Python Meets Elicit: Decoding Medical Insights from AI-Driven Data Dives

I'm working on a Python project involving scientific publishing data. In the pursuit of enhancing medical knowledge accessibility through data science, I've utilized an emerging AI research tool known as Elicit, Python's Pandas library to analyze trends in epilepsy surgery publications. We'll analyze trends, popular research topics, and changes in scientific thought over time and perform basic data analysis on a scientific publishing dataset. This will include loading the data, performing simple analyses like counting the number of publications per year, and creating basic visualizations.

Step 1: Search for research papers

Ask a research question using natural language and get back a list of relevant papers from Elicit's database of 200 million publications.

Step 2: Set Up Your Python Environment

Ensure you have Python installed and install necessary packages:

pip install pandas matplotlib

Step 3: Import Libraries

In your Python script, start by importing Pandas and Matplotlib:

import pandas as pd
import matplotlib.pyplot as plt

Step 4: Load and Explore the Data

Assuming you have a CSV file epilepsy_surgery_publications.csv from Elicit:

data = pd.read_csv('epilepsy_surgery_publications.csv')
print(data.head())  # Display first few rows

Step 5: Basic Data Cleaning

Check and handle missing values if necessary:

print(data.isnull().sum())  # Check missing values
data = data.dropna()       # Drop rows with missing values

Step 6: Analyze Publication Trends

Count publications per year:

publications_per_year = data['Year'].value_counts().sort_index()
print(publications_per_year)

Step 7: Visualize the Trends

Plot the publication trends over the years:

# Preparing the figure with a specified size
plt.figure(figsize=(12, 8))
# Plotting a line graph from the 'publications_per_year' data
publications_per_year.plot(kind='line', color='#5737f4', linewidth=2, marker='o', markersize=8)
# Adding a title to the plot with a larger font size for visibility
plt.title('Epilepsy Surgery Publications Over Years', fontsize=16)
# Setting the label for the x-axis with a specified font size
plt.xlabel('Year', fontsize=14)
# Setting the label for the y-axis with a specified font size
plt.ylabel('Number of Publications', fontsize=14)
# Adjusting the font size of the tick labels on both axes for readability
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
# Adding a grid to the plot for easier interpretation of data points
plt.grid(True)
# Displaying the final plot
plt.show()

Step 8: Visualizing Top Cited Papers

A bar chart will help us visualize which papers have received the most citations.

# Sorting the data to find the top cited papers
# 'Citation count' column is used to sort the papers
# 'head(10)' selects the top 10 papers
top_cited_papers = data.sort_values(by='Citation count', ascending=False).head(10)

# Function to split long titles into multiple lines
def split_title(title, max_length=50):
    """
    Splits a title into multiple lines if it exceeds max_length.
    This makes long titles more readable in the plot.
    """
    if len(title) <= max_length:
        return title  # Return title as is if it's short enough
    else:
        # Find space near max_length to split title
        split_index = title.rfind(' ', 0, max_length)
        if split_index == -1:  # If no space is found, split at max_length
            split_index = max_length
        # Recursive call to handle next part of the title
        return title[:split_index] + '\n' + split_title(title[split_index+1:], max_length)

# Apply the function to each title in the DataFrame
# This modifies the 'Title' column to have split titles
top_cited_papers['Title'] = top_cited_papers['Title'].apply(split_title)

# Creating the horizontal bar plot
plt.figure(figsize=(15, 12))  # Setting the size of the plot
plt.subplots_adjust(left=0.3, bottom=0.2) # Adjust margins to fit

# Plotting bars with horizontal orientation
bars = plt.barh(top_cited_papers['Title'], top_cited_papers['Citation count'], color='#5737f4', label='Citation Count')

# Adding text labels to each bar for citation count
for bar in bars:
    plt.text(bar.get_width(), bar.get_y() + bar.get_height() / 2, 
             f'{int(bar.get_width())}', # Citation count as label
             va='center') # Vertically center the label

# Setting the title and labels with adjusted font sizes
plt.title('Top Cited Epilepsy Surgery Publications', fontsize=16)
plt.xlabel('Citation Count', fontsize=14)
plt.ylabel('Title', fontsize=14)

# Setting the font size for tick labels
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.grid(axis='x')  # Adding gridlines for the x-axis
plt.legend()  # Adding a legend to the plot
plt.show()  # Displaying the plot

Conclusion:

In summary, this project has successfully demonstrated the foundational aspects of data analysis using Python. By skillfully applying tools like Pandas and Matplotlib, we've extracted significant insights from a real-world dataset, specifically focusing on epilepsy surgery publications. This exploration not only highlights Python's versatility in handling and visualizing complex data but also underscores the practical application of programming skills in real-life scenarios. For beginners and intermediate learners alike, this project serves as a valuable example of how Python can be leveraged to turn raw data into meaningful and actionable knowledge.

Resources:

Kung, J. Y. (2023). Elicit. The Journal of the Canadian Health Libraries Association, 44(1), 15–18. https://doi.org/10.29173/jchla29657
Elicit. (2023). Retrieved from https://elicit.com/
Ought. (2023). Elicit. Retrieved from https://ought.org/elicit
The Python Software Foundation. (2023). Welcome to Python.org. Retrieved from https://www.python.org/
Pandas. (2023). pandas - Python Data Analysis Library. Retrieved from https://pandas.pydata.org/

INFINIDEX