Abstract:
OBJECTIVES:A well-developed appointment system can help increase the utilization of medical facilities in an outpatient department. This paper outlines the development of an appointment system that can make an outpatient department work more efficiently and improve patient satisfaction level. METHODS:A Markov decision process model is proposed to schedule sequential appointments with the consideration of patient preferences in order to maximize the patient satisfaction level. Adaptive dynamic programming algorithms are developed to avoid the curse of dimensionality. These algorithms can dynamically capture patient preferences, update the value of being a state, and thus improve the appointment decisions. RESULTS:Experiments were conducted to investigate the performance of the algorithms. The convergence behaviors under different settings, including the number of iterations needed for convergence and the accuracy of results, were examined. Bias-adjusted Kalman filter step-sizes were found to lead to the best convergence behavior, which stabilized within 5000 iterations. As for the effects of exploration and exploitation, it resulted in the best convergence behavior when the probability of taking a myopically optimal action equaled 0.9. The performance of value function approximation algorithm was greatly affected by the combination of basis functions. Under different combinations, errors varied from 2.7% to 8.3%. More preferences resulted in faster convergence, but required longer computation time. CONCLUSIONS:System parameters are adaptively updated as bookings are confirmed. The proposed appointment scheduling system could certainly contribute to better patient satisfaction level during the booking periods.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Wang J,Fung RYdoi
10.1016/j.artmed.2014.12.002subject
Has Abstractpub_date
2015-01-01 00:00:00pages
33-40issue
1eissn
0933-3657issn
1873-2860pii
S0933-3657(14)00139-0journal_volume
63pub_type
杂志文章abstract::Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to peri...
journal_title:Artificial intelligence in medicine
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abstract::We introduce a novel approach to couple temporal similarity with spatial neighborhood information. This is achieved by concatenating the K nearest, spatially contiguous neighbors of a pixel time-course (TC) of T time-instances. This produces a new TC of (K+1)T time instances. Depending on how "nearest" is defined, we ...
journal_title:Artificial intelligence in medicine
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doi:10.1016/s0933-3657(02)00005-2
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abstract::We describe an approach for developing knowledge-based medical decision support systems based on the new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca + project which mainly deals with medical applications. One goal was to s...
journal_title:Artificial intelligence in medicine
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abstract::A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an incr...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2018.12.008
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abstract::Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure ...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.01.009
更新日期:2004-06-01 00:00:00
abstract::Abnormalities in the organization of brain circuits may underlie many types of epilepsy. This hypothesis can best be evaluated in the case of temporal lobe epilepsy, where evidence of rewiring (synaptic reorganization) can be found in the dentate gyrus. Computer modeling of normal and reorganized dentate gyrus was use...
journal_title:Artificial intelligence in medicine
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doi:10.1016/s0933-3657(98)00005-0
更新日期:1998-05-01 00:00:00
abstract::The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual pro...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.01.017
更新日期:2004-07-01 00:00:00
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journal_title:Artificial intelligence in medicine
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doi:10.1016/j.artmed.2009.07.012
更新日期:2010-02-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.11.001
更新日期:2006-01-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2008.07.014
更新日期:2009-02-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2015.11.003
更新日期:2018-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101950
更新日期:2020-08-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2008.08.004
更新日期:2009-02-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2004.01.015
更新日期:2004-09-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2019.101742
更新日期:2020-01-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2007.08.002
更新日期:2007-10-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101821
更新日期:2020-04-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2008.07.015
更新日期:2009-02-01 00:00:00
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journal_title:Artificial intelligence in medicine
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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更新日期:2001-01-01 00:00:00
abstract:OBJECTIVES:Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough f...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2013.08.004
更新日期:2013-10-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/0933-3657(94)00028-q
更新日期:1995-04-01 00:00:00
abstract::Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer p...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2019.101706
更新日期:2019-09-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/s0933-3657(96)00355-7
更新日期:1996-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2020.101790
更新日期:2020-04-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2013.08.001
更新日期:2013-10-01 00:00:00