Browsing by Author "Lakmala, Prathima"

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    Beyond content knowledge: transferable skills connected to experience as a peer-leader in a PLTL program and long-term impacts
    (Springer, 2020) Chase, Anthony; Rao, Anusha S.; Lakmala, Prathima; Varma-Nelson, Pratibha; Chemistry and Chemical Biology, School of Science
    Background Being a successful peer-led team learning (PLTL) workshop leader involves developing content knowledge and workshop facilitation skills. These skills connected to being a peer leader, however, do not terminate at the end of one’s undergraduate program. In fact, many former peer leaders denote having been a peer leader on their LinkedIn profile. This study examines the transferable skills that former peer leaders identified as being valuable in their current positions. We conducted semi-structured interviews with former peer leaders from varying disciplines, universities, ages, and years since being a peer leader. Results Interview questions captured leadership experiences including successes and challenges of being peer leaders, roles and responsibilities, and specific transferable skills further developed by being peer leaders and how they are being utilized in the leaders’ current position. Conclusion Thematic analyses of these interviews indicate that former peer leaders recognize leadership skills, coping with many challenges (including not having the right answer), collaboration/teamwork skills, self-confidence, and problem-solving skills as being relevant and frequently used in their current work.
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    A Predictive Modelling Approach in the Diagnosis of Parkinson's Disease Using Cerebrospinal Fluid Biomarkers
    Lakmala, Prathima; Jones, Josette; Lai, Patrick
    The research in Parkinson's disease {PD) using biomarkers has long been dominated by measuring dopamine metabolites or alpha-Synuclein in cerebrospinal fluid. However, these markers do not allow early detection or monitoring of disease progression. In the recent years, metabolic profiling of body fluids has become powerful and promising tools in identification of the novel biomarkers in the diagnosis of the disease. While not much research has been done using machine learning techniques and predictive modeling to predict the severity of Parkinson's disease. The purpose of this project is to apply a predictive modeling approach in the diagnosis of Parkinson's disease using Cerebrospinal Fluid Biomarkers. The dataset for this study was collected from the PPMI website which comprises of 360 - Parkinson's patient, 220 - Control and 20 - SWEDD (Scans without evidence for dopaminergic deficit). Various predictive models were developed in order to classify the disease based on its severity. The various machine learning algorithms used in this process are Decision tree, Random forest, Support Vector Machine {SVM), K- Nearest Neighbor (KNN), and Gradient boosting. Feature scaling and Mean normalization was applied to standardize the dataset. The above mentioned machine learning algorithms were applied on the Parkinson's Progression Markers Initiative (PPMI) data and accuracy for each algorithm was calculated. Out of all the models, Random forest and Gradient Boosting gave the best classification accuracy of 66.67%. In conclusion, the main factors that might have affected accuracy of the model are dataset size, missing data and number of features. To sum up, while the results show some predictive power, we conclude negative results and hence these models are not Clinically significant.