outcomes typically have low accuracy . The systematic review by Loymans et al [52] and our review [43] showed that for forecasting hospital use (emergency department visits and inpatient stays) for asthma in patients with asthma, each previous model, excluding the models of Zein et al [58], has an area under the receiver operating characteristic curve (AUC) within 0.61-0.81, a sensitivity within 25%-49%, and a positive predictive value within 4%-22% [46][47][48][49][50][51][52][53][54][55][56][57]. The models of Zein et al [58] and our recent new models [43][44][45] have similarly higher accuracy but are still not good enough for aligning preventive care with the patients needing it the most.…”

Section: Gap 1: Low Prediction Accuracymentioning

confidence: 86%

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

et al. 2021

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Background Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. Objective To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. Methods We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation. Results We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. Conclusions Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. International Registered Report Identifier (IRRID) PRR1-10.2196/27065

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Section: Gap 1: Low Prediction Accuracymentioning

confidence: 86%

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

et al. 2021

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“…In addition, it also allows missing values for prediction, which is more advantageous than the conventional logistic regression model. For example, the previous study demonstrated the usefulness of light GBM to build an accurate risk prediction for asthma exacerbation with possessing the advantage of identifying missing values as a unique entity 22 . Importantly, our study is unique in that we created the calculator on the website to assist interventional cardiologists in identifying high-risk patients for AKI after PCI, highlighting the importance of implementing the risk model in physicians.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, we constructed the SHAP approach to select the important variables with the light GBM using the 12 NCDR variables. This approach explains the models at the level of individual patients based on the sum of the numeric computed credit (SHAP) values of each feature 22 , 23 . Categorical variables (age, eGFR, and preprocedural hemoglobin) were entered as continuous variables in the Lasso and SHAP models.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, a light GBM model using a stratified K-fold cross-validation method was applied to the training dataset (K = 5) 24 . The light GBM was designed to be accurate, efficient, and fast, which are advantages in handling large-scale data 22 . In comparison to the logistic regression model, the light GBM used “NaN” to represent missing values and were dealt with separately from zero, as missing values were interpreted as containing information 22 .…”

Section: Methodsmentioning

confidence: 99%

“…The light GBM was designed to be accurate, efficient, and fast, which are advantages in handling large-scale data 22 . In comparison to the logistic regression model, the light GBM used “NaN” to represent missing values and were dealt with separately from zero, as missing values were interpreted as containing information 22 . Hyperparameter optimization was performed using an implementation called “Optuna” 25 .…”

Section: Methodsmentioning

confidence: 99%

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Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

Kuno

Mikami

Sahashi

et al. 2022

Sci Rep

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Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predicting the preprocedural risk of AKI, although the scoring system requires a number of clinical contents. We sought to examine whether machine learning (ML) techniques could predict AKI with fewer NCDR-AKI risk model variables within a comparable PCI database in Japan. We evaluated 19,222 consecutive patients undergoing PCI between 2008 and 2019 in a Japanese multicenter registry. AKI was defined as an absolute or a relative increase in serum creatinine of 0.3 mg/dL or 50%. The data were split into training (N = 16,644; 2008–2017) and testing datasets (N = 2578; 2017–2019). The area under the curve (AUC) was calculated using the light gradient boosting model (GBM) with selected variables by Lasso and SHapley Additive exPlanations (SHAP) methods among 12 traditional variables, excluding the use of an intra-aortic balloon pump, since its use was considered operator-dependent. The incidence of AKI was 9.4% in the cohort. Lasso and SHAP methods demonstrated that seven variables (age, eGFR, preprocedural hemoglobin, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction/unstable angina, heart failure symptoms, and cardiogenic shock) were pertinent. AUC calculated by the light GBM with seven variables had a performance similar to that of the conventional logistic regression prediction model that included 12 variables (light GBM, AUC [training/testing datasets]: 0.779/0.772; logistic regression, AUC [training/testing datasets]: 0.797/0.755). The AKI risk model after PCI using ML enabled adequate risk quantification with fewer variables. ML techniques may aid in enhancing the international use of validated risk models.

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3D mask based lung function monitoring system using machine learning for early identification of lung disorders

Bhansali,

Rekha,

Shahapure

et al. 2024

Int J Imaging Syst Tech

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Early identification of illness can aid in lowering the death rate related to lung illnesses. Asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis are all chronic respiratory illnesses that cause irritation and oedema of the airway due to increased mucus discharge. Monitoring the asthmatic patient's physiological state is vital to avoiding dangerous circumstances. This study offers a regular lung function monitoring system that employs Machine Learning (ML) approach to aids in the prompt detection of symptoms of illness and the prevention of significant epidemics of the lung condition. A collection of sensors are coupled to the microcontroller in a 3D mask created using 3D printing technology. When a person wearing a face mask breathes in and out, the sensor values are instantly retrieved. The sensor data is sent to the cloud via a Wi‐Fi module for additional evaluation, and categorisation is performed using genetic algorithms, Support Vector Machine (SVM), and Principal Component Analysis (PCA). The GA, SWM, and PCA algorithms identify lung sickness using data from sensors obtained from the 3D masks through the web interface. There were 250 participants in total, comprising persons from all ages, smoker and those who do not smoke as well as asthmatics. The classifiers are trained utilising a set of pretrained values obtained from freely accessible datasets. Furthermore, patients are alerted when physiological indicators deviate from normal and when favourable atmospheric circumstances change.

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Novel Machine Learning Can Predict Acute Asthma Exacerbation

Cited by 44 publications

References 24 publications

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

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