Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Xiang, Yang; Ji, Hangyu; Zhou, Yujia; Du, Jingcheng; Rasmy, Laila; Wu, Stephen; Zheng, W. Jim; Xu, Hua; Zhi, Degui; Zhang, Yaoyun; Tao, Cui

doi:10.2196/16981

Cited by 42 publications

(41 citation statements)

References 52 publications

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“…For example, an artificial neural network was used to analyze clinical data and create an automated pediatric asthma severity score, which showed better performance than the pediatric asthma score and could therefore help manage pediatric asthma exacerbation in the pediatric intensive care unit 54 . Similarly, a retrospective cohort study of 31,433 adult asthma patients reported a time-sensitive predictive model based on an artificial neural network, which integrated clinical variables in the observed time window to predict asthma exacerbation 55 . In addition, a modified artificial neural network was applied to predict emergency department visits of asthma and COPD patients due to exacerbation.…”

Section: Ai/ml and Asthmamentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

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Section: Ai/ml and Asthmamentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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“…The data modality of structured EHR We define structured EHR data of each patient as a sequence of visits, each as a list of codes. This is a classic formulation commonly used in the literature 12,[49][50][51] . The codes within a visit can be either ordered or unordered.…”

Section: Introductionmentioning

confidence: 99%

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

et al. 2021

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Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed for the text domain to the structured EHR domain. Med-BERT is a contextualized embedding model pretrained on a structured EHR dataset of 28,490,650 patients. Fine-tuning experiments showed that Med-BERT substantially improves the prediction accuracy, boosting the area under the receiver operating characteristics curve (AUC) by 1.21–6.14% in two disease prediction tasks from two clinical databases. In particular, pretrained Med-BERT obtains promising performances on tasks with small fine-tuning training sets and can boost the AUC by more than 20% or obtain an AUC as high as a model trained on a training set ten times larger, compared with deep learning models without Med-BERT. We believe that Med-BERT will benefit disease prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.

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“…IH, UWM, KPSC, and many other health care systems use IDM and use inaccurate predictive models with AUC<0.8 and sensitivity ≤49% for preventive care via care management [22,[24][25][26][46][47][48][49][50][51][52][53][54][55][56][57]. Similar to our recent work of using IH, UWM, and KPSC data to greatly increase prediction accuracy for hospital use for asthma [43][44][45] related to exacerbation proneness, we expect our models predicting exacerbation proneness to be more accurate than those inaccurate models, benefit many patients, and have practical value.…”

Section: Using Our Results In Clinical Practicementioning

confidence: 99%

“…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: 99%

“…This matches recent progress in areas such as video classification, speech recognition, and natural language processing, where temporal features LSTM automatically discovered from data beat those that experts provided or other temporal and sequential pattern mining methods [101][102][103][104] mined from data. The LSTM model of Xiang et al for predicting asthma outcome [57] had a low AUC of 0.7 because it used only 3 types of attributes and mostly inpatient data without much outpatient data, not because LSTM is ineffective. 2. Although >100 potential risk factors for poor outcomes in asthma and COPD are known [50][51][52][105][106][107][108][109][110][111][112], a typical previous model uses only a few (eg, ≤17) [46][47][48][49][50][51][52][53][54][55][56][57].…”

Section: Gap 1: Low Prediction Accuracymentioning

confidence: 99%

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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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Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Cited by 42 publications

References 52 publications

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

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

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