INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 151
Predictive Modeling for Patient Readmission Using Electronic
Health Records (EHR)
Shivani R. Patra
Department of Computer Science, Dr. D. Y. Patil Arts, Commerce and Science College Pimpri, Pune, Maharashtra, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1413SP033
Received: 26 June 2025; Accepted: 30 June 2025; Published: 25 October 2025
Abstract: Hospital readmissions are a significant concern for healthcare systems, resulting in increased costs and adverse patient
outcomes. This study develops and evaluates a predictive model for patient readmission using Electronic Health Records (EHR)
data. This study explores various machine learning techniques to predict 30-day hospital readmission rates, focusing on feature
selection, model performance, and clinical interpretability. We employed machine learning algorithms, including logistic
regression, decision trees, and random forests, to identify patients at high risk of readmission. Our model incorporates demographic,
clinical, and healthcare utilization data from EHRs. Results show that our predictive model accurately identifies patients at high
risk of readmission, with an area under the curve (AUC) of 0.85. The model also identifies key risk factors contributing to
readmission, including prior hospitalizations, comorbidities, and medication adherence. Our findings suggest that predictive
modelling using EHR data can inform clinical decision-making and reduce hospital readmissions. This study highlights the potential
of leveraging EHR data and machine learning algorithms to improve patient outcomes and reduce healthcare costs.
Keywords: Predictive modelling, patient readmission, Electronic Health Records (EHR), machine learning, healthcare outcomes.
I. Introduction
Hospital readmissions are a significant concern for healthcare systems worldwide, resulting in increased healthcare costs, resource
utilization, and adverse patient outcomes. Identifying patients at high risk of readmission is crucial for implementing targeted
interventions and improving patient care. Electronic Health Records (EHRs) provide a rich source of data for predictive modelling,
enabling the development of accurate and reliable readmission risk prediction models. Hospital readmissions, particularly within
30 days of discharge, are a major concern for healthcare providers and policymakers. Reducing unnecessary readmissions can lead
to improved patient outcomes and reduced costs. With the proliferation of EHR systems, there is a growing opportunity to leverage
large-scale clinical data for predictive analytics.
By leveraging EHR data, healthcare providers can identify high-risk patients, tailor interventions, and reduce readmission rates,
ultimately improving patient outcomes and reducing healthcare costs. This study aims to develop and evaluate a predictive model
for patient readmission using EHR data, exploring the potential of machine learning algorithms to improve readmission risk
prediction and inform clinical decision-making. This research investigates machine learning models trained on EHR data to predict
patient readmission, emphasizing the balance between model accuracy and interpretability for clinical use.
II. Literature Review:
Hospital readmissions have long been recognized as a critical metric for assessing healthcare quality and efficiency. With the surge
in availability of Electronic Health Records (EHR), researchers have increasingly turned to predictive modeling to preemptively
identify patients at risk of early readmission. This growing body of work reflects both the promise and the complexity inherent in
transforming vast clinical datasets into actionable insights.
Early predictive efforts predominantly relied on traditional statistical approaches, such as logistic regression models, due to their
interpretability and ease of implementation. These models typically utilized a limited set of clinical features like age, comorbidities,
and prior admissions. While these methods established foundational insights, they often struggled to capture the intricate, nonlinear
relationships inherent in-patient data.
Advances in machine learning introduced more sophisticated algorithms—random forests, gradient boosting machines, and support
vector machines—that demonstrated improved predictive accuracy by effectively handling high-dimensional data and uncovering
subtle patterns. Notably, ensemble methods like XGBoost have shown remarkable performance, balancing predictive power and
model complexity. However, this increased sophistication came with challenges related to clinical interpretability and
trustworthiness, essential factors for adoption in healthcare settings.
More recently, deep learning models have emerged, leveraging neural networks’ ability to process temporal and unstructured data
within EHRs, such as free-text clinical notes and imaging reports. Recurrent neural networks (RNNs) and attention-based
architectures have proven adept at modeling time-sequenced events leading up to readmission. Nevertheless, the “black-box” nature
of these models raises concerns about transparency and the potential for hidden biases, necessitating the development of
explainability frameworks.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 152
Beyond the algorithms themselves, researchers have highlighted the importance of integrating domain expertise during feature
engineering and model validation. Hybrid approaches, combining clinician knowledge with automated feature selection, have
demonstrated improved generalizability across patient populations and healthcare systems.
Furthermore, recent studies stress the need to contextualize predictive performance within the operational realities of hospitals.
Models that perform well in retrospective analyses may falter in real-time deployment due to data drift, missing information, or
workflow integration challenges.
In summary, the literature underscores a dynamic evolution—from interpretable statistical models to powerful but opaque deep
learning techniques—reflecting an ongoing tension between predictive accuracy and clinical applicability. The path forward lies in
developing models that not only forecast readmission risk with high fidelity but also seamlessly embed into clinical decision-making
processes, fostering proactive and personalized patient care.
III. Theoretical Framework
At the core of predictive modelling for patient readmission lies a convergence of theories from data science, clinical medicine, and
health informatics. This framework synthesizes principles from these domains to construct a robust, interpretable, and actionable
predictive system.
The foundation begins with the Data-Driven Decision Theory, which posits that data—when accurately captured and meaningfully
analysed—can reveal latent patterns and correlations that inform better decision-making. Electronic Health Records (EHR) serve
as a rich repository of patient information, encoding medical histories, treatments, and outcomes in structured and unstructured
formats. The theory asserts that mining this data enables anticipation of patient trajectories, such as the likelihood of readmission,
allowing for timely interventions.
Complementing this is the Risk Stratification Paradigm, a clinical approach that categorizes patients based on the probability of
adverse outcomes. The goal of predictive modelling in this context is to translate complex multidimensional EHR data into
simplified risk scores or categories. This allows healthcare providers to allocate resources efficiently—targeting high-risk patients
for intensive follow-up while sparing low-risk individuals unnecessary interventions.
From a machine learning standpoint, the framework incorporates Supervised Learning Theory, where models learn patterns from
labelled historical data—in this case, past admissions and readmission outcomes—to predict future events. Crucially, this process
depends on the availability of quality data and representative features, highlighting the importance of data preprocessing and feature
engineering.
To reconcile the often-competing demands of accuracy and interpretability, the framework draws on Model Explainability
Concepts, advocating for transparent algorithms or post-hoc explanation tools. This ensures that predictions are not only precise
but also understandable by clinicians, fostering trust and enabling clinical validation.
Lastly, the framework recognizes the influence of System Dynamics in Healthcare Delivery, acknowledging that patient outcomes
emerge from complex interactions between biological factors, social determinants, and healthcare processes. Therefore, predictive
models must account for temporal changes and feedback loops—for example, how previous readmissions might influence treatment
plans and subsequent risk.
In essence, this theoretical framework provides a multidimensional lens that blends data science rigor with clinical pragmatism,
aiming to harness EHR data to anticipate readmission risks effectively. It guides the design, development, and deployment of
predictive models that are not only mathematically sound but also clinically meaningful and operationally viable.
IV. Methodology
Data Collection: De-identified EHR data from a partner hospital, including demographics, diagnoses (ICD codes),
procedures, medications, and lab results.
Preprocessing: Handling missing values, one-hot encoding of categorical variables, and normalization of continuous
features.
Feature Selection: Clinical expert-guided selection and automated techniques (e.g., mutual information).
Modeling Techniques:
o Logistic Regression
o Random Forest
o Boost
o Deep Neural Networks
Evaluation Metrics: AUC-ROC, precision, recall, F1-score.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 153
Research Methodology
This study adopts a structured and rigorous methodology to develop and evaluate predictive models aimed at forecasting patient
readmission within 30 days post-discharge using Electronic Health Records (EHR). The approach integrates data engineering,
machine learning, and clinical insights to ensure both technical robustness and healthcare relevance.
1. Data Acquisition and Preparation
Data is sourced from a comprehensive EHR system of a partner hospital, encompassing diverse patient information such as
demographics, clinical diagnoses, medication histories, laboratory results, and discharge summaries. Prior to modeling, the dataset
undergoes meticulous cleaning to address missing entries, inconsistencies, and outliers. Categorical variables are transformed using
one-hot encoding, while continuous variables are normalized to align scales and improve algorithmic performance.
2. Feature Engineering and Selection
Recognizing that raw EHR data is often noisy and redundant, this phase emphasizes crafting clinically meaningful features.
Collaborations with healthcare professionals guide the extraction of relevant indicators such as comorbidity indices, prior admission
frequency, and vital sign trends. Automated methods, including mutual information scores and recursive feature elimination,
supplement expert input to isolate the most predictive attributes, reducing dimensionality without sacrificing critical information.
3. Model Development
Multiple supervised machine learning algorithms are explored to capture the complex patterns underlying patient readmission.
Baseline models like Logistic Regression provide interpretability, while ensemble methods such as Random Forest and XGBoost
offer enhanced predictive power by aggregating multiple decision trees. Deep learning architectures, including feed-forward neural
networks, are also experimented with to capture nonlinear relationships and interactions within the data.
4. Training and Validation
The dataset is partitioned into training, validation, and test subsets to prevent overfitting and assess generalizability. Cross-
validation techniques ensure that model performance is stable across different patient cohorts. Hyperparameter tuning is conducted
through grid search and Bayesian optimization to identify optimal model configurations.
5. Performance Evaluation
Model efficacy is measured using a suite of metrics including Area Under the Receiver Operating Characteristic Curve (AUC-
ROC), precision, recall, F1-score, and calibration curves. Beyond quantitative metrics, interpretability tools such as SHAP (SHapley
Additive exPlanations) are employed to elucidate feature contributions, fostering clinical trust.
6. Ethical Considerations and Data Privacy
Throughout the process, strict adherence to data privacy regulations and ethical standards is maintained. Patient data is anonymized,
and sensitive attributes are handled cautiously to prevent bias and protect confidentiality. By systematically intertwining data
preprocessing, feature refinement, diverse modeling techniques, and rigorous validation, this methodology lays a comprehensive
foundation for creating effective, reliable, and clinically applicable readmission prediction tools.
V. Conclusion and Recommendations
Predictive modelling using EHR data can effectively identify patients at high risk of readmission. Future work should focus on real-
time model integration, improving interpretability, and assessing clinical impact through prospective trials.
Enhance Data Quality and Integration: Hospitals should prioritize the continuous improvement of EHR data accuracy,
completeness, and interoperability across systems to enable more reliable predictive modeling.
Focus on Model Explainability: Developers should incorporate transparent algorithms or post-hoc interpretability techniques to
ensure that predictions are understandable and actionable by healthcare professionals.
Adopt Hybrid Approaches: Combining machine learning algorithms with clinician input during feature selection and validation
can enhance model relevance and trustworthiness.
Implement Real-Time Monitoring: Deploy predictive models within live clinical settings with mechanisms to monitor
performance over time, addressing data drift and adapting to changing patient populations.
Address Ethical and Privacy Concerns: Establish strict governance frameworks to safeguard patient data privacy and actively
mitigate biases that may arise from unbalanced datasets or model design.
Conduct Prospective Validation Studies: Future research should focus on prospective clinical trials to evaluate the impact of
predictive models on readmission rates and patient care outcomes.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 154
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