Can we optimize locations of hospitals by minimizing the number of patients at risk?

BACKGROUND: To reduce risk of death in acute ST-segment elevation myocardial infraction (STEMI), patients must reach a percutaneous coronary intervention (PCI) within 120 min from the start of symptoms. Current hospital locations represent choices made long since and may not provide the best possibilities for optimal care of STEMI patients. Open questions are: (1) how the hospital locations could be better optimized to reduce the number of patients residing over 90 min from PCI capable hospitals, and (2) how this would affect other factors like average travel time. METHODS: We formulated the research question as a facility optimization problem, which was solved by clustering method using road network and efficient travel time estimation based on overhead graph. The method was implemented as an interactive web tool and tested using nationwide health care register data collected during 2015-2018 in Finland. RESULTS: The results show that the number of patients at risk for not receiving optimal care could theoretically be reduced significantly from 5 to 1%. However, this would be achieved at the cost of increasing average travel time from 35 to 49 min. By minimizing average travel time, the clustering would result in better locations leading to a slight decrease in travel time (34 min) with only 3% patients at risk. CONCLUSIONS: The results showed that minimizing the number of patients at risk alone can significantly improve this single factor but, at the same time, increase the average burden of others. A more appropriate optimization should consider more factors. We also note that the hospitals serve also for other operators than STEMI patients. Although optimization of the entire health care system is a very complex optimization problems goal, it should be the aim of future research.

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation.

The smart grid (SG) is a contemporary electrical network that enhances the network's performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.