A visual approach to enhance coordination among diagnostic units using self-organizing map

Jha, R. K.; Sahay, B.S.; Chattopadhyay, Manojit; Gajpal, Yuvraj

doi:10.1007/s40622-017-0170-8

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Widely adopted in manufacturing to handle increasing product complexity and variety (Greene & Sadowski, 1984), reports of cellular layout adoption in healthcare are scarce and limited to patient clustering (Jha, Sahay, Chattopadhyay, & Gajpal, 2018) or consulting reports of applications in hospitals such as the Virginia Mason Medical Center (Riopelle, 2011). Group technology consists of identifying products that could be clustered together into families and processed in mini-factories denoted as cells (Sakran, Mahbuba, & Jafer, 2017).…”

Section: Research Opportunitiesmentioning

confidence: 99%

Layout Planning in Healthcare Facilities: A Systematic Review

Benitez

Silveira

Fogliatto

2019

HERD

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This study presents a systematic review of the literature on layout planning in healthcare facilities. The review includes 81 articles from journals, conferences, books, and other documents. Articles were classified in two groups according to their main contents including (i) concepts and guidelines and (ii) techniques and tools to assist in layout planning in healthcare facilities. Results indicate that a great variety of concepts and tools have been used to solve layout problems in healthcare. However, healthcare environments such as hospitals can be complex, limiting the ability to obtain optimal layout solutions. Influential factors may include the flows of patients, staff, materials, and information; layout planning and implementation costs; staff and patients safety and well-being; and environmental contamination, among others. The articles reviewed discussed and often proposed solutions covering one or more factors. Results helped us to propose future research directions on the subject.

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Section: Research Opportunitiesmentioning

confidence: 99%

Layout Planning in Healthcare Facilities: A Systematic Review

Benitez

Silveira

Fogliatto

2019

HERD

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A dynamic annealing learning for PLSOM neural networks: Applications in medicine and applied sciences

Hameed

2023

Journal of Radiation Research and Applied Sciences

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Deep Learning in the Healthcare Industry

Shirazi

Souza

Kashef

et al. 2020

Advances in Healthcare Information Systems and Administration

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Artificial Neural networks (ANN) are composed of nodes that are joint to each other through weighted connections. Deep learning, as an extension of ANN, is a neural network model, but composed of different categories of layers: input layer, hidden layers, and output layers. Input data is fed into the first (input) layer. But the main process of the neural network models is done within the hidden layers, ranging from a single hidden layer to multiple ones. Depending on the type of model, the structure of the hidden layers is different. Depending on the type of input data, different models are applied. For example, for image data, convolutional neural networks are the most appropriate. On the other hand, for text or sequential and time series data, recurrent neural networks or long short-term memory models are the better choices. This chapter summarizes the state-of-the-art deep learning methods applied to the healthcare industry.

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A visual approach to enhance coordination among diagnostic units using self-organizing map

Cited by 3 publications

References 19 publications

Layout Planning in Healthcare Facilities: A Systematic Review

Layout Planning in Healthcare Facilities: A Systematic Review

A dynamic annealing learning for PLSOM neural networks: Applications in medicine and applied sciences

Deep Learning in the Healthcare Industry

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