386 research outputs found
Transfer Learning using Computational Intelligence: A Survey
Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..
Simulation and Modeling for Improving Access to Care for Underserved Populations
Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs)
in Indiana, constructed effective outpatient appointment scheduling systems by
determining care needs of CHC patients, designing an infrastructure for meaningful use of
patient health records and clinic operational data, and developing prediction and simulation
models for improving access to care for underserved populations. The aims of this study
are 1) redesigning appointment scheduling templates based on patient characteristics,
diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive
modeling to improve understanding the complexity of appointment adherence in
underserved populations; and 3) developing simulation models with complex data to guide
operational decision-making in community health centers. This research addresses its aims
by applying a multi-method approach from different disciplines, such as statistics,
industrial engineering, computer science, health informatics, and social sciences. First, a
novel method was developed to use Electronic Health Record (EHR) data for better
understanding appointment needs of the target populations based on their characteristics
and reasons for seeking health, which helped simplify, improve, and redesign current
appointment type and duration models. Second, comprehensive and informative predictive
models were developed to better understand appointment non-adherence in community
health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network
found factors contributing to patient no-show. Predictors of appointment non-adherence
might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems
in CHCs, and necessary steps to extract information for simulation modeling of scheduling
systems in CHCs are described. Agent-Based Models were built in AnyLogic to test
different scenarios of scheduling methods, and to identify how these scenarios could impact
clinic access performance. This research potentially improves well-being of and care
quality and timeliness for uninsured, underinsured, and underserved patients, and it helps
clinics predict appointment no-shows and ensures scheduling systems are capable of
properly meeting the populations’ care needs.2021-12-2
Tuberculosis Prediction by Machine Learning Techniques
Tuberculosis is one of the top reasons of death all over the planet. Mycobacterium tuberculosis, bacteria that infects the lungs, is what causes it. For professionals working in the medical field, accurately identifying and timely predicting tuberculosis are major challenges. The course of treatment also varies from patient to patient since occasionally a patient develops drug resistance. Doctors will be given algorithmic support while using machine learning to help them diagnose, treat patients appropriately, and make quicker and better judgments. This paper discusses the many tuberculosis causes and symptoms as well as how accurate and fast prediction and diagnostic investigations have been carried out in recent years with the aid of machine learning (ML) technique
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