Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Abstract

Background Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions. Methods The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling. Results From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68). Conclusion Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data

“…Many studies using machine learning to investigate multimorbidity patterns focus on handling sparse data sets by either removing sparsity-generating features or merging feature categories to reduce sparsity. However, these methods often result in information loss and less precise interpretation of multimorbidity features [ 19 ]. Instead of relying solely on a sequential deep learning model, we aggregated all evolved bins to create a new data set.…”

“…Data for the multimorbidity analyses were collected from the Piedmont Longitudinal Study (PLS), a health administrative cohort comprising anonymized records linked at the individual level from various social, health, and administrative databases [ 5 , 19 ]. Since February 2020, the PLS has been augmented by the regional COVID-19 platform, which collects data on COVID-19 infections.…”

“…Some studies using machine learning to investigate multimorbidity patterns tackled data set sparsity by strategies such as removing sparsity-inducing features [ 16 ], consolidating feature categories after one-hot encoding [ 17 ], or clustering rare features [ 18 ]. However, while these methods can alleviate sparsity, they may also result in the loss of important information and impede the meaningful interpretation of multimorbidity features [ 19 ].…”