MedML: Fusing medical knowledge and machine learning models for early pediatric COVID-19 hospitalization and severity prediction

Gao, Jianliang; Yang, Chaoqi; Heintz, Joerg; Barrows, Scott; Elise, Albers,; Mary, Stapel,; Warfield, Sara; Cross, Adam T.; Sun, Jimeng

doi:10.1016/j.isci.2022.104970

Cited by 18 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the claims data do not show the primary cause for hospitalizations, we define a hospitalization case as a patient hospitalized within 35 days after the COVID-19 diagnosis in the claims dataset, and we calculate the total hospitalization cases for each location to get . This setting is inspired by previous COVID-19 hospitalization prediction works 18 , 19 .…”

Section: Resultsmentioning

confidence: 99%

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics

et al. 2023

Self Cite

View full text Add to dashboard Cite

In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6 $${R}^{2}$$ R 2 and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

show abstract

Section: Resultsmentioning

confidence: 99%

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the past two years, many machine learning and deep learning models have been proposed to conduct COVID-19 clinical prediction tasks, including diagnosis prediction 13,14 , length-of-stay prediction 15,16 , severity and mortality prediction [3][4][5][6][7][8][9][10][11][12] , etc. Yan et al 3 conducted mortality prediction for COVID-19 patients from the Tongji Hospital in China.…”

Section: Covid-19 Predictive Modeling Using Ehr Datamentioning

confidence: 99%

“…Bennett et al 10 compared multiple machine learning models including gradient boosted tree and linear regression to predict hospitalization severity for COVID-19 patients in the US. Gao et al 12 used deep learning and tree-based models to predict COVID-19 severity and hospitalization risks. Ma et al 15 conducted length-of-stay prediction for hospitalized COVID-19 patients from the HM Hospitals in Spain.…”

Section: Covid-19 Predictive Modeling Using Ehr Datamentioning

confidence: 99%

“…and LOS prediction works simply follow the same task settings in previous EHR data-mining works. For risk or mortality prediction models, they only output the predicted risk score at the last timestep of the available EHR sequences 3,12 . However, for high-risk COVID-19 patients (especially the ICU patients), patients' status at the last timestep is already highly dangerous and it may be too late to conduct life-saving treatments even if the model gives the alarm.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care: Choosing the Best Model for COVID-19 Prognosis

Gao¹,

Zhu²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Objective:The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data. Despite their initial success in certain clinical applications, there is currently a lack of benchmarking results to achieve a fair comparison so that we can select the optimal model for clinical use. Furthermore, there is a discrepancy between the formulation of traditional prediction tasks and real-world clinical practice in intensive care. Methods: To fill these gaps, we propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units. The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients. We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks, including 5 machine learning models, 6 basic deep learning models and 6 deep learning predictive models specifically designed for EHR data. Results: We provide benchmarking results using data from two real-world COVID-19 EHR datasets. Both datasets are publicly available without needing any inquiry and one dataset can be accessed on request. We provide fair, reproducible benchmarking results for two tasks. Conclusions:We deploy all experiment results and models on an online platform. We also allow clinicians and researchers to upload their data to the platform and get quick prediction results using our trained models. We hope our efforts can further facilitate deep learning and machine learning research for COVID-19 predictive modeling. Software Repository: https://github.com/yhzhu99/covid-ehr-benchmarks IntroductionThe COVID-19 pandemic needs no introduction. As of May 2022, the virus has caused over 500 million infected cases and over 6 million deaths 1 . Though research shows that new variants of COVID-19 are less deadly, they are more spreadable and cause the number of cases still surging globally 2 . Under current circumstances, achieving early risk prediction and estimating the disease progression especially for COVID-19 patients in intensive care units have been an important topic to allocate limited medical resources and relieve the burdens of our healthcare system.Electronic health record (EHR) data and intelligent models have been viable solutions to solve this challenge. Many machine learning and deep learning models have been proposed to utilize COVID-19 patients' EHR data to conduct clinical prediction tasks including severity 3-12 , diagnosis 13, 14 , length-of-stay (LOS) 15, 16 , etc. There are more previous general EHR predictive models, which can also be applied to COVID-19 prediction tasks. These works achieve better prediction performances compared with ...

show abstract

“…High-accuracy models are shown in the literature, but papers focus on obtaining high-accuracy results but do not consider possible biases present in the datasets used. As a result, some previous articles have studied the bias in datasets related to chest X-ray images in COVID-19 11 – 13 .…”

Section: Introductionmentioning

confidence: 99%

Biases associated with database structure for COVID-19 detection in X-ray images

Arias-Garzón

Tabares-Soto

Bernal-Salcedo

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

show abstract

MedML: Fusing medical knowledge and machine learning models for early pediatric COVID-19 hospitalization and severity prediction

Cited by 18 publications

References 23 publications

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics

Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics

A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care: Choosing the Best Model for COVID-19 Prognosis

Biases associated with database structure for COVID-19 detection in X-ray images

Contact Info

Product

Resources

About