Application of Machine Learning Based Hospital Up-gradation Policy for Bangladesh

Shuvo, Salman Sadiq; Ahmed, Rubel; Kabir, Sadia Binta; Shetu, Shaila Akter

doi:10.1145/3428363.3428364

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…Recently, the work [14] has discussed the application of ML-based hospital up-gradation policy for Bangladesh. However, they run their experiments for a hypothetical rural Bangladesh region, thus lacking a practical zone's feasibility analysis.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The work provided an efficient guideline for this problem; however, it lacks some tools for practical implementation. So, we intend to address the following shortcomings of the paper [14]:…”

Section: B Literature Reviewmentioning

confidence: 99%

“…6. MDP Model for Policymaking [14] and the memory of the block. The overall architecture is shown in Fig.…”

Section: E Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Markov decision processes (MDP) are helpful when it is a matter of providing theoretical structure for making agentbased scenarios. We follow the MDP model provided in [14] with our modification of using an LSTM network to predict population growth. For clarity, We have explained the model in full here.…”

Section: A Mdp Model For Policymakingmentioning

confidence: 99%

“…The regional healthcare authority of our country has a population history of more than 20 years of all the Upazila they serve. This sorts one of the drawbacks of the work in [14] and makes this work nationwide implementable. The Fig.…”

Section: B Lstm Network For Population Prediction P Tmentioning

confidence: 99%

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

Kabir

Shuvo

Ahmed

2021

Preprint

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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.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

“…6. MDP Model for Policymaking [14] and the memory of the block. The overall architecture is shown in Fig.…”

Section: E Long Short-term Memory (Lstm)mentioning

confidence: 99%

Section: A Mdp Model For Policymakingmentioning

confidence: 99%

Section: B Lstm Network For Population Prediction P Tmentioning

confidence: 99%