E-resources
Peer reviewed
-
Chen, Wenjia; Li, Jinlin
Artificial intelligence in medicine, March 2024, 2024-Mar, 2024-03-00, 20240301, Volume: 149Journal Article
In this study, the start time of teleconsultations is optimized for the clinical departments of class A tertiary hospitals to improve service quality and efficiency. For this purpose, first, a general teleconsultation scheduling model is formulated. In the formulation, the number of services (NS) is one of the objectives because of demand intermittency and service mobility. Demand intermittency means that demand has zero size in several periods. Service mobility means that specialists move between clinical departments and the National Telemedicine Center of China to provide the service. For problem-solving, the general model is converted into a Markov decision process (MDP) by elaborately defining the state, action, and reward. To solve the MDP, deep reinforcement learning (DRL) is applied to overcome the problem of inaccurate transition probability. To reduce the dimensions of the state–action space, a semi-fixed policy is developed and applied to the deep Q network (DQN) to construct an algorithm of the DQN with a semi-fixed policy (DQN-S). For efficient fitting, an early stop strategy is applied in DQN-S training. To verify the effectiveness of the proposed scheduling model and the model solving method DQN-S, scheduling experiments are carried out based on actual data of teleconsultation demand arrivals and service arrangements. The results show that DQN-S can improve the quality and efficiency of teleconsultations by reducing 9%–41% of the demand average waiting time, 3%–42% of the number of services, and 3%–33% of the total cost of services. •Start time is optimized to improve the quality and efficiency of teleconsultations.•The scheduling problem is modeled and solved from a data-driven perspective.•A deep reinforcement learning approach is developed for efficient solutions.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.