Deep Reinforcement Learning Based Cost-Benefit Analysis for Hospital Capacity Planning

Shuvo, Salman Sadiq; Ahmed, Rubel; Symum, Hasan; Yılmaz, Yasin

doi:10.1109/ijcnn52387.2021.9533482

Cited by 3 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compare our deep MORL-based policy with a myopic policy from [3], a Recurrent Neural Network (RNN)-based policy from [24], and a single objective RL-based policy from [6] for a 30-year scheme. We selected decision thresholds to incur investment costs ranging from maximum to minimum for every policy to make a head-to-head comparison with our proposed MORL method.…”

Section: B Benchmark Policiesmentioning

confidence: 99%

“…3) Single Objective RL (SORL) Based Policy in [6]: In our preliminary work [6], we converted the DOS into monetary cost by assigning DOS cost for each region each day as in…”

Section: B Benchmark Policiesmentioning

confidence: 99%

“…) for the single objective RL approach in [6]. This method does not provide a handle over preference between the two objectives.…”

Section: B Benchmark Policiesmentioning

confidence: 99%

“…We propose a Multi-Objective Reinforcement Learning (MORL) method based on deep neural networks to satisfy two objectives: minimize augmentation cost and Denial of Service (DOS) to the patients. In our preliminary work [6], we proposed an RL approach that converts the patients' discomfort caused by DOS into monetary cost through fixed coefficients. However, defining fixed coefficients for different places and periods is not feasible, causing a practical challenge for the applicability of the work [6].…”

Section: Introductionmentioning

confidence: 99%

“…In our preliminary work [6], we proposed an RL approach that converts the patients' discomfort caused by DOS into monetary cost through fixed coefficients. However, defining fixed coefficients for different places and periods is not feasible, causing a practical challenge for the applicability of the work [6]. To this end, in this work, motivated by the Paretooptimal Q-learning (PQL) method [7] we propose multiobjective actor-critic method to avoid the forced conversion of DOS discomfort to monetary cost.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Objective Reinforcement Learning Based Healthcare Expansion Planning Considering Pandemic Events

Shuvo

Symum

Ahmed

et al. 2023

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

Hospital capacity expansion planning is critical for a healthcare authority, especially in regions with a growing diverse population. Policymaking to this end often requires satisfying two conflicting objectives, minimizing capacity expansion cost and minimizing the number of denial of service (DOS) for patients seeking hospital admission. The uncertainty in hospital demand, especially considering a pandemic event, makes expansion planning even more challenging. This work presents a multi-objective reinforcement learning (MORL) based solution for healthcare expansion planning to optimize expansion cost and DOS simultaneously for pandemic and non-pandemic scenarios. Importantly, our model provides a simple and intuitive way to set the balance between these two objectives by only determining their priority percentages, making it suitable across policymakers with different capabilities, preferences, and needs. Specifically, we propose a multiobjective adaptation of the popular Advantage Actor-Critic (A2C) algorithm to avoid forced conversion of DOS discomfort cost to a monetary cost. Our case study for the state of Florida illustrates the success of our MORL based approach compared to the existing benchmark policies, including a state-of-the-art deep RL policy that converts DOS to economic cost to optimize a single objective.

show abstract

Section: B Benchmark Policiesmentioning

confidence: 99%

“…3) Single Objective RL (SORL) Based Policy in [6]: In our preliminary work [6], we converted the DOS into monetary cost by assigning DOS cost for each region each day as in…”

Section: B Benchmark Policiesmentioning

confidence: 99%

“…) for the single objective RL approach in [6]. This method does not provide a handle over preference between the two objectives.…”

Section: B Benchmark Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Objective Reinforcement Learning Based Healthcare Expansion Planning Considering Pandemic Events

Shuvo

Symum

Ahmed

et al. 2023

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

show abstract

Use of Machine Learning for Long Term Planning and Cost Minimization in Healthcare Management

Kabir

Shuvo

Ahmed

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The Healthcare system of a country is a crucial infrastructure that requires long-term capacity planning. The covid 19 outbreak pointed to the necessity of adequate hospital capacity, especially for developing countries like Bangladesh. The existing infrastructure planning of these countries emphasizes short-term goals and lacks vision planning for a long time horizon. It is in the country's best interest to make long-term capacity expansion plans, a strategy the developed countries banked to provide adequate healthcare facilities to their residents. However, no single solution is appropriate for a different region. Hence, it is required to comprehensively study the situation and constraints of the specific region before providing expensive capacity expansion plans. This work focuses on applying a deep Reinforcement Learning based long-term hospital bed capacity expansion plan. We utilize the RNN-LSTM based population forecast, deep Reinforcement Learning (RL) based policy-making, and state-of-the-art Artificial Intelligence techniques to provide a solution. We perform a case study for the Abhaynagar Upazila of Jessore, one of the largest cities in the southwest part of Bangladesh, to analyze the benefits of such an approach compared to existing myopic policies. The experiment results show that the deep RL-based policy significantly minimizes cost over a 30-year expansion plan.

show abstract

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

Shuvo

Yılmaz

2022

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Deep Reinforcement Learning Based Cost-Benefit Analysis for Hospital Capacity Planning

Cited by 3 publications

References 20 publications

Multi-Objective Reinforcement Learning Based Healthcare Expansion Planning Considering Pandemic Events

Multi-Objective Reinforcement Learning Based Healthcare Expansion Planning Considering Pandemic Events

Use of Machine Learning for Long Term Planning and Cost Minimization in Healthcare Management

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

Contact Info

Product

Resources

About