NIKHITHA TELAGAMALLA

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Skills Summary

Languages: Python, R, SQL

Tools/ Software: MySQL, WEKA, Tableau, UMLS, Selenium, STATA

Platforms: macOS, Windows, Linux

Academic Qualifications

MSc Health Informatics & Data Analytics

George Mason University, Fairfax, VA

Aug 2021 - May 2023

GPA: 3.91

Bachelor of Dental Surgery

SVS Institute of Dental Sciences, India

Sep 2011 - Apr 2017

GPA: 3.78

Professional Experience

Healthcare Data Analyst

MedStar Health Research Institute

Jan 2023 – Present

In the Behavioral FIPS project, I used Microsoft Excel to extend Geocode entries and generate 11 and 12-digit FIPS codes. Subsequently, I employed Python in Jupyter notebook to seamlessly merge these enhanced geocode files with SVI and ADI data, establishing a comprehensive dataset for advanced analysis.

During my internship, I collaborated with MedStar Health hospitals to analyze EHR data on maternal health. Performed tasks such as data cleaning, preprocessing, descriptive statistics, data analysis, statistical analysis on a dataset consisting of 32,480 patients. Effectively presented the findings to the team, worked collaboratively with a team of five fellows. During the internship, utilized SQL, Python, Excel as essential tools for data analysis.

Adjunct Professor / Graduate Teaching Assistant

George Mason University

Aug 2022 - May Present

Assisting, conducting dissection labs and teaching anatomy, physiology labs for 50 students. 50% students got A grade and remaining 50% got more than average grade.

Junior Doctor

SMS Dental Hospital (July 2020 - Dec 2020)

Government Dental College and Hospital

(Nov 2019 - April 2020) SVS Dental Hospital (May 2017 - Oct 2019)

Maintained patient EHR, examined the patient’s oral cavity, investigated, diagnosed impaired tooth, did various minor and major surgical extractions.

Academic Projects

All of Us Project (NIH): Obtained clearance from the NIH to work with their database within the NIH workspace. Constructed a cohort of 35,040 patients focused on leukemia, incorporating independent variables from the All of Us dataset. Preprocessed the dataset by isolating unique variables, removing unnecessary columns, duplicates, and binarizing the data. Applied Lasso Regression, temporal analysis, and network modeling to identify four significant variables associated with leukemia: vomiting, acute tubular necrosis, radiation dermatitis, and thiamine deficiency. (Apr-May '23)

Data Analysis (Tableau): Employing Tableau, created a visually informative dashboard that depicted average readmission rates at the national and state levels using data provided by CMS for 3,300 hospitals. Conducted a comparative analysis to assess the readmission rates of Florida hospitals in comparison to national, state benchmarks. Based on the findings, formulated recommendations aimed at enhancing readmission rates for these facilities. (May '22)

Voice assisted receptionist at Doctor’s office (Python, Alexa console): Within the context of a doctor's office, developed a voice-assisted receptionist system using the Python programming language and the Alexa developer console. This system was designed to guide and assist individuals with their appointments when visiting the doctor's office. The resulting Alexa skill, integrated with the patient's Alexa device, provided interactive, helpful support during their visit. (Nov '22)

Image Enhancement in JPEG Domain in persons with Visual Impairment (Matlab): In the realm of image enhancement for individuals with visual impairment, I leveraged the Matlab programming language to enhance images specifically in the JPEG domain. This was achieved through the utilization of image compression and subsequent decompression techniques based on the Discrete Cosine Transform standard. (Nov '22)

Deep Learning Model to classify chest X-rays images (Python, PyTorch): Employing the PyTorch machine learning framework and Python language, conducted an analysis on random chest X-ray images sourced from Google. Utilizing a pre-trained Convolutional Neural Network (CNN) model trained on extensive datasets, successfully classified the chest X-ray images. (Oct '22)

Prediction of Heart Failure in Patients (SQL, PostgreSQL, Pgadmin, WEKA, ML Algorithms): Utilizing the Oregon claims dataset, employed machine learning models to predict the occurrence of heart failure among a population of 6,973,218 patients admitted with diverse medical conditions. The prediction model was constructed using logistic regression, the Bayes network algorithm, yielding ROC values of 0.725 and 0.731, respectively, as measures of their predictive performance. (Aug '22)

Statistical analysis of Chronic Health conditions (STATA): Using the STATA software, conducted an in-depth statistical analysis on chronic health conditions including Stroke, Heart disease, Alzheimer's, and COPD. By employing descriptive statistics, Chi-square tests, and logistic regression, identified substantial correlations between these conditions and factors such as falls, age, gender, Medicaid. The dataset was sourced from the National Survey of Residential Care Facilities (NSRCF). (May '22)

Web scraping and Data Visualization (Python, Selenium): Utilizing Python and Selenium, developed a Python program that automated the process of web scraping from http://covid19.who.int/. By leveraging the Beautiful Soup library, extracted, gathered COVID-19 case data from the website for further analysis and visualization purposes. (Dec '21)

Volunteer activities

Volunteered for Immunization Drive of school children by helping in entering the data of each student in the EHR.

Worked as Community Involvement Staff for Mason Life – helped students with autism in the extracurricular activities.

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