Collin Engstrom
University of Wisconsin-Madison
6 Papers
5 Citations
Collin Engstrom is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Software deployment & Induced pluripotent stem cell. The author has an hindex of 2, co-authored 5 publications. Previous affiliations of Collin Engstrom include Winona State University.
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Papers
Human pluripotent stem cell-derived neural constructs for predicting neural toxicity
Michael P. Schwartz,Zhonggang Hou,Nicholas E. Propson,Jue Zhang,Collin Engstrom,Vítor Santos Costa,Peng Jiang,Bao Kim Nguyen,Jennifer M. Bolin,William T. Daly,Yu Wang,Ron Stewart,C. David Page,William L. Murphy,James A. Thomson,James A. Thomson,James A. Thomson +16 more
TL;DR: Stem cell biology, tissue engineering, bioinformatics, and machine learning were combined to implement an in vitro human cellular model for developmental neurotoxicity screening and demonstrated the value of human cell-based assays for predictive toxicology and should be useful for both drug and chemical safety assessment.
314
Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.
Brian W. Patterson,Collin Engstrom,Varun Sah,Maureen A. Smith,Eneida A. Mendonça,Michael S. Pulia,Michael D. Repplinger,Azita G. Hamedani,David C. Page,Manish N. Shah +9 more
TL;DR: The ability to translate the results of the analysis to the potential tradeoff between referral numbers and NNT offers decisionmakers the ability to envision the effects of a proposed intervention before implementation.
42
Machine learning-assisted screening for cognitive impairment in the emergency department
Simon R. Yadgir,Collin Engstrom,Collin Engstrom,Gwen Costa Jacobsohn,Rebecca K. Green,Courtney M.C. Jones,Jeremy T. Cushman,Jeremy T. Cushman,Thomas V. Caprio,Amy J.H. Kind,Amy J.H. Kind,Michael Lohmeier,Manish N. Shah,Brian W. Patterson +13 more
TL;DR: In this paper, the authors developed and evaluated an automated screening tool to identify a subset of patients at high risk for cognitive impairment in the emergency department (ED) using only variables available in electronic health record (EHR).
16
Iterative processes: a review of semi-supervised machine learning in rehabilitation science.
TL;DR: It is established that SSML was a feasible approach for analysis of complex data in rehabilitation research and that semi-supervised approaches may be more accurate than supervised machine learning approaches.
7
Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department
TL;DR: In this paper , a case study describes challenges and barriers identified and overcome in such an operationalization for a model aimed at predicting risk of outpatient falls after Emergency Department (ED) visits among older adults.