Analysis of the Quality of Patient Treatment Data in MIMIC-IV Using Alpha, Heuristic, and Inductive Miner

MIMIC-IV (Medical Information Mart for Intensive Care IV), is a medical dataset used for research in the fields of medicine and health computer science. This dataset contains health information collected from intensive care units (ICUs) at the Beth Israel Deaconess Medical Center in Boston, MA. This research uses process mining to analyze data quality of patient treatment in the MIMIC-IV dataset using the Alpha Miner, Heuristic Miner, and Inductive Miner algorithms. It begins with planning and justification, followed by database reconstruction, data quality assessment, and data extraction, leading to the development of a control flow model. Subsequently, conformance checking is performed, and the study concludes with an evaluation of the results. It is expected that the results of this study will provide a better understanding of the quality of patient care process data in the MIMIC-IV dataset and a positive contribution to developing more effective health services.

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Publication Details

Journal
Jurnal Indonesia Sosial Teknologi
Published
2025-01-29
DOI
https://doi.org/10.59141/jist.v6i1.2232
Primary Topic
Artificial Intelligence in Healthcare
Type
article
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article

Analysis of the Quality of Patient Treatment Data in MIMIC-IV Using Alpha, Heuristic, and Inductive Miner

Angelina Prima Kurniati, Muhammad Reynaldi Mujantara
Jurnal Indonesia Sosial Teknologi
Artificial Intelligence in Healthcare
article

Analysis of the Quality of Patient Treatment Data in MIMIC-IV Using Alpha, Heuristic, and Inductive Miner

Angelina Prima Kurniati, Muhammad Reynaldi Mujantara
article en

Abstract

MIMIC-IV (Medical Information Mart for Intensive Care IV), is a medical dataset used for research in the fields of medicine and health computer science. This dataset contains health information collected from intensive care units (ICUs) at the Beth Israel Deaconess Medical Center in Boston, MA. This research uses process mining to analyze data quality of patient treatment in the MIMIC-IV dataset using the Alpha Miner, Heuristic Miner, and Inductive Miner algorithms. It begins with planning and justification, followed by database reconstruction, data quality assessment, and data extraction, leading to the development of a control flow model. Subsequently, conformance checking is performed, and the study concludes with an evaluation of the results. It is expected that the results of this study will provide a better understanding of the quality of patient care process data in the MIMIC-IV dataset and a positive contribution to developing more effective health services.

Jurnal Indonesia Sosial TeknologiVol. 6(1)
Telkom University (ID)
Openalex Percentile: Top 99%
Artificial Intelligence in Healthcare
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Analysis of the Quality of Patient Treatment Data in MIMIC-IV Using Alpha, Heuristic, and Inductive Miner — Angelina Prima Kurniati, Muhammad Reynaldi Mujantara · Jurnal Indonesia Sosial Teknologi (2025) | TGRS Research Map | TGRS