Spectrum of Engineering and Technology Applications Journal (SETAJ)

The Future of Personalized Healthcare: AI and Machine Learning Approaches to Predicting Patient Response

Bilal Athar — Tue, 31 Dec 2024 00:00:00 +0000

The purpose of individualized health care is to provide the patient with the specific healthcare care by being extremely precise when predicting the response of the patient. The present research examines the potential of real-world artificial intelligence (AI) and machine learning (ML) to enter into predicting the outcomes of patients more effectively. The other method makes use of state of the art ML-based models that can be used to make predictions based on volumes of data that can be fed in an AI form in regards to data on the nature and history of patients and genetic data. The paper will analyze the recent advancements regarding the sphere of AI and ML in order to elaborate individual care packages, as well as, predictive modeling of the clinical environment. The most crucial ones are the evaluation of the relevance and applicability of the existing models, one of which is the decision tree, the neural networks and a support vector machine, in analyzing the response of the patient. In the methodology of the research, it is possible to assume comparing various models of AI, and the purpose behind it is to disprove and test them; therefore, to design the solution, data sets of medical information published publicly should be utilized. On the basis of the findings, it is concluded that ML models, in particular, deep learning methods produce very good results and are vastly superior to all the conventional methods in terms of the accuracy level and consideration of the needs of each of the patients in question. Such consequences of the implications of the findings on the future practice of healthcare are considered at the conclusion of the end of the study where the statement of the need of inserting the transparent and interpretable models into the clinical decision-making process is formulated.

Assessing the Role of Machine Learning in Predicting Hospital Readmissions: A Study Using Real-World EHR Data

Mohsin Wazir — Tue, 31 Dec 2024 00:00:00 +0000

This article assesses the role of machine learning in predicting hospital readmissions. One of the tasks that concern not only healthcare but also are considered very necessary is hospital readmission prediction that should be utilized to recognize vulnerable patients and ultimately improve the clinical outcomes. Machine learning (ML) models exhibit the potential to automate this process by taking data in the form of Electronic Health Records (EHR). The purpose of the study is to learn what the purpose of ML is in predicting hospital readmission and how the usage of real world EHR data can be applied so as to generate the predictive model. The research exploits various ML algorithms including the Random Forest (RF), Support Vector Machine (SVM), and the Neural Networks (NN) in the quest to interpret attributes on the basis of the patient demographics, medical history, and hospitalization records. It is indicated in the results that the model such as Random Forest of the ensemble method was more accurate and interpretable than the other models and possessed an AUC of 0.82. The trends suggest that one can utilise the ML models in clinical practices to foretell the readmissions at the initial phase. Nevertheless, quality of data, lack of interpretability of a model and restriction of generalizability towards diverse populations still exist. Future research ought to be focusing within the determination of upsurge in model openness and gains in the production of hybrid models that include the domain learning as well as clinical knowledge. The paper would contribute to the body of literature on articles on the AI in healthcare as it would demonstrate the operation of the ML techniques in reality in the story to predict the hospital readmission.

Leveraging Electronic Health Records for Predictive Analytics: Enhancing Patient Care with AI-Driven Insights

Msaud Afridi — Tue, 31 Dec 2024 00:00:00 +0000

Using Electronic Health Records (EHRs) in combination with predictive analytics, Artificial Intelligence (AI) has a chance of restructuring the existing method of delivering care in a fundamental way. This paper explores the results of AI in EHRs and the extent to which the same may promote enhancements in the care delivery process through making predictions about overall health, identifying vulnerable patients, and facilitating clinical decision-making. The articles provide an overview of the current strategies to use EHR data to perform predictive modeling and the lack of progress regarding the adoption of AI technology in the healthcare field. Training both the Machine Learning models (such as Random Forest and Neural Networks) and the decision tree, this paper presents a framework on how to make the prediction made through the analysis of EHR data more accurate and interpretable. Based on the results, AI models have the promise to improve the accuracy of the predictive models of chronic disease, readmission and patient deterioration significantly that can be used to achieve an optimal resource allocation process and intervene early. The end of the paper is completed by discussing the data privacy, model transparency, and integration with the existing healthcare systems. Among the consequences that have a significant impact on future research, one may point to the idea to improve the interpretability of AI models and consider ethical aspects of using data related to patients.

Integrating EHR Data with AI Algorithms for Real-Time Patient Outcome Predictions in Hospital Settings

Shehryar Ali Chatta — Tue, 31 Dec 2024 00:00:00 +0000

EHR can be embedded into an artificial intelligence (AI) formula, and this opens a revolutionary prospect to healthcare systems of attending to patients in a superior way. The research goal is to develop and integrate AI models that will be applied to predict the outcomes of patients at their bedside, using EHRs data. This research is important because it tries to solve the issues of predictive accuracies and data use in a hospital where time is essential in making certain decisions. The goals would be to use the AI methods and, in particular, machine learning algorithms to process the EHR data and deliver a real-time assessment of the patient outcomes, which can be their disease evolution, readmission rates, responses to treatment, etc. Prior works have already indicated encouraging data when applied to similar activities through machine learning models (Smith et al., 2019; Liu et al., 2020). The given paper discusses combining a range of AI models, random forests, support vector machines, and deep learning models to find the best predictors of patient outcomes. Our findings lead to the assumption according to which the proposed multifaceted AI solution enhances the forecasting power of the EHR data and depicts a higher degree of precision and credibility as compared to the traditional methods. The results also reflect the applied way of clinical decision-making, management of patients, and operation of hospitals according to real-time prediction. The paper comes to the conclusion that additional optimization should be undertaken and more AI-driven solutions should be deployed to hospital settings, particularly related to the problem of missing data and unstructured data.

Evaluating the Impact of Machine Learning Models in Forecasting Patient Outcomes: An EHR-Based Study

Zille Huma Rani — Tue, 31 Dec 2024 00:00:00 +0000

Machine learning (ML) as a method of procedures has transformed healthcare especially in the outcomes forecasting process of patients. EHRs contain huge volumes of patient-related information and hence, possible opportunity exists to administer ML models to perform predictive analysis. The study is concerned with the application of machine learning to forecast the destiny of patients in accordance with the EHR data. The analysis will involve a comparison of various algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN) in order to determine the fate of the patients in terms of disease advance and risk of readmission. The tactics that were used in the current investigation may be listed as feature selection, data preprocessing, application of the ML algorithms to a publicly available EHR dataset. The accuracy of performance measures is based on the performance of the models, precision recall, and F1-score, upon which they are tested. Based on the findings, although neural models show that this neural model gives high accuracy rates, it is observed that they are likely to perform better in clinical use applications compared to Random Forest models due to their probability of interpretability. The paper helps in the advancement of knowledge on the application of machine learning in healthcare which would culminate in better patient care and decision making. In the future, more advanced methods of deep learning are to be applied and the transparency of the model is to be enhanced.