Abstract: Methods are provided to predict, based on chest radiographs, whether a patient suffers from any comorbidities in one or more high-level comorbidity classes, e.g., high-level hierarchical condition categories. The chest radiograph is a low-cost, minimally-invasive source of visual information that is able to accurately predict whether a subject suffers from diabetes with chronic complications, morbid obesity, congestive heart failure, specified heart arrhythmias, vascular disease, or chronic obstructive pulmonary disease, among other high-level comorbidity classes. The predictive methods provided herein are also able to accurately predict overall measures of health-related complications, including the risk adjustment factor. These predictive methods can be used to focus review of medical records, improving the detection of disease. Outputs generated by these methods can also be used to predict the severity of disease and/or the extent of care provided to subjects with COVID-19 or related diseases.