Statistical Data Analysis using GPT3: An Overview

Sharma, Abhishek; Devalia, Disha; Almeida, Wilfred; Patil, Harshali; Mishra, Aniket

doi:10.1109/ibssc56953.2022.10037383

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…GPT-based technologies allow modern researchers to analyze their data with the help of prompts. A researcher can use GPT, LLM, and associated technologies for data analysis and critical research by leveraging their ability to perform complex textual analysis and pattern recognition in large datasets, aiding in tasks like detecting nuanced patterns in financial texts [23,24,75,76]. These models also excel at solving intricate problems, such as those in discrete mathematics, showing significant improvements in advanced versions like GPT-4.…”

Section: Research Analysismentioning

confidence: 99%

“…The study includes case studies and comparisons of GPT-3, Codex, and ChatGPT performances in generating visualizations from various types of queries, highlighting their potential in rendering visualizations from natural language, even when queries are ambiguous or poorly specified. The study in [24] explores the application of GPT-3 for statistical data analysis. It proposes a method for analyzing large datasets using GPT-3 to predict insights from calculated statistics.…”

Section: Research Analysismentioning

confidence: 99%

“…These selected studies specifically address three pivotal research questions, delineated as follows: In Figure 2, the systematic literature review adeptly highlights the central focus on data augmentation with GPT, encapsulating 45 highly pertinent scholarly contributions. Beyond this primary focus, the study delves into the proactive role of GPT in facilitating critical analysis of research data [22][23][24][25] and shaping research design [26][27][28]. Significantly advancing the scholarly discourse, this study pioneers the development of a comprehensive classification framework for "GPT's use of research data", marking a seminal contribution.…”

Section: Introductionmentioning

confidence: 99%

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Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Sufi

2024

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GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits.

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Section: Research Analysismentioning

confidence: 99%

Section: Research Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Sufi

2024

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“…Data Analysis: As demonstrated in [21][22][23][24][25][26], GPT assists in analyzing research data and generating critical insights. Within the medical domain, research works in [11,19] demonstrate AI's utility in analyzing complex datasets, including patient outcomes and health message effectiveness, enhancing predictive modeling and comprehension of medical data.…”

mentioning

confidence: 99%

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Sufi

2024

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This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts.

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