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Sapnita Sapnita
Universitas Putra Abadi Langkat, Indonesia
Computer Science & IT

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Improving longitudinal health data analysis with stochastic models for predicting disease trajectories and optimizing treatment strategies Nur Hasanah; Nanarita Tarigan; Siskawati Amri; Siti Saodah; Sapnita Sapnita
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i2.141

Abstract

Longitudinal health data analysis helps diagnose and treat disease. Traditional deterministic models fail to represent longitudinal data's unpredictability and uncertainty, limiting their forecast accuracy and decision-making capacities. This research improves Longitudinal Health Data Analysis by adding stochastic models for disease trajectories and therapy optimization. The research begins with a stochastic model that accounts for the complicated dynamics of illness progression and therapy responses. This model captures individual variability and probability outcomes using patient-specific factors, features, and treatment information. Numerical examples demonstrate the model's practicality. The numerical example shows that the stochastic model may forecast illness trajectories and optimize treatment choices. The model predicts illness development probabilistically, helping understand disease dynamics and identify high-risk patients. Simulating and probabilistically estimating therapeutic interventions optimizes treatment options. Personalized therapy decision-making improves patient outcomes. Longitudinal Health Data Analysis should use stochastic models, the study suggests. These models improve disease prediction, therapy optimization, and personalized healthcare decision-making by capturing variability and uncertainty. Advanced modeling methodologies and real-world data validation are next. The research could change illness management and clinical care
Optimizing maternal and child health services with operations research techniques approach Fitri Andriani; Setia Sihombing; Sapnita Sapnita; Tri Suci Dewiwati
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 2 (2023): June : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i2.144

Abstract

Operations research is used to optimize mother and child health services through appointment scheduling and resource allocation. Public health is reflected in maternal and child health. Maternal and infant death rates remain a global issue despite medical advances. These issues stem from mother and child health service inefficiencies and poor care. This study uses operations research to improve healthcare delivery and patient outcomes.The study begins by identifying maternal and child health service issues such high wait times, insufficient resource allocation, and poor appointment scheduling. It then creates a mathematical formulation model that encompasses healthcare system intricacies including patient flow, resource use, and appointment scheduling. Linear programming, simulation, queuing theory, and data analytics enhance patient scheduling for varying medical urgency levels and time needs. A numerical illustration illustrates the mathematical formulation model. Patient wait times, resource allocation, and service efficiency improved significantly. Early time slots favor patients with higher medical urgency, ensuring timely healthcare treatments. Optimized resource use prevents overcrowding and ensures appointment equity. Stakeholder engagement and collaboration with healthcare practitioners, administrators, policymakers, and others are stressed throughout the study process. Key stakeholders can adjust proposed solutions to mother and child health service requirements and obstacles, improving acceptance and feasibility. This research advances operations research-based mother and child health service optimization. Data-driven decision-making and creative approaches aim to improve mother and child health service delivery, resource usage, and patient outcomes. Global mother and child health initiatives and sustainable development goals might benefit from evidence-based policy decisions and healthcare management solutions.
Co-Authors Fitri Andriani Nanarita Tarigan Nur Hasanah Setia Sihombing Siskawati Amri Siti Saodah Tri Suci Dewiwati