Increased relative proportions of advanced melanoma among veterans: A comparative analysis with the Surveillance, Epidemiology, and End Results registry

Chang, Michael S.; La, Jennifer; Trepanowski, Nicole; Cheng, David; Bihn, John; Do, Nhan; Brophy, Mary; Fillmore, Nathanael; Hartman, Rebecca I.

doi:10.1016/j.jaad.2022.02.063

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis in the US-VA system was conducted with approval from the VA Boston Healthcare System Institutional Review Board. For the US-VA system, we used data from the VA Corporate Data Warehouse (CDW), which collates electronic health record and cancer registry information on veterans treated at VA facilities nationwide (Price, Shea, and Gephart 2015), using methods similar to prior work (Elbers et al 2020; Chang et al 2022)(Wu et al 2022). The CDW includes electronic health record data from 1999 to the present originating from the comprehensive range of primary care and specialized services the VA provides at its inpatient and outpatient facilities, as well as claims data for care received at outside facilities and paid for by the VA. Each outpatient visit in the database is associated with the date it occurred, and each inpatient visit is associated with an admission and discharge date.…”

Section: Methodsmentioning

confidence: 99%

Pancreatic cancer risk predicted from disease trajectories using deep learning

Yuan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be facilitated by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a large corpus of more than 6 million patient records spanning 40 years with 24,000 pancreatic cancer cases in the Danish National Patient Registry. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories. In addition, the models predict the risk of cancer occurrence in time intervals of 3 to 60 months duration after risk assessment. For cancer occurrence within 12 months, the performance of the best model trained on full trajectories (AUROC=0.91) substantially exceeds that of a model without time information (AUROC=0.81). For the best model, lower performance (AUROC=0.86) results when disease events within a 3 month window before cancer diagnosis are excluded from training, reflecting the decreasing information value of earlier disease events. These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of real-world population-wide clinical screening trials, in which high risk patients are assigned to serial imaging and measurement of blood-based markers to facilitate earlier cancer detection. AI on real-world clinical records has the potential to shift focus from treatment of late- to early-stage cancer, benefiting patients by improving lifespan and quality of life.

show abstract

Section: Methodsmentioning

confidence: 99%

Pancreatic cancer risk predicted from disease trajectories using deep learning

Yuan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the US-VA system, we used data from the VA CDW, which collates EHR and cancer registry information on veterans treated at VA facilities nationwide 51 , using methods similar to prior work [52][53][54] . The CDW includes EHR data from 1999 to the present, originating from the comprehensive range of primary care and specialized services the VA provides at its inpatient and outpatient facilities as well as claims data for care received at outside facilities and paid for by the VA. Each outpatient visit in the database is associated with the date of the visit, and each inpatient visit is associated with an admission and discharge date.…”

Section: Military Veterans Us-va Datasetmentioning

confidence: 99%

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Brunak

Yuan

Hjaltelin

et al. 2023

Nat Med

Self Cite

View full text Add to dashboard Cite

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

show abstract