Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

Lindén, Thomas; Hanses, Frank; Domingo‐Fernándéz, Daniel; DeLong, Lauren Nicole; Kodamullil, Alpha Tom; Schneider, J; Vehreschild, Maria J.G.T.; Lanznaster, Julia; Ruethrich, Maria Madeleine; Borgmann, Stefan; Hower, Martin; Wille, Kai; Feldt, Torsten; Rieg, Siegbert; Hertenstein, Bernd; Wyen, Christoph; Roemmele, Christoph; Vehreschild, Jörg Janne; Jakob, Carolin E. M.; Stecher, Melanie; Kuzikov, Maria; Zaliani, Andrea; Fröhlich, Holger

doi:10.1016/j.ailsci.2021.100020

Cited by 7 publications

(4 citation statements)

References 66 publications

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“…Using a combined strategy consisting of feature importance analysis, BN structure learning and statistical hypothesis testing, we were able to identify diagnoses and prescriptions that have a significant impact on model prediction and may causally influence the endpoint. Our analysis supports that socioeconomic and psycho-social health risks play an important role in addition to well-known risk factors such as obesity, diabetes, cardiovascular diseases, and dementia, which have already been reported as known risk factors for severe COVID-19 disease progression in several studies [36,39,[47][48][49]. This confirms the validity of our approach, which can be applied to other datasets as well.…”

Section: Discussionsupporting

confidence: 86%

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

Lentzen

Lindén

Veeranki

et al. 2023

IEEE J. Biomed. Health Inform.

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In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to Random Forests, XGBoost, and RETAIN, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.

show abstract

Section: Discussionsupporting

confidence: 86%

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

Lentzen

Lindén

Veeranki

et al. 2023

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using a combined strategy consisting of feature importance analysis, BN structure learning and statistical hypothesis testing, we were able to identify diagnoses and prescriptions that have a significant impact on model prediction and may causally influence the endpoint. Our analysis supports that socioeconomic and psycho-social health risks play an important role in addition to well-known risk factors such as obesity, diabetes, cardiovascular diseases, and dementia, which have already been reported as known risk factors for severe COVID-19 disease progression in several studies [33,36,[44][45][46]. This confirms the validity of our approach, which can be applied to other datasets as well.…”

Section: Discussionsupporting

confidence: 86%

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

Lentzen

Lindén

Veeranki

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to XGBoost and Random Forests, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.

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“…The applicability of MC-19 with approximately 50% missing values specifically for variables with follow-up measurements highly depends on the type of the AI algorithm. In contrast, one recent study [25] aimed at predicting COVID-19 mortality using the LEOSS dataset-a dataset containing relatively fewer missing values (less than 50%) and a broad range of modalities (i.e., demographics, clinical, drugs, laboratory, imaging, comorbidities)-resulted in an AUC of approximately 80%. Taken together, for a dataset to be "AI-ready", which might need a lot of preprocessing work, is highly subjective, and different factors should be taken into consideration.…”

Section: Discussionmentioning

confidence: 99%

Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk

Khatami,

Sargsyan,

Russo

et al. 2024

Data

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Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.

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Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

Cited by 7 publications

References 66 publications

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk

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