Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods. The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods.
Regional rural medical school campuses offer many opportunities for medical students to gain more hands-on experience, have more direct interaction with attending physicians, and cultivate a deeper understanding of challenges and opportunities specific to rural medicine. Some specialty services such as neurology are not available at these small regional campuses, and telemedicine technology can be a valuable tool to address this need. We report the implementation of teleneurology stroke consultation services as part of the third-year neurology clerkship at a regional medical school campus. We analyzed daily clinical notes and student satisfaction surveys. Students saw many common and important presentations of cerebrovascular events. Students worked as part of a multi-disciplinary care team while following these patients through their hospital course with effective instruction provided by remote stroke neurologists. All students strongly agreed that telemedicine was a positive component of the clerkship. We conclude that teleneurology is an effective way to provide inpatient neurology clinical exposure, especially when remote attendings have a strong screen presence and are enthusiastic about teaching. We believe these findings could be useful to other campuses considering similar teaching methods, as innovations in telemedicine continue to address challenges in medical education and clinical care. The authors have no conflicts of interest to report and the Baptist Health Madisonville Institutional Review Board found this study to be exempt.
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