Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Piccialli, Francesco; Giampaolo, Fabio; Prezioso, Edoardo; Camacho, David; Acampora, Giovanni

doi:10.1016/j.inffus.2021.03.004

Cited by 35 publications

(13 citation statements)

References 60 publications

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“…DL algorithms by having high levels of abstraction are expected to improve the accuracy of the prediction process (Mocanu et al 2016 ). The forecasting problem in the collected material refers to source forecasting (Charmchi et al 2021 ), demand forecasting (Nikolopoulos et al 2021 ; Chien et al 2020 ; Koç and Türkoğlu 2021 ; Bousqaoui et al 2021 ; Mocanu et al 2016 ; Kilimci et al 2019 ; Punia et al 2020 ; Tang and Ge 2021 ), sales forecasting (Weng et al 2019a ; Liu et al 2020 ; Piccialli et al 2021 ), price forecasting (Weng et al 2019a , b ; Guo 2020 ), performance forecasting (Shankar et al 2020 ), or a combination of these problems named as hybrid forecasting (Khan et al 2020 ; Wu et al 2021 ).…”

Section: Systematic Review Resultsmentioning

confidence: 99%

“…Two real-world sales datasets from a supermarket and a company selling pesticides have been used to verify the performance of the model. In the healthcare industry, Piccialli et al ( 2021 ) proposed a predictive framework to forecast a 7-day sequence of respiratory disease bookings based on a hybrid neural network. Bookings time series data of the healthcare authorities of Campania Region in Italy as well as air quality and weather data have been used in the forecasting model.…”

Section: Systematic Review Resultsmentioning

confidence: 99%

“…In all of the above-mentioned papers, DLs outperformed over the other methods. In terms of time-series forecasting methods autoregressive integrated moving average (ARIMA) has been the most tested method (Weng et al 2019a , b ; Piccialli et al 2021 ; Koç and Türkoğlu 2021 ; Bousqaoui et al 2021 ; Mao et al 2018 ) that could not perform as good as DLs.…”

Section: Systematic Review Resultsmentioning

confidence: 99%

“…With the increasingly vast amount of data available today, companies should use Artificial Intelligence techniques to drive business growth through actionable insights generated from datasets (Piccialli et al 2021 ). Although there is no common category among researchers to classify AI techniques, based on Wikipedia website, we can categorize AI techniques in 7 different sections: Search and optimization, Logic, Probabilistic methods for uncertain reasoning, Classifiers and statistical learning methods, Artificial neural networks, Deep learning, Specialized languages and hardware (Wikipedia 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

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Section: Systematic Review Resultsmentioning

confidence: 99%

Section: Systematic Review Resultsmentioning

confidence: 99%

Section: Systematic Review Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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“…• Forecasting hospital demand (A&E, outpatient and inpatient specialties), i.e. Aboagye-Sarfo et al (2015), Gul and Guneri (2015), Zinouri et al (2018), Cote and Smith (2018), Ordu et al (2019a), Kaushik et al (2020) and Piccialli et al (2021). • Mathematical modelling in healthcare settings, i.e.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A hybrid analytical model for an entire hospital resource optimisation

2021

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Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

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Temporal Saliency Detection Towards Explainable Transformer-Based Timeseries Forecasting

Duong-Trung,

Nguyen,

Le-Phuoc

2024

Communications in Computer and Information Science

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Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Cited by 35 publications

References 60 publications

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

A hybrid analytical model for an entire hospital resource optimisation

Temporal Saliency Detection Towards Explainable Transformer-Based Timeseries Forecasting

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