Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study

• URL: http://arxiv.org/abs/2109.02439v1
• Date: Wed, 1 Sep 2021 04:46:27 GMT
• Title: Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study
• Authors: Joy Tzung-yu Wu, Miguel \'Angel Armengol de la Hoz, Po-Chih Kuo, Joseph Alexander Paguio, Jasper Seth Yao, Edward Christopher Dee, Wesley Yeung, Jerry Jurado, Achintya Moulick, Carmelo Milazzo, Paloma Peinado, Paula Villares, Antonio Cubillo, Jos\'e Felipe Varona, Hyung-Chul Lee, Alberto Estirado, Jos\'e Maria Castellano, Leo Anthony Celi
• Abstract summary: We develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.
• Score: 1.5308395762165423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N=2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N=242) and an academic center in Seoul, Republic of Korea (N=336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day-mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.

Related papers

• Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning [7.750772093056729]
  coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. Patients have to revisit the emergency room (ER) within a short time after discharge. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases.
  arXiv  Detail & Related papers  (2023-06-29T18:51:42Z)
• A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care [15.64030213048907]
  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.
  arXiv  Detail & Related papers  (2022-09-16T09:09:15Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort [0.0]
  Yan et al. 1 have published a research that uses Machine learning (ML) methods to predict the outcome of COVID-19 patients. Here, we show the limitations of this model by deploying it on one of the largest datasets of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-01-15T07:06:49Z)
• Classification supporting COVID-19 diagnostics based on patient survey data [82.41449972618423]
  logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19 were generated. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. This data set consists of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
  arXiv  Detail & Related papers  (2020-11-24T17:44:01Z)
• Predicting special care during the COVID-19 pandemic: A machine learning approach [0.0]
  We propose an analytical approach based on statistics and machine learning to predict whether patients are going to require special care. We also predict the number of days the patients will stay under such care. The analytical approach can be used in other diseases and can help the planning hospital capacity.
  arXiv  Detail & Related papers  (2020-11-06T00:18:27Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Individualized Prediction of COVID-19 Adverse outcomes with MLHO [9.197411456718708]
  We developed an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health outcomes. We modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. Our results demonstrated that while demographic variables are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model.
  arXiv  Detail & Related papers  (2020-08-10T02:44:52Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.