Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence

Hilton, Cameron Beau; Milinovich, Alex; Felix, Christina; Vakharia, Nirav; Crone, T. J.; Donovan, Chris; Proctor, Andrew; Nazha, Aziz

doi:10.1038/s41746-020-0249-z

Cited by 55 publications

(39 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, it is imperative to diagnose these patients early. Hence these machine learning-based prediction models may also provide to reducing the time of triage and financial burdens at the admission level over the clinical characteristics [ 1 , 32 , 33 ]. In addition to helping decision of hospitalization status, neurological disorders caused by arbovirus infections may be enlightened, especially by using deep learning and transfer learning models for hospital scene and clinician [ 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Özer

Çetin

Gorur

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

The prediction of hospital patients and outpatients with suspected arboviral infection individuals in research-limited settings of the urban areas is defined as a challenging process for clinicians. Dengue, Chikungunya, and Zika arboviruses have gained attention in recent years because of the high prevalence in the society and financial burden of major global health systems. In this study, we proposed a machine learning algorithm based prediction model over retrospective medical records, which are named as SISA (the Severity Index for Suspected Arbovirus) and SISAL (the Severity Index for Suspected Arbovirus with Laboratory) datasets. Therefore, we aim to inform the clinicians about the use of machine learning with transfer learning success for diagnosis and comprehensive comparison of the classification performances over the SISA/SISAL datasets in the resource-limited settings that may cause to the small datasets of arboviral infection. In this study, Convolutional Neural Network and Long Short-Term Memory have achieved 100% accuracy and 1 of area under the curve (AUC) score, Fully Connected Deep Network has provided 92.86% accuracy and 0.969 AUC score in the SISAL dataset with transfer learning. Moreover, 98.73% accuracy and 0.988 AUC score were obtained by Convolutional Neural Network and Long Short-Term Memory for the SISA dataset. Furthermore, Linear Discriminant Analysis (shallow algorithm) has provided reaching up to 96.43% accuracy. Notably, deep learning based models have achieved improved performances compared to the previously reported study.

show abstract

Section: Discussionmentioning

confidence: 99%

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Özer

Çetin

Gorur

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…25 Feature engineering algorithms such as SHAP and Boruta algorithm are being increasingly utilized in various fields of healthcare to ascertain feature importance and deliver personalized prediction models. 5…”

Section: Discussionmentioning

confidence: 99%

“…4 Modern machine learning techniques can identify useful patterns from diverse data sources in real-time and help understand the difference in disease spread. 5 The features or determinants affecting COVID-19 mortality should be easily explainable to trust the predictive algorithms output and to build confidence in decision making.…”

Section: Introductionmentioning

confidence: 99%

“…15 Beyond prediction of disease related outcomes, machine learning models are being increasingly utilized with explainable and interpretable results in healthcare. 5,21,22 Use of multiple models and comparison of results has become more common in healthcare as investigators seek to overcome concerns around generalizability and accuracy of the results. 21,23 Although random forest has been shown to handle even imbalanced data, newer machine learning models such as CatBoost in addition have also demonstrated capability of handling different types of variables while maintaining high level of accuracy and efficiency.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

Mathur

Sethi

Mathur³

et al. 2020

Preprint

View full text Add to dashboard Cite

COVID-19 is now one of the leading causes of mortality amongst adults in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations. The goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel, and health care characteristics of different states across the United States and COVID-19 mortality. Using multiple public data sets, 24 variables linked to COVID-19 disease were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States. Feature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age,median income, climate and disease mitigation measures on COVID-19 related mortality remained unclear. COVID-19 policy making will need to take population density, pre-existing medical care and state travel policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.

show abstract

“…It can identify important variables, explain model decisions and reveal non-linear relationships and feature interactions. Several medical studies have used TreeExplainer to extract valuable discoveries from “black box”, tree-based models [13, 14] to generate new hypotheses. Importantly, in addition to providing accurate prediction scores, interpretable tree-based models can support clinical decision making by revealing individualized risk factors of mortality.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning prediction of all-cause mortality

Chen

et al. 2021

Preprint

View full text Add to dashboard Cite

Explainable artificial intelligence provides an opportunity to improve prediction accuracy over standard linear models using “black box” machine learning (ML) models while still revealing insights into a complex outcome such as all-cause mortality. We propose the IMPACT (Interpretable Machine learning Prediction of All-Cause morTality) framework that implements and explains complex, non-linear ML models in epidemiological research, by combining a tree ensemble mortality prediction model and an explainability method. We use 133 variables from NHANES 1999–2014 datasets (number of samples: n = 47, 261) to predict all-cause mortality. To explain our model, we extract local (i.e., per-sample) explanations to verify well-studied mortality risk factors, and make new discoveries. We present major factors for predicting x-year mortality (x = 1, 3, 5) across different age groups and their individualized impact on mortality prediction. Moreover, we highlight interactions between risk factors associated with mortality prediction, which leads to findings that linear models do not reveal. We demonstrate that compared with traditional linear models, tree-based models have unique strengths such as: (1) improving prediction power, (2) making no distribution assumptions, (3) capturing non-linear relationships and important thresholds, (4) identifying feature interactions, and (5) detecting different non-linear relationships between models. Given the popularity of complex ML models in prognostic research, combining these models with explainability methods has implications for further applications of ML in medical fields. To our knowledge, this is the first study that combines complex ML models and state-of-the-art feature attributions to explain mortality prediction, which enables us to achieve higher prediction accuracy and gain new insights into the effect of risk factors on mortality.

show abstract

Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence

Cited by 55 publications

References 25 publications

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

Interpretable machine learning prediction of all-cause mortality

Contact Info

Product

Resources

About