A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization

Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning

Background

Attrition is a challenge in parameter estimation in both longitudinal and multi-stage cross-sectional studies. Here, we examine utility of machine learning to predict attrition and identify associated factors in a two-stage population-based epilepsy prevalence study in Nairobi.

Methods

All individuals in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) (Korogocho and Viwandani) were screened for epilepsy in two stages. Attrition was defined as probable epilepsy cases identified at stage-I but who did not attend stage-II (neurologist assessment). Categorical variables were one-hot encoded, class imbalance was addressed using synthetic minority over-sampling technique (SMOTE) and numeric variables were scaled and centered. The dataset was split into training and testing sets (7:3 ratio), and seven machine learning models, including the ensemble Super Learner, were trained. Hyperparameters were tuned using 10-fold cross-validation, and model performance evaluated using metrics like Area under the curve (AUC), accuracy, Brier score and F1 score over 500 bootstrap samples of the test data.

Results

Random forest (AUC = 0.98, accuracy = 0.95, Brier score = 0.06, and F1 = 0.94), extreme gradient boost (XGB) (AUC = 0.96, accuracy = 0.91, Brier score = 0.08, F1 = 0.90) and support vector machine (SVM) (AUC = 0.93, accuracy = 0.93, Brier score = 0.07, F1 = 0.92) were the best performing models (base learners). Ensemble Super Learner had similarly high performance. Important predictors of attrition included proximity to industrial areas, male gender, employment, education, smaller households, and a history of complex partial seizures.

Conclusion

These findings can aid researchers plan targeted mobilization for scheduled clinical appointments to improve follow-up rates. These findings will inform development of a web-based algorithm to predict attrition risk and aid in targeted follow-up efforts in similar studies.

Acute-on-chronic liver failure in patients with severe acute respiratory syndrome coronavirus 2 infection

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a significant impact on the lives of millions of people, especially those with other concomitant diseases, such as chronic liver diseases. To date, seven coronaviruses have been identified to infect humans. The main site of pathological action of these viruses is lung tissue. However, a substantial number of studies have proven that SARS-CoV-2 shows affinity towards several organs, including the gastrointestinal tract and the liver. The current state of evidence points to several proposed mechanisms of liver injury in patients with COVID-19 and their combination. Liver impairment is considered to be the result of the direct effect of the virus on the hepatic tissue cells, a systemic reaction consisting of inflammation, hypoxia and cytokine storm, drug-induced liver injury, with the possible contribution of a perturbed gut-liver axis. Reactivation of chronic hepatic disease could be another factor for liver impairment in patients with SARS-CoV-2 infection. Acute-on-chronic liver failure (ACLF) is a relatively new syndrome that occurs in 10%–30% of all hospitalized patients with chronic liver disease. It is crucial to recognize high-risk patients due to the increased morbidity and mortality in these cases. Several published studies have reported virus infection as a trigger factor for ACLF. However, to date, there are few relevant studies describing the presence of ACLF in patients with acute SARS-CoV-2 infection. In this minireview we summarize the current state of knowledge regarding the relation between ACLF and acute SARS-CoV-2 infection.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were "gastrointestinal" (title) together with "random forest" or "explainable artificial intelligence" (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70-0.98 for accuracy, 0.04-0.25 for sensitivity, and 0.54-0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.

Gastrointestinal sequalae months after severe acute respiratory syndrome corona virus 2 infection: a prospective, observational study

INTRODUCTION

Post-coronavirus disease (post-COVID) symptoms arise mostly from impaired function of respiratory tract although in many patients, the dysfunction of gastrointestinal tract and liver among other organ systems may persist.

METHODS

Primary data collection was based on a short gastrointestinal symptom questionnaire at the initial screening. A brief telephone survey within the patient and control group was performed 5-8 months after the initial screening. R ver. 4.0.5 and imbalanced RandomForest (RF) machine-learning algorithm were used for data explorations and analyses.

RESULTS

A total of 590 patients were included in the study. The general presence of gastrointestinal symptoms 208.2 days (153-230 days) after the initial acute severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection was 19% in patients with moderate-to-serious course of the disease and 7.3% in patients with mild course compared with 3.0% in SARS-CoV-2 negative controls (P < 0.001). Diarrhea and abdominal pain are the most prevalent post-COVID gastrointestinal symptoms. RF machine-learning algorithm identified acute diarrhea and antibiotics administration as the strongest predictors for gastrointestinal sequelae with area under curve of 0.68. Variable importance for acute diarrhea is 0.066 and 0.058 for antibiotics administration.

CONCLUSION

The presence of gastrointestinal sequelae 7 months after the initial SARS-CoV-2 infection is significantly higher in patients with moderate-to-severe course of the acute COVID-19 compared with asymptomatic patients or those with mild course of the disease. The most prevalent post-COVID gastrointestinal symptoms are diarrhea and abdominal pain. The strongest predictors for persistence of these symptoms are antibiotics administration and acute diarrhea during the initial infection.