Browsing by Subject "Statistical models"

Now showing 1 - 2 of 2
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    Identification of colorectal cancer using structured and free text clinical data
    (Sage, 2022) Redd, Douglas F.; Shao, Yijun; Zeng-Treitler, Qing; Myers, Laura J.; Barker, Barry C.; Nelson, Stuart J.; Imperiale, Thomas F.; Medicine, School of Medicine
    Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even with the recent screening guidelines recommending that screening begins at age 45, nearly half of all early-onset colorectal cancer will be missed. Methods are needed to identify high-risk individuals in this age group for targeted screening. Colorectal cancer studies, as with other clinical studies, have required labor intensive chart review for the identification of those affected and risk factors. Natural language processing and machine learning can be used to automate the process and enable the screening of large numbers of patients. This study developed and compared four machine learning and statistical models: logistic regression, support vector machine, random forest, and deep neural network, in their performance in classifying colorectal cancer patients. Excellent classification performance is achieved with AUCs over 97%.
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    Research Needs for Prognostic Modeling and Trajectory Analysis in Patients with Disorders of Consciousness
    (Springer, 2021) Hammond, Flora M.; Katta-Charles, Sheryl; Russell, Mary Beth; Zafonte, Ross D.; Claassen, Jan; Wagner, Amy K.; Puybasset, Louis; Egawa, Satoshi; Laureys, Steven; Diringer, Michael; Stevens, Robert D.; Curing Coma Campaign and its Contributing Members; Physical Medicine and Rehabilitation, School of Medicine
    Background: The current state of the science regarding the care and prognosis of patients with disorders of consciousness is limited. Scientific advances are needed to improve the accuracy, relevance, and approach to prognostication, thereby providing the foundation to develop meaningful and effective interventions. Methods: To address this need, an interdisciplinary expert panel was created as part of the Coma Science Working Group of the Neurocritical Care Society Curing Coma Campaign. Results: The panel performed a gap analysis which identified seven research needs for prognostic modeling and trajectory analysis ("recovery science") in patients with disorders of consciousness: (1) to define the variables that predict outcomes; (2) to define meaningful intermediate outcomes at specific time points for different endotypes; (3) to describe recovery trajectories in the absence of limitations to care; (4) to harness big data and develop analytic methods to prognosticate more accurately; (5) to identify key elements and processes for communicating prognostic uncertainty over time; (6) to identify health care delivery models that facilitate recovery and recovery science; and (7) to advocate for changes in the health care delivery system needed to advance recovery science and implement already-known best practices. Conclusion: This report summarizes the current research available to inform the proposed research needs, articulates key elements within each area, and discusses the goals and advances in recovery science and care anticipated by successfully addressing these needs.