COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients

Alle, Shanmukh; Siddiqui, Samreen; Kanakan, Akshay; Garg, Akshit; Karthikeyan, Akshaya; Mishra, Neha; Waghdhare, Swati; Tyagi, Akansha; Tarai, Bansidhar; Hazarika, Pranjal Pratim; Das, Poonam; Budhiraja, Sandeep; Nangia, Vivek; Dewan, Arun; Sethuraman, Ramanathan; Subramanian, Chitra; Srivastava, Mashrin; Chakravarthi, Avinash; Jacob, Johnny; Namagiri, Madhuri; Konala, Varma; Dash, Debasish; Jha, Sujeet; Pandey, Rajesh; Agrawal, Anurag; Vinod, P. K.

doi:10.1101/2020.12.19.20248524

Cited by 3 publications

(1 citation statement)

References 42 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset unfortunately did not include information on comorbidities. Owing to its importance as a determinant of COVID-19 outcome, we have collected clinical data on Indian COVID19 patients including the details of comorbidities and have developed ML models for both risk stratification and mortality (66)(67)(68)(69). After experimenting with various algorithms, we observed that there is a trade-off between accuracy and interpretability.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Karthikeyan

Garg

Vinod

et al. 2021

Front. Public Health

Self Cite

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Karthikeyan

Garg

Vinod

et al. 2021

Front. Public Health

Self Cite

View full text Add to dashboard Cite

show abstract

Modern AI/ML Methods for Healthcare: Opportunities and Challenges

Garg

Venkataramani

Karthikeyan

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Artificial intelligence: machine learning for chemical sciences

Karthikeyan

Priyakumar

2021

J Chem Sci

Self Cite

View full text Add to dashboard Cite

Graphical abstract Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high–level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences. Synopsis Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years.

show abstract

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients

Cited by 3 publications

References 42 publications

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Modern AI/ML Methods for Healthcare: Opportunities and Challenges

Artificial intelligence: machine learning for chemical sciences

Contact Info

Product

Resources

About