AI Article Synopsis
- - The paper addresses a complex surgical scheduling problem, aiming to optimize start times for multiple elective surgeries in a single operating room to maximize utility value while meeting performance standards.
- - Due to the analytical complexities involving expectations and variances, traditional optimization methods aren't suitable for this problem, making it difficult to solve directly.
- - A new decision support algorithm is introduced, which uses a two-phase approach: first screening for variance, then applying multiple attribute utility theory to identify the best scheduling solution, showing promising results in tests.
Article Abstract
In this paper, we consider a stochastic optimization model for a surgical scheduling problem with a single operating room. The goal is to determine the optimal start times of multiple elective surgeries within a single day. The term "optimal" is defined as the largest surgically related utility value while achieving a given threshold defined by the performance variation of a reference solution. The optimization problem is analytically intractable because it involves quantities such as expectation and variance formulations. This implies that traditional mathematical programming techniques cannot be directly applied. We propose a decision support algorithm, which is a fully sequential procedure using variance screening in the first phase, and then employing multiple attribute utility theory to select the best solution in the second phase. The numerical experiments show that the proposed algorithm can find a promising solution in a reasonable amount of time.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s10729-021-09580-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Automated Integration of AI Results into Radiology Reports Using Common Data Elements.
J Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
View Article and Find Full Text PDFPrognostic factors for overall survival in castration-resistant metastatic prostate cancer treated with docetaxel (MeProCSS): results from a German real-world cohort.
Int Urol Nephrol
January 2025
Department of Urology and Urosurgery, Medical Faculty Mannheim, University Medical Centre Mannheim (UMM), University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Baden-Württemberg, Germany.
Purpose: To identify prognostic factors for overall survival (OS) and develop a prognostic score in patients receiving docetaxel in metastatic castration-resistant prostate cancer (mCRPC).
Methods: Retrospective analysis was conducted on mCRPC patients treated with docetaxel at a German tertiary center between March 2010 and November 2023. Prognostic clinical and laboratory factors were analyzed using uni- and multivariable logistic regression.
"To Whom Should I Refer Her?" Factors Contributing to Religious Informal Helpers' Referral Decisions for Women with Mental Illness and At-Risk Women: The Case of Israeli Balaniyot.
J Relig Health
January 2025
School of Social Work, Hadassah Academic College, Jerusalem, Israel.
Religious informal helpers may play a crucial role in recognizing and providing referrals to mental health professional for at-risk individuals, including those with mental illness, especially since members of religious communities tend to conceal their difficulties and to view religious leaders as a sole source of assistance. This quantitative study aimed to explore Jewish bathhouse attendants ("balaniyot") who assist women in their monthly immersion, a unique situation in which mental health symptoms (e.g.
View Article and Find Full Text PDFImpact of policy adjustments on low carbon transition strategies in construction using evolutionary game theory.
Sci Rep
January 2025
College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China.
The construction industry is generally characterized by high emissions, making its transition to low-carbon practices essential for achieving a low-carbon economy. However, due to information asymmetry, there remains a gap in research regarding the strategic interactions and reward/punishment mechanisms between governments and firms throughout this transition. This paper addresses this gap by investigating probabilistic and static reward and punishment evolutionary games.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!