Abstract
Various predictive frameworks have evolved over the last decade to facilitate the efficient diagnosis of critical diseases in the healthcare sector. Some have been commercialized, while others are still in the research and development stage. An effective early predictive principle must provide more accurate outcomes in complex clinical data and various challenging environments. The open-source database system medical information mart for intensive care (MIMIC) simplifies all of the attributes required in predictive analysis in this regard. This database contains clinical and non-clinical information on a patient’s stay at a healthcare facility, gathered during their duration of stay. Regardless of the number of focused research attempts employing the MIMIC III database, a simplified and cost-effective computational technique for developing the early analysis of critical problems has not yet been found. As a result, the proposed study provides a novel and cost-effective machine learning framework that evolves into a novel feature engineering methodology using the MIMIC III dataset. The core idea is to forecast the risk associated with a patient’s clinical outcome. The proposed study focused on the diagnosis and clinical procedures and found distinct variants of independent predictors from the MIMIC III database and ICD-9 code. The proposed logic is scripted in Python, and the outcomes of three common machine learning schemes, namely Artificial Neural Networks, K-Nearest Neighbors, and Logistic Regression, have been evaluated. Artificial Neural Networks outperform alternative machine learning techniques when accuracy is taken into account as the primary performance parameter over the MIMIC III dataset.
Highlights
The use of science in the healthcare industry has transformed the approach to understanding diseases, treatment, patient care, and hospital management planning
Such admission can either be into one specific intensive care units (ICUs) or in multiple ICUs to get diagonalized with the specific medical devices
This paper proposes a prospective health care model-based framework on the medical information mart for intensive care (MIMIC)-III dataset for predicting the probability of readmission of the patient based on the risk associated with a particular procedure applied before discharging the patient from the hospital
Summary
The use of science in the healthcare industry has transformed the approach to understanding diseases, treatment, patient care, and hospital management planning. This paper proposes a prospective health care model-based framework on the MIMIC-III dataset for predicting the probability of readmission of the patient based on the risk associated with a particular procedure applied before discharging the patient from the hospital. The main highlights of this research paper are the challenges of obtaining the global feature vector and identifying the dependencies of variables with potential correlation on the vast and varied MIMIC-III dataset for designing an ML-based predictive model to visualize the insights of the hidden knowledge for a specific patient with their procedure and associated risk. Table set: 1, 5, 12, 14, 21, 24, 25 MIMIC-III clinical dataset file descriptions (refer TableI) are used as input in the feature engineering process to transform and prepare a dataset for the machine learning model to predict the possibility of risk after undergoing a specific procedure of ICD-9 codes.
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