Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare

Objective

Clinical applications of machine learning are promising as a tool to improve patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for screening. Possible clinical applications are especially prominent in dermatology as many diseases and conditions present visually. This allows a machine learning model to analyze and diagnose conditions using patient images and data from electronic health records (EHRs) after training on clinical datasets but could also introduce bias. Despite promising applications, artificial intelligence has the capacity to exacerbate existing demographic disparities in healthcare if models are trained on biased datasets.

Methods

Through systematic literature review of available literature, we highlight the extent of bias present in clinical datasets as well as the implications it could have on healthcare if not addressed.

Results

We find the implications are worsened in dermatological models. Despite the severity and complexity of melanoma and other dermatological diseases as well as differing disease presentations based on skin-color, many imaging datasets underrepresent certain demographic groups causing machine learning models to train on images of primarily fair-skinned individuals leaving minorities behind.

Conclusion

In order to address this disparity, research first needs to be done investigating the extent of the bias present and the implications it may have on equitable healthcare.

Role of Hypertension on the Severity of COVID-19: A Review

ABSTRACT

The novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved into a global pandemic. The substantial morbidity and mortality associated with the infection has prompted us to understand potential risk factors that can predict patient outcomes. Hypertension has been identified as the most prevalent cardiovascular comorbidity in patients infected with COVID-19 that demonstrably increases the risk of hospitalization and death. Initial studies implied that renin-angiotensin-aldosterone system inhibitors might increase the risk of viral infection and aggravate disease severity, thereby causing panic given the high global prevalence of hypertension. Nonetheless, subsequent evidence supported the administration of antihypertensive drugs and noted that they do not increase the severity of COVID-19 infection in patients with hypertension, rather may have a beneficial effect. To date, the precise mechanism by which hypertension predisposes to unfavorable outcomes in patients infected with COVID-19 remains unknown. In this mini review, we elaborate on the pathology of SARS-CoV-2 infection coexisting with hypertension and summarize potential mechanisms, focusing on the dual roles of angiotensin-converting enzyme 2 and the disorders of renin-angiotensin-aldosterone system in COVID-19 and hypertension. The effects of proinflammatory factors released because of immune response and gastrointestinal dysfunction in COVID-19 are also discussed.

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Background

COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance.

Objective

Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved.

Methods

We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes.

Results

A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM.

Conclusions

We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.