โก Quick Summary
This study investigates the impact of categorical data on machine learning model outputs in healthcare, emphasizing the importance of understanding the social construction of these data categories. By employing a mixed methods approach, the research reveals significant insights into how categorical data can affect model training and performance.
๐ Key Details
- ๐ Dataset: Brazilian dermatological dataset (PAD-UFES 20)
- ๐งฉ Features used: Categorical data such as gender, socioeconomic status, and skin color
- โ๏ธ Methodology: Mixed methods approach combining quantitative and qualitative analyses
- ๐ Key findings: Scattered effects of categorical data on model training across predictive classes
๐ Key Takeaways
- ๐ Categorical data plays a crucial role in training machine learning models in healthcare.
- ๐ก The study highlights the need for a mixed methods approach to assess data features.
- ๐ฉโ๐ฌ Insights from interviews with dataset authors provide context for data collection.
- ๐ Findings indicate that the utility of categorical data is context-dependent.
- ๐ค Caution is advised when using publicly available datasets without understanding their social context.
- ๐ The research emphasizes the importance of equitable model training for diverse populations.
- ๐ Study published in JMIR Medical Informatics.
๐ Background
In the realm of healthcare, the integration of machine learning has the potential to enhance decision-making and improve patient outcomes. However, the effectiveness of these models often hinges on the quality and characteristics of the data used for training. Categorical data, which includes variables such as gender and socioeconomic status, is frequently employed in model training. Yet, the implications of these data features remain underexplored, particularly in diverse populations where equitable healthcare is paramount.
๐๏ธ Study
This study aimed to delve into the effects of categorical data on machine learning model outputs by utilizing a mixed methods approach. The researchers focused on the PAD-UFES 20 dataset, which encompasses various categorical features relevant to dermatological conditions. The study combined a quantitative analysis of the dataset’s unique categorical features with qualitative insights gathered from interviews with the dataset authors, providing a comprehensive understanding of the data’s context and implications.
๐ Results
The quantitative analysis revealed scattered effects of including categorical data in model training across different predictive classes. The qualitative insights shed light on the data collection processes and the motivations behind the dataset’s publication, helping to explain the observed quantitative effects. This dual approach underscores the social constructedness of categorical data, highlighting that the definitions and contexts of these categories significantly influence their utility in machine learning applications.
๐ Impact and Implications
The findings of this study carry profound implications for the use of publicly available datasets in healthcare. By emphasizing the context dependency of categorical data, the research advocates for a more nuanced approach to data analysis and model training. This is particularly crucial in data-sparse areas, where the equitable application of machine learning models can significantly impact diverse populations. Understanding the social context of data can lead to more accurate and fair healthcare solutions.
๐ฎ Conclusion
This study highlights the critical importance of examining the social construction of categorical data in machine learning applications. By employing a mixed methods approach, researchers can better assess the utility of these data features and their implications for model training. As we continue to integrate machine learning into healthcare, it is essential to remain vigilant about the context and definitions of the data we use, ensuring that our models serve all populations equitably. The future of healthcare technology looks promising, but it requires careful consideration of the data that drives it.
๐ฌ Your comments
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The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.
Abstract
BACKGROUND: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models’ outputs. As a standard, categorical data, such as patients’ gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
OBJECTIVE: This study aimed to explore categorical data’s effects on machine learning model outputs, rooted the effects in the data collection and dataset publication processes, and proposed a mixed methods approach to examining datasets’ data categories before using them for machine learning training.
METHODS: Against the theoretical background of theย social construction of categories, we suggest a mixed methods approach to assess categorical data’s utility for machine learning model training. As an example, we applied our approach to a Brazilian dermatological dataset (Dermatological and Surgical Assistance Program at the Federal University of Espรญrito Santo [PAD-UFES] 20). We first present an exploratory, quantitative study that assesses the effects when including or excluding each of the unique categorical data features of the PAD-UFES 20 dataset for training a transformer-based model using a data fusion algorithm. We then pair our quantitative analysis with a qualitative examination of the data categories based on interviews with the dataset authors.
RESULTS: Our quantitative study suggests scattered effects of including categorical data for machine learning model training across predictive classes. Our qualitative analysis gives insights into how the categorical data were collected and why they were published, explaining some of the quantitative effects that we observed. Our findings highlight the social constructedness of categorical data in publicly available datasets, meaning that the data in a category heavily depend on both how these categories are defined by the dataset creators and the sociomedico context in which the data are collected. This reveals relevant limitations of using publicly available datasets in contexts different from those of the collection of their data.
CONCLUSIONS: We caution against using data features of publicly available datasets without reflection on the social construction and context dependency of their categorical data features, particularly in data-sparse areas. We conclude that social scientific, context-dependent analysis of available data features using both quantitative and qualitative methods is helpful in judging the utility of categorical data for the population for which a model is intended.
Author: [‘Willem T’, ‘Wollek A’, ‘Cheslerean-Boghiu T’, ‘Kenney M’, ‘Buyx A’]
Journal: JMIR Med Inform
Citation: Willem T, et al. The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets. The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets. 2025; 13:e59452. doi: 10.2196/59452