Bridging the gap between older folks and mental healthcare with machine learning.

Alexandra Hernandez

Done as a part of San Diego State University's [2024 Big Data Hackathon](https://bigdataforsandiego.github.io/), my teammate and I (Friar Health) created [MindTheGap](https://mind-the-age.streamlit.app/), a web app dedicated to helping elderly patients of SHARP Healthcare start their mental healthcare journeys by providing them with resources based on their information–both self-reported and medical staff provided. Utilizing a classification model trained on recognizing potential mental health "leanings" (via text-based classifications) and suggesting them resources based on those weightings. The goal of the app is to make starting to care for your mental health less intimidating, especially with the loads of information that need to be sifted through to find the right fit.

Source code can be accessed [here](https://github.com/alexandrarh/Team-133).


Group case study analyzing researcher data on marijuana usage to determine what factors are best for marking recent usage of marijuana. Used metrics such as blood, oral fluid, and breath to examine marijuana dosages and compound differences.

Conducted a detailed case study examining marijuana usage detection methods using advanced statistical analysis in R and RStudio. Leveraging powerful data science packages like `tidyverse` and `ggplot2`, we systematically investigated multiple biological markers—including blood, oral fluid, and breath samples to identify the most effective indicators of recent marijuana consumption. By applying rigorous machine learning techniques such as Random Forest modeling, we evaluated the sensitivity and specificity of various marijuana compounds, with a specific focus on potential roadside detection applications for law enforcement. Our findings revealed a clear hierarchy of reliability, with blood samples emerging as the most dependable method for detecting recent marijuana use, closely followed by oral fluid measurements. This research provides critical insights into the complex landscape of marijuana detection methodologies, offering valuable guidance for forensic and medical professionals seeking accurate and timely assessment of recent cannabis consumption.

Due to research privacy protocol, code can't be displayed.


Case Study 01: Biomarkers of Recent Usage

Group project/special presentation for COGS 188. Discusses and demos the real world implications of racial biases in facial recognition algorithms.

Group project/special presentation done on the usage of Facial Recognition, and how internal–and external–biases such as racial biases are implicated in the algorithms in the real world, and what these implications can do for those utilizing such methods. Demo is done with Jupyter notebooks in Python, along with data from `utkcropped` and `age_gender` databases. By getting to the biases at their core, we then discuss and propose potential solutions in combating such biases from popping up in recognition algorithms.

Repository for the project can be found [here](https://github.com/alexandrarh/COGS188_group_project).
