Improving the anesthetic process by a fuzzy rule based medical decision system

Méndez, J. Hernández; Leon, Ana M.; Marrero, Ayoze; González-Cava, José M.; Reboso, José Antonio; Estévez, J. I.; Gómez‐González, José Francisco

doi:10.1016/j.artmed.2017.12.005

Cited by 56 publications

(19 citation statements)

References 35 publications

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“…The knowledge-based methods explicitly represent knowledge as symbols in the form of rules, ontologies, past cases, or other types of knowledge structures. Specifically, the surveyed approaches ranged from the use of ontologies 61 , 62 , 67 – 69 , to rule-based reasoning (including the adoption of fuzzy rules) 64 , 66 – 68 , 72 – 75 , 78 – 80 , 82 – 85 , to the definition of semantic or associative networks 76 , 77 , to temporal reasoning 65 , to probabilistic models such as Bayesian Networks 60 , 63 , 70 , 81 , and to case base reasoning CBR 61 , 71 .…”

Section: Resultsmentioning

confidence: 99%

“…As regards to the addressed task, most works deal with diagnosis 60 , 62 , 64 , 70 , 73 , 74 , 77 – 80 , 82 , 84 , 85 or therapy/treatment support 61 – 63 , 66 , 75 , 81 , while some works address classification 83 , patient’s behaviour tracking or activity scheduling 65 , and training 68 . A set of papers deals with computer-interpretable guidelines (CIGs) 67 , 69 , 71 , 72 .…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Montani

Striani

2019

Yearb Med Inform

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Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Montani

Striani

2019

Yearb Med Inform

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“…Prior fuzzy works have shown several applications of fuzzy methodologies in the medical and pharmaceutical industries. Some researchers use this approach to improve medical procedures and training at hospitals, such as Mendez et al (2018) and Nakawala et al (2018). From a different prism, Gascón et al (2007) classify countries depending on their likelihood to consume and produce generics through fuzzy techniques.…”

Section: On Strategic Choices Faced By Large Pharmaceutical Laboratormentioning

confidence: 99%

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

Puente

Gascón

Ponte

et al. 2019

Artificial Intelligence in Medicine

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Structured abstractObjectives. We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment.Through three structured fuzzy inference systems (FIS), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of innovation risk.Methods and Materials. Three FIS, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to the strategic choices they face.Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base were agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and validated the model. Results.In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret the results. We have also run regressions to better understand the implications of our results.ii Conclusions. The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of the system, which may be easily applied to other settings. KeywordsFuzzy inference systems; 2-tuple-based method; innovation risk; R&D; Pharmaceutical laboratories Highlights -Consensual fuzzy sets and rules are used to model innovation risk in large pharmaceutical laboratories, clarifying the system for pharmaceutical decision-makers and stakeholders.-With the aid of experts, we have combined knowledge with large and diverse datasets and embedded it into the fuzzy inference systems.-The fuzzy evaluation model captures both the financial side and the pipeline side of innovation risk of pharmaceutical companies operating in uncertain environments.-The fuzzy evaluation model we develop in this work has been applied to evaluate innovation risk in a sample of 31 large pharmaceutical laboratories covering the period from 2008 to 2013. Acronyms ATCAnatomical, Therapeutic, Chemical (classification system) CFO Chief Financi...

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“…Closed-loop systems based on proportional-integral-derivative (PID) controllers have been proposed in [2,11,12]. Other solutions exploit more sophisticated control architectures, such as model predictive control (MPC) [13][14][15][16][17][18], fuzzy control [19][20][21], positive control [22], fractional control [23,24] and H ∞ control [25][26][27]. Finally, some methodologies focus exclusively on the induction phase to achieving a smooth transient response [28,29].…”

Section: Introductionmentioning

confidence: 99%

Event‐based control tuning of propofol and remifentanil coadministration for general anaesthesia

Merigo

Padula

Latronico

et al. 2020

IET Control Theory & Appl

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In this study, the authors present robust tuning rules for an event-based control architecture for the automatic regulation of the depth of hypnosis in anaesthesia. The authors' control system uses propofol and remifentanil coadministration as control variables and the bispectral index as controlled variable. The control system is based on a PIDPlus controller combined with an event generator that detects significant variations of the BIS signal, thus providing strong filtering of the noise. A fixed ratio between the drug infusions allows the anaesthesiologist to explicitly regulate the opioid-hypnotic balance of the anaesthesia. The tuning rules are developed by solving a min-max optimisation problem that optimises the worst-case scenario over a given data set of patient models. A gain scheduling strategy yields optimal performance in both the induction and the maintenance phases of anaesthesia. Finally, through the Monte Carlo method, they validate the effectiveness of the proposed approach on a general population, and the robustness to the intra-and inter-patient variability for different infusion balances.

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Improving the anesthetic process by a fuzzy rule based medical decision system

Cited by 56 publications

References 35 publications

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

Event‐based control tuning of propofol and remifentanil coadministration for general anaesthesia

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