Yazar "Delen, D." seçeneğine göre listele

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    The Interrelationships between the length of stay, readmission, and post-acute care referral in cardiac surgery patients
    (Elsevier Inc., 2022) Sultana, I.; Erraguntla, M.; Kum, H.-C.; Delen, D.; Lawley, M.
    Prolonged hospital stays, and readmission contribute to substantial healthcare cost. Hence, an assessment of the optimal inpatient length of stay (LOS) associated with lower readmission rate is important for healthcare providers. Post-acute care (PAC) facilities have promising potential to shorten the LOS; however, currently their influence on overall patient outcomes is not well understood. The primary goal of this study is to highlight the interrelated risk factors of LOS and readmission for cardiac patients. The study also examines the influence of PAC referral on LOS and readmission. In this paper, a cohort of 13,982 Coronary Artery Bypass Graft (CABG) and Valve Replacement (VR) patients from 49 hospitals in the U.S. were analyzed with respect to the association of healthcare delivery, demographics, PAC referral, and clinical conditions with LOS and readmission. A generalized linear mixed model and multinomial logistic regression model were developed to evaluate the readmission and LOS associative risk factors, respectively. Referral to PAC was included as a vital predictor in both models to examine its impact on optimal LOS and improving patient outcomes. The results indicate a non-uniform care distribution across census divisions and an inverse relationship between LOS and readmissions. The analytics showed that higher LOS patients were more often referred to PAC and yet they were more prone to readmission except for the patients who were referred to Long Term Care (LTC). This study identifies the effects of healthcare delivery, demographic, comorbidity, and PAC referral factors on readmission and LOS, so that personalized acute and post-acute care coordination can be achieved to improve overall patient outcomes. Interventions such as treating CABG and VR patients with multiple comorbidities for longer time in acute hospitals and increasing LTC referral can result in significant improvement. © 2022 The Author(s)
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    A machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitude
    (Elsevier Inc., 2022) Agrawal, R.K.; Sewani, R.R.; Delen, D.; Benjamin, B.
    According to the World Health Organization, Heart disease is the number one killer of humans, with coronary heart disease (CHD) being the most common type of heart disease. CHD leads to myocardial ischemia (MI) or infarction. Several clinical tests are available to assist physicians in diagnosing MI or infarcted (unhealthy) patients. However, diagnostic tests can be costly, invasive, and unreliable in identifying patients with declining coronary health conditions. This study investigated the application of Machine Learning (ML) techniques on the Vector Magnitude (VM) data of heart signals generated via Vectorcardiography (VCG) to classify unhealthy patients from healthy patients. Patients with MI, a CHD, are identified as ill patients. Three machine-learning classification techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), were applied to classify healthy and unhealthy (MI) patients. The heart signal dataset was acquired from the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic electrocardiogram (ECG) Database. A 10-fold cross-validation sampling method was used to improve the predictability of the sample. Results from ML techniques produced high classification sensitivity, specificity, and accuracy. ML analysis findings indicated that DT is the best predictor for classification accuracy, followed by SVM and ANN. The future study goal is to expand the study with forward-looking data and the right sample size for clinical validity and support the high accuracy results. © 2022 The Author(s)
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    A machine learning decision support system for determining the primary factors impacting cancer survival and their temporal effect
    (Elsevier Inc., 2023) Dag, A.Z.; Johnson, M.; Kibis, E.; Simsek, S.; Cankaya, B.; Delen, D.
    It is critical for healthcare providers to accurately determine lung cancer patients' prognostics and develop customized treatment plans. However, lung cancer has proven to be a complex disease, and every patient responds differently to treatment options, making survivability predictions highly challenging. This study proposes a holistic machine learning model that can assist healthcare providers in predicting the temporal effects of lung cancer-related factors on one-, five-, and ten-year survival rates. Variable selection algorithms such as genetic algorithm (GA) and Baruta are employed along with data balancing methods to achieve parsimonious models for survival prediction. Classification results are obtained through logistic regression and extreme gradient boosting algorithms followed by an information fusion technique to combine the classification results and identify the temporal effects of lung cancer variables over time. Results demonstrate that the prediction power of the classification models improved as the survival period increased. The models trained using the GA and intersection variable sets generated better average prediction scores. The study contributes to the cancer literature by analyzing the varying temporal impacts of lung cancer variables over varying time periods. Medical professionals can use these findings to understand better the longitudinal characteristics of lung cancer patients’ survival indicators. © 2023 The Authors
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    A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration
    (Elsevier Inc., 2024) Abdulrashid, I.; Delen, D.; Usman, B.; Uzochukwu, M.I.; Ahmed, I.
    Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient's body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization. © 2024 The Authors
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    Social capital and organizational performance: The mediating role of innovation activities and intellectual capital
    (Elsevier Inc., 2022) Ozgun, A.H.; Tarim, M.; Delen, D.; Zaim, S.
    While the positive influence of intellectual capital on innovation is well-established in the extant literature, research on how innovation activities affect intellectual capital is relatively scarce. Moreover, even though there is ample research showing the positive relationship between social capital and organizational performance, its significance is generally underappreciated by practitioners. This paper aims to contribute to the literature by investigating the influence of innovation activities on the depth of intellectual capital and the role they play in the relationship of social capital and organizational performance, using Turkish public hospitals as an exemplary application case. We argue that the activities carried out in these institutions during the innovation implementation process contribute to intellectual capital internally, with positive impacts on organizational performance. We hypothesize that social capital plays a vital role in this relationship by enhancing social interaction while fostering trust and cooperation. We formalize these ideas in a structural equation modeling framework in which innovation activities and intellectual capital serially mediate the relationship between social capital and performance and show that the implications of our model are supported by data from Turkish public hospitals. We find no evidence of a direct link between social capital and performance or between innovation activities and performance and determine that intellectual capital is the crucial link between social capital and organizational performance. © 2022 The Author(s)
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    A text analytics model for agricultural knowledge discovery and sustainable food production: A case study from Oklahoma Panhandle
    (Elsevier Inc., 2023) Bagheri, A.; Taghvaeian, S.; Delen, D.
    With recent increases in the use of social media in agricultural communities, many farmers are showing more and more interest in participating in social media and sharing different aspects of their profession with peers and policymakers. However, researchers have not explored this valuable data source well enough to help improve agribusiness decision-making. This study aims to investigate the potential capability and richness of social media for agricultural knowledge discovery, which can help monitor, detect, and predict critical agricultural events and activities and develop more sustainable food production and agricultural economy. This research utilizes text-mining tools and techniques to collect, process, and mine unstructured textual data from Twitter. Then, it examines the agreement between the retrieved information from tweets and that from the current monitoring systems and common cultural practices. Our findings illustrate that social media data can effectively provide information regarding the commencement and duration of significant cultural activities This study also examines the classification performance of several popular sentiment analysis tools on the farmer's tweets and provides suggestions for future research on domain-specific sentiment lexicons for agricultural purposes. © 2023 The Author(s)
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    Towards explainable artificial intelligence through expert-augmented supervised feature selection
    (Elsevier B.V., 2024) Rabiee, M.; Mirhashemi, M.; Pangburn, M.S.; Piri, S.; Delen, D.
    This paper presents a comprehensive framework for expert-augmented supervised feature selection, addressing pre-processing, in-processing, and post-processing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enhance the exploration and exploitation capabilities of a Genetic Algorithm (GA). Balancing explainability and prediction accuracy, we formulate two multi-objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability by reducing the number of selected features and prioritizing those considered more relevant from the domain expert's perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection process as a multi-objective problem. Traditional feature selection techniques lack the capability to efficiently search the solution space considering our explainability-focused objective function. To overcome this, we leverage the Genetic Algorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters through Bayesian optimization. For post-processing XAI, we present the Posterior Ensemble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying features as underrated, overrated, or appropriately rated. We evaluate the performance of our proposed GAs on 16 publicly available datasets, focusing on prediction accuracy in a single objective setting. Moreover, we test our multi-objective model on a classification dataset to show the applicability and effectiveness of our framework. Overall, this paper provides a holistic and nuanced approach to explainable feature selection, offering decision-makers a comprehensive understanding of feature importance. © 2024 Elsevier B.V.