Predicting HCV Incidence in Latinos with High-Risk Substance Use: A Data Science Approach

A machine learning approach using migration-related cultural stress to classify depression and post-traumatic stress disorder among hurricane survivors

BACKGROUND

Data science approaches have increasingly been used in behavioral health research and may be useful for addressing social factors contributing to disparities in health status. This study evaluated the importance of cultural stress-related factors in classifying depression and post-traumatic stress disorder (PTSD) among adult survivors (N = 319) of Hurricane Maria who migrated from Puerto Rico to the United States mainland.

METHODS

We evaluated the performance of random forests (RF) and logistic regression (LR) for classifying PTSD and depression. Models included demographic, hurricane exposure, and migration-related cultural stress variables. We inspected area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, F1 score, precision, recall, and specificity.

RESULTS

Negative context of reception and language-related stressors were moderately important for accurately classifying depression and PTSD. For classifying depression, RF showed higher accuracy, balanced accuracy, specificity, precision, and F1. For classifying PTSD, RF showed higher accuracy, specificity, precision, and F1.

LIMITATIONS

A more thorough classification model would also include biomarkers (e.g., of allostatic load), family, community, or neighborhood-level attributes. Findings may not generalize to other groups who have experienced crisis-related migration.

CONCLUSIONS

Findings underscore the importance of culturally and linguistically appropriate and trauma-informed clinical services for recent migrants. Use of assessments to identify pre-migration and post-migration stressors could inform clinical practice with migrants presenting with behavioral health-related difficulties.

Machine learning for prediction of viral hepatitis: A systematic review and meta-analysis

BACKGROUND

Lack of accurate and timely diagnosis of hepatitis poses obstacles to effective treatment, disease progression prevention, complication reduction, and life-saving interventions of patients. Utilizing machine learning can greatly enhance the achievement of timely and precise disease diagnosis. Therefore, we carried out this systematic review and meta-analysis to explore the performance of machine learning algorithms in predicting viral hepatitis.

METHODS

Using an extensive literature search in PubMed, Scopus, and Web of Science databases until June 15, 2023, English publications on hepatitis prediction using machine learning algorithms were included. Two authors independently extracted pertinent information from the selected studies. The PRISMA 2020 checklist was followed for study selection and result reporting. The risk of bias was checked using the International Journal of Medical Informatics (IJMEDI) checklist. Data were analyzed using the 'metandi' command in Stata 17.

RESULTS

Twenty-one original studies were included, covering 82 algorithms. Sixteen studies utilized five algorithms to predict hepatitis B. Ten studies used five algorithms for hepatitis C prediction. For hepatitis B prediction, the SVM algorithms demonstrated the highest sensitivity (90.0%; 95% confidence interval (CI): 77.0%-96.0%), specificity (94%; 95% CI: 90.0%-97.0%), and a diagnostic odds ratio (DOR) of 145 (95% CI: 37.0-559.0). In the case of hepatitis C, the KNN algorithms exhibited the highest sensitivity (80%; 95% CI:30.0%-97.0%), specificity (95%; 95% CI: 58.0%-99.0%), and DOR (72; 95% CI: 3.0-1644.0) for prediction.

CONCLUSION

SVM and KNN demonstrated superior performance in predicting hepatitis. The proper algorithm along with clinical practice could improve hepatitis prediction and management.

Cancer Epidemiology in Hispanic Populations: What Have We Learned and Where Do We Need to Make Progress?

The Hispanic/Latino(x) population (H/L) in the United States of America is heterogeneous and fast growing. Cancer is the number one cause of death among H/Ls, accounting for 21% of deaths. Whereas for the most common cancers, incidence rates are lower in H/Ls compared with non-H/L White (NHW) individuals, H/Ls have a higher incidence of liver, stomach, cervical, penile, and gallbladder cancers. H/L patients tend to be diagnosed at more advanced stages for breast, colorectal, prostate, and lung cancers, and melanoma compared with NHW individuals. Etiologic and cancer outcomes research among H/Ls lags other populations. In this review, we provide a summary of challenges, opportunities, and research priorities related to cancer etiology, cancer outcomes, and survivorship to make progress in addressing scientific gaps. Briefly, we prioritize the need for more research on determinants of obesity, nonalcoholic fatty liver disease and its progression to liver cancer, stomach and gallbladder cancers, and pediatric acute lymphoblastic leukemia. We emphasize the need to improve cancer screening, early detection of cancer, and survivorship care. We highlight critical resources needed to make progress in cancer epidemiologic studies among H/L populations, including the importance of training the next generation of cancer epidemiologists conducting research in H/Ls.