Weather map overlaid on skin, symbolizing bullous pemphigoid triggers.

Decoding Bullous Pemphigoid: How Environmental Factors Impact This Skin Condition

Lena Kashyap in Health & Wellness November 2025 • 4 min read.

"A Deep Dive into Time Series Data Mining and Its Revelations About Bullous Pemphigoid"

Bullous pemphigoid (BP) is a chronic autoimmune skin disease where the body's immune system mistakenly attacks healthy tissue, leading to blisters. Characterized by tense bullae on the skin, predominantly in areas like the lower abdomen and inner thighs, BP primarily affects older adults.

While the exact causes of bullous pemphigoid remain under investigation, environmental factors are suspected of playing a significant role in triggering or exacerbating the condition. Recognizing these factors could significantly improve prevention strategies and patient care.

Recent research published in the Journal of the Chinese Institute of Engineers explores the use of time series data mining to identify environmental influences on bullous pemphigoid. By analyzing extensive datasets, researchers aim to uncover patterns that link meteorological conditions with disease incidence, offering new insights into managing and potentially preventing BP.

Unveiling the Environmental Culprits: What Triggers Bullous Pemphigoid?

Weather map overlaid on skin, symbolizing bullous pemphigoid triggers.

Researchers utilized time series data mining to analyze the influences of environmental factors on bullous pemphigoid (BP). The data analyzed were from Taiwan’s National Health Insurance Research Database (NHIRD), which covers approximately 97% of the population. This extensive dataset included patient diagnoses and meteorological data, allowing for a comprehensive analysis.

Several statistical methods were employed to explore the connection between meteorological variables and the incidence of BP. The team utilized the Fourier-Gaussian Decomposition (FGD) algorithm, developed by their research group, to recognize seasonal incidence distributions of BP. The effectiveness of this method was compared with conventional time series data mining techniques to ensure robust results.

Data Collection: Retrospective collection of monthly BP records from 2000-2010.
Environmental Data: Integration of monthly meteorological data (temperature, humidity, precipitation, wind speed, sunshine) from the Taiwan Central Weather Bureau.
Statistical Tests: Application of Ljung-Box, Shapiro-Wilk, and Kuiper's tests to validate time series data.
Correlation Analysis: Used Pearson correlation coefficient to quantify relationships.
Fourier-Gaussian Decomposition (FGD): Novel algorithm to identify and analyze periodic components.

The research found a significant increase in monthly incident counts of BP in August. Correlation analysis revealed a strong positive correlation between BP incidence and high ambient temperatures. The FGD algorithm proved effective in extracting hidden patterns from the data, validating its usefulness compared to conventional methods.

Empowering Prevention: What's Next in BP Research?

This research highlights the potential for leveraging data mining techniques to uncover environmental factors influencing bullous pemphigoid. By understanding these patterns, healthcare professionals can develop targeted prevention strategies and provide more effective care for individuals at risk. Future research should explore the impact of air pollutants and other environmental variables, paving the way for a comprehensive understanding of BP triggers and improved patient outcomes.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1080/02533839.2018.1535282, Alternate LINK

Title: Using Methods Of Time Series Data Mining To Recognize The Influences Of Environmental Factors On Bullous Pemphigoid

Subject: General Engineering

Journal: Journal of the Chinese Institute of Engineers

Publisher: Informa UK Limited

Authors: Jian-Liang Lai, Yu-Ming Chang, Pin-Liang Chen, Lih-Ching Chou, Ding-Dar Lee, Meng-Han Yang

Published: 2018-11-17

Everything You Need To Know

What is Bullous Pemphigoid (BP) and what are its primary characteristics?

Bullous pemphigoid (BP) is a chronic autoimmune skin disease where the body's immune system erroneously targets healthy tissue, leading to the formation of blisters. These blisters, known as bullae, are typically tense and appear on the skin, often in areas such as the lower abdomen and inner thighs. This condition primarily affects older adults.

How are environmental factors related to Bullous Pemphigoid (BP), and why is this important?

Environmental factors are believed to significantly influence the onset or worsening of Bullous Pemphigoid (BP). Identifying these factors is crucial for developing preventive strategies and enhancing patient care. Recent research has utilized time series data mining techniques to explore the links between meteorological conditions and the incidence of BP, aiming to uncover patterns that can inform management and prevention strategies for the disease.

What specific environmental data was analyzed in the research, and what methods were used to analyze it?

The research analyzed monthly meteorological data from Taiwan's Central Weather Bureau, including temperature, humidity, precipitation, wind speed, and sunshine. Statistical methods such as Ljung-Box, Shapiro-Wilk, and Kuiper's tests were used to validate the time series data. Pearson correlation coefficient was employed for correlation analysis, and a novel algorithm, the Fourier-Gaussian Decomposition (FGD), was used to identify and analyze periodic components within the data. The FGD algorithm's effectiveness was compared with conventional time series data mining techniques.

What were the key findings of the time series data mining analysis regarding environmental influences on Bullous Pemphigoid (BP)?

The research revealed a significant increase in the monthly incidence of Bullous Pemphigoid (BP) in August. Correlation analysis showed a strong positive correlation between the incidence of BP and high ambient temperatures. The Fourier-Gaussian Decomposition (FGD) algorithm effectively extracted hidden patterns from the data, which validated its usefulness compared to conventional methods. This data confirms the link between higher temperatures and the occurence of BP.

How can the insights gained from this research on Bullous Pemphigoid (BP) be used to improve patient outcomes and future research directions?

The study highlights the potential of leveraging data mining techniques to identify environmental factors that influence Bullous Pemphigoid (BP). This understanding allows healthcare professionals to create targeted prevention strategies and provide more effective care for those at risk. Future research should focus on examining the impact of other environmental variables, such as air pollutants, to develop a more comprehensive understanding of BP triggers. This broader approach can pave the way for improved patient outcomes and more effective management of the condition.