A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Lu, Keyu; Liao, Huchang

doi:10.1007/s10489-021-02909-y

Cited by 14 publications

(2 citation statements)

References 207 publications

(288 reference statements)

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“…Therefore, flood susceptibility estimation is a Multi‐Criteria Decision Making (MCDM) problem. MCDM is a methodology to address the latent convolution and uncertainty of such problems and the vague knowledge due to the participation of several experts in the decision‐making procedure (Lu & Liao, 2022). Many studies assert that the MCDM approach has played a significant role by identifying, integrating, or rating conditioning factors markedly in natural hazard analysis which can outweigh other methods which require historical data and a large amount of accurate data.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

Sahraei

Kanani‐Sadat

Homayouni

et al. 2022

J Flood Risk Management

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Characterizing and identification of flood-susceptible areas can be a solution to mitigate the damages and fatality rate. This study proposes a novel hybrid MCDM framework to assess flood susceptibility in large ungauged watersheds dealing with data scarcity issues. The proposed method examines the interdependencies and causal relationships between various criteria affecting the flooding procedure using the DEcision-MAking Trial and Evaluation Laboratory (DEMATEL). Moreover, since experts' opinions contain uncertainty, the fuzzy logic is integrated with DEMATEL to overcome this shortcoming. Then, the local weights of criteria were estimated using the Best-Worst Method (BWM) to enhance the pairwise comparisons process. Final criteria weights were obtained using Fuzzy DEMATEL and BWM results in Analytical Network Process (ANP) super-matrix. Finally, the criteria were distributed spatially using the Complex Proportional Assessment of Alternatives (COPRAS) method based on obtained weights. The proposed method was compared with different approaches such as Fuzzy-DEMATEL ANP, BWM, and AHP using several statistical measures. We concluded that the novel hybrid proposed method outperformed other approaches based on our results. Moreover, by overlaying classified maps with the historical flood events locations, it was concluded that 85.96% of flooded areas were classified as "high" and "very high.

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Section: Introductionmentioning

confidence: 99%

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

Sahraei

Kanani‐Sadat

Homayouni

et al. 2022

J Flood Risk Management

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“…The most relevant goal of a SMART study is the determination of the optimal DTR. Statistical techniques [4] have been adopted to achieve this goal. Patients with similar conditions may have different reactions to given treatments.…”

Section: Introductionmentioning

confidence: 99%

Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes

et al. 2022

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Dynamic Treatment Regimes (DTRs) are adaptive treatment strategies that allow clinicians to personalize dynamically the treatment for each patient based on their step-by-step response to their treatment. There are a series of predefined alternative treatments for each disease and any patient may associate with one of these treatments according to his/her demographics. DTRs for a certain disease are studied and evaluated by means of statistical approaches where patients are randomized at each step of the treatment and their responses are observed. Recently, the Reinforcement Learning (RL) paradigm has also been applied to determine DTRs. However, such approaches may be limited by the need to design a true reward function, which may be difficult to formalize when the expert knowledge is not well assessed, as when the DTR is in the design phase. To address this limitation, an extension of the RL paradigm, namely Inverse Reinforcement Learning (IRL), has been adopted to learn the reward function from data, such as those derived from DTR trials. In this paper, we define a Projection Based Inverse Reinforcement Learning (PB-IRL) approach to learn the true underlying reward function for given demonstrations (DTR trials). Such a reward function can be used both to evaluate the set of DTRs determined for a certain disease, as well as to enable an RL-based intelligent agent to self-learn the best way and then act as a decision support system for the clinician.

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Artificial Intelligence and Machine Learning in Healthcare Sector

Chavda¹,

Patel²,

Patel³

et al. 2023

Bioinformatics Tools for Pharmaceutical Drug Product Development

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A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Cited by 14 publications

References 207 publications

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes

Artificial Intelligence and Machine Learning in Healthcare Sector

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