A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning.

INTRODUCTION: Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high-risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real-world database containing clinical features and employing numerous artificial intelligent approach algorithms. METHODS: This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest. RESULTS: The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid-modifying drug use. CONCLUSION: This study successfully developed a highly accurate 4-year risk model for pancreatic cancer in patients with diabetes using real-world clinical data and multiple machine-learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods: Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results: The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion: This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

The long-lasting relationship of distress on radiation oncology-specific clinical outcomes.

PURPOSE: The diagnosis and treatment of cancer can have significant mental health ramifications. The National Comprehensive Cancer Network currently recommends using a distress screening tool to screen patients for distress and facilitate referrals to social service resources. Its association with radiation oncology-specific clinical outcomes has remained relatively unexplored. METHODS AND MATERIALS: With institutional review board approval, National Comprehensive Cancer Network distress scores were collected for patients presenting to our institution for external beam radiation therapy during a 1-year period from 2015 to 2016. The association between distress scores (and associated problem list items and process-related outcomes) and radiation oncology-related outcomes, including inpatient admissions during treatment, missed treatment appointments, duration of time between consultation and treatment, and weight loss during treatment, was considered. RESULTS: A total of 61 patients who received either definitive (49 patients) or palliative (12 patients) treatment at our institution and completed a screening questionnaire were included in this analysis. There was a significant association between an elevated distress score (7+) and having an admission during treatment (36% vs 11%; P = .04). Among the patients treated with definitive intent, missing at least 1 appointment (71% vs 26%; P = .03) and having an admission during treatment (57% vs 10%; P = .009) were significantly associated with our institutional definition of elevated distress. We found no correlation between distress score and weight loss during treatment or a prolonged time between initial consult and treatment start. CONCLUSIONS: High rates of distress are common for patients preparing to receive radiation therapy. These levels may affect treatment compliance and increase rates of hospital admissions. There remains equipoise in the best method to address distress in the oncology patient population. These results may raise awareness of the consequences of distress among radiation oncology patients. Specific interventions to improve distress need further study, but we suggest a more proactive approach by radiation oncologists in addressing distress.