Background: Identifying patients at risk of hereditary cancer based on their family health history is a highly nuanced task. Frequently, patients at risk are not referred for genetic counseling as providers lack the time and training to collect and assess their family health history. Consequently, patients at risk do not receive genetic counseling and testing that they need to determine the preventive steps they should take to mitigate their risk. Objective: This study aims to automate clinical practice guideline recommendations for hereditary cancer risk based on patient family health history. Methods: We combined chatbots, web application programming interfaces, clinical practice guidelines, and ontologies into a web service–oriented system that can automate family health history collection and assessment. We used Owlready2 and Protégé to develop a lightweight, patient-centric clinical practice guideline domain ontology using hereditary cancer criteria from the American College of Medical Genetics and Genomics and the National Cancer Comprehensive Network. Results: The domain ontology has 758 classes, 20 object properties, 23 datatype properties, and 42 individuals and encompasses 44 cancers, 144 genes, and 113 clinical practice guideline criteria. So far, it has been used to assess >5000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 (SD 1.9) seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds. Conclusions: Web service–enabled, chatbot-oriented family health history collection and ontology-driven clinical practice guideline criteria risk assessment is a simple and effective method for automating hereditary cancer risk screening.

Background: Metabolic dysfunction associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide and affects >25% in the United States population. We hypothesized that clinical features present in electronic health records (EHR) could be extracted early to characterize patients with MASLD who are at high risk of early mortality and that machine learning models would predict mortality better than noninvasive assessments of liver disease/fibrosis. Methods: Using previously published criteria for MASLD, applied to data from the US Veterans Affairs EHR, we identified a cohort of 13,071 patients between 2000 and 2018 who had an initial diagnosis of MASLD without clinical evidence of cirrhosis. We subsequently used machine-learning and conducted analysis of variance and logistic regression to identify clinical variables to characterize cirrhosis risk and predict mortality within the ensuing 5-years. Results: The average age of the cohort was 60 years, had a BMI of 31, and 34% diabetes prevalence. Patients who progressed to cirrhosis were younger when first diagnosed with MASLD (56), had a higher BMI (33), and had significantly higher noninvasive fibrosis scores. Having diabetes at index MASLD diagnosis significantly increased the risk of developing cirrhosis and doubled the risk cirrhosis plus HCC (2.09 CI:1.217–3.63). Our machine-learning model performed significantly better than FIB-4 at predicting mortality within 5-years of being diagnosed with MASLD (AUC 83% vs 68%). Conclusion: Our data suggest that machine learning models based on data extracted from the EHR early during MASLD can identify patients likely to develop cirrhosis and predict short term mortality.

Background/Objectives: Multimorbidity, where patients have ≥2 comorbidities, is recognized as a major challenge for health systems worldwide, driving up morbidity and cost. The differences in multimorbidity burden between those with and without type-2 diabetes mellitus (T2DM) in the Veteran population are not well studied. This large retrospective cohort study fills the existing gap. Methods: Using a retrospective cohort of adult Veterans with and without T2DM, we examined 29 comorbidities defined by Elixhauser criteria for 10,499,394 Veterans from 1 January 2008 to 31 December 2009. We then ascertained diabetes status for 10 years of follow-up from 1 January 2010 to 31 December 2019. Multimorbidity status was categorized using the Elixhauser comorbidity index (0, 1, ≥2) and logistic regression was used to estimate the odds ratio (OR) for its association with risk of diabetes, adjusting for covariates. Results: Compared to those with zero comorbidities, the odds of having diabetes were more than doubled (2.53, CI: 2.51–2.54) for those with ≥2 comorbidities. Conclusions: The doubling of the odds of T2DM among those with more than one comorbidity is typical of Veterans with T2DM. In addition, the odds were significantly higher for Hispanics compared to other groups when adjusting for covariates. This calls for more attention to reduce the risk of T2DM through improved management and effective use of treatments informed by disparities that exist in the VHA.

Cholangiocarcinoma (CCA) is a highly heterogeneous malignancy with limited thera-peutic options and poor prognosis, underscoring the need for more effective precision medicine strategies. In this context, digital twins (DTs) and biological twins (BTs) have emerged as complementary approaches to model disease complexity and personalize treatment. DTs integrate multi-modal patient data, including imaging, clinical, and multi-omics information, into computational frameworks capable of predicting disease trajectories and therapeutic responses. BTs, such as patient-derived organoids and xenografts, enable functional validation of these predictions in patient-specific ex-perimental systems. This review synthesizes current advances in DT- and BT-based approaches in CCA, highlighting their respective strengths and limitations, and dis-cusses emerging hybrid frameworks that iteratively connect computational modeling with biological experimentation. Despite promising developments, significant chal-lenges remain, including data integration, model validation, regulatory alignment, and clinical adoption. Addressing these barriers will be critical to translating twin-based strategies into clinically actionable tools for personalized oncology.

BACKGROUND Identifying patients at risk of hereditary cancer based on their family health history is a highly nuanced task. Frequently, patients at risk are not referred for genetic counseling as providers lack the time and training to collect and assess their family health history. Consequently, patients at risk do not receive genetic counseling and testing that they need to determine the preventive steps they should take to mitigate their risk. OBJECTIVE This study aims to automate clinical practice guideline recommendations for hereditary cancer risk based on patient family health history. METHODS We combined chatbots, web application programming interfaces, clinical practice guidelines, and ontologies into a web service–oriented system that can automate family health history collection and assessment. We used Owlready2 and Protégé to develop a lightweight, patient-centric clinical practice guideline domain ontology using hereditary cancer criteria from the American College of Medical Genetics and Genomics and the National Cancer Comprehensive Network. RESULTS The domain ontology has 758 classes, 20 object properties, 23 datatype properties, and 42 individuals and encompasses 44 cancers, 144 genes, and 113 clinical practice guideline criteria. So far, it has been used to assess >5000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 (SD 1.9) seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds. CONCLUSIONS Web service–enabled, chatbot-oriented family health history collection and ontology-driven clinical practice guideline criteria risk assessment is a simple and effective method for automating hereditary cancer risk screening.