Quentin Boussard

Undergraduate Student, Stanford University

1 active project

BPD

This research aims to: Evaluate the effectiveness of various treatments for Borderline Personality Disorder (BPD) using a Python algorithm, focusing on 12-month outcomes like hospitalizations and medication adjustments. Investigate how treatment outcomes differ across demographics (race, age, socio-economic status). Analyze…

Scientific Questions Being Studied

This research aims to:

Evaluate the effectiveness of various treatments for Borderline Personality Disorder (BPD) using a Python algorithm, focusing on 12-month outcomes like hospitalizations and medication adjustments.

Investigate how treatment outcomes differ across demographics (race, age, socio-economic status).

Analyze patterns of co-occurring mental health conditions (depression, anxiety, PTSD) in BPD and their impact on treatment outcomes.

Study healthcare utilization trends among individuals with BPD and identify predictors of high healthcare use (hospital visits, ED visits, outpatient care, refills, telehealth visits).

Project Purpose(s)

  • Disease Focused Research (Bipolar Disorder)

Scientific Approaches

Dataset Description:
The primary dataset will consist of longitudinal clinical data from patients diagnosed with Borderline Personality Disorder. This dataset will include demographic information (such as age, race, socio-economic status), clinical variables (symptom severity, comorbid conditions), treatment history (therapeutic interventions received, medications prescribed), and healthcare utilization metrics.

Research Methods:
Data Analysis: Descriptive statistics will be used to characterize the study population and summarize treatment outcomes and healthcare utilization patterns.
Machine Learning Algorithms: Python-based machine learning algorithms will be applied to identify predictive models for treatment outcomes and healthcare utilization. Techniques such as classification and regression will be employed.
Statistical Analysis: Multivariate analysis techniques will be used to analyze the relationship between demographic factors, comorbidities, and treatment outcomes.

Anticipated Findings

-The study aims to identify which therapeutic interventions (such as Dialectical Behavior Therapy, Cognitive Behavioral Therapy, or medication regimens) are most effective for improving long-term outcomes in BPD patients. This knowledge can guide clinicians in selecting the most appropriate treatment strategies tailored to individual patient characteristics and needs.

-By analyzing treatment outcomes across different demographic groups (such as race, age, and socio-economic status), the study aims to uncover disparities in healthcare access and outcomes. Understanding these disparities can inform efforts to reduce inequities and improve healthcare delivery for diverse patient populations.

-By identifying predictors of high healthcare utilization among BPD patients (including hospitalizations, ED visits, outpatient visits, and telehealth usage), the study aims to optimize healthcare resource allocation and improve cost-effectiveness in managing this patient population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Yeon Mi Hwang - Research Fellow, Stanford University
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