Research Article
Predictive Model for Depression Without Medical Intervention
Charles Mwangi*
,
Kennedy Nyongesa,
Everlyne Akoth Odero
Issue:
Volume 14, Issue 1, February 2025
Pages:
1-11
Received:
15 August 2024
Accepted:
5 September 2024
Published:
7 January 2025
DOI:
10.11648/j.ajtas.20251401.11
Downloads:
Views:
Abstract: Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develop a predictive model for depression that uses the symptoms. The study used both simulated data and real data from the hospitals. The study developed hidden markov model that help to compute the transitional probabilities. The study also used the logistic regression to assess the predictive power of the symptoms of depression. The study found that insomnia positively influence the probability of depression among the patients. The study also found that guilt positively influence the probability of depression among the patients. From the results, the study found that suicidal positively influence the probability of depression among the patients and also fatigue influence the probability of depression. From the study it was also found that retardation positively influence the probability of depression. Finally, found that the change in anxiety negatively influence the probability of depression among the patients. The study also conclude that the predictive model can be used to predict the depression status of the patients by a medical doctor given that the observable symptoms are present.
Abstract: Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develo...
Show More
Research Article
Impact of Varying Response Time on Ambulance Deployment Plans in Heterogeneous Regions Using Multiple Performance Indicators
Tichaona Wilbert Mapuwei*,
Oliver Bodhlyera,
Henry Mwambi
Issue:
Volume 14, Issue 1, February 2025
Pages:
12-29
Received:
10 September 2024
Accepted:
13 December 2024
Published:
14 January 2025
DOI:
10.11648/j.ajtas.20251401.12
Downloads:
Views:
Abstract: The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the fleet size influences the average response time below a certain threshold value across all the heterogeneous regions. However, when fleet size is increased beyond this threshold value, no significant changes occur in the performance indicators. Fleet size varied inversely to ambulance utilisation levels. As fleet size is gradually increased, utilisation levels also gradually decreased. Due care must be taken to avoid under-utilisation of ambulances during deployment. Under utilisation culminates to human and material equipment idleness and yet the resources available are scarce and should be deployed where needed most. For critical resources such as ambulances in emergency response, increasing the resource did not always translate to better performance. However, directing efforts towards reducing response time (call delay time, chute time, queuing and travel time) results in improvement of service performance and corresponding reduction in number of ambulances required to achieve a desired service level. Performance indicators such as utilisation levels and throughput ratios are imperative in ensuring balanced resource allocation and capacity utilisation which avoids under or over utilisation of scarce and yet critical resources. This has a strong bearing on both human and material resource workloads. The integrated strategy can also be replicated with relative ease to manage other service systems with a server-to-customer relationship.
Abstract: The paper conducts an assessment of the impact of varying response time distributions on ambulance deployment plans by integrating forecasting, simulation and optimisation techniques to predefined locations with heterogeneous demand patterns. Bulawayo metropolitan city was used as a case study. The paper proposes use of future demand and allows for...
Show More
