Enhanced visual data mining process for dynamic decision-making

Ltifi, Hela; Benmohamed, Emna; Kolski, Christophe; Ayed, Mounir Ben

doi:10.1016/j.knosys.2016.09.009

Cited by 26 publications

(10 citation statements)

References 57 publications

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“…Indeed, ML can have a significant clinical and practical effect on nursing practice. ML can be considered a kind of algorithm that enables the adjustment of internal parameters to collate and analyse large datasets while mining potentially relevant information, which can accurately classify and predict patients (Johnson et al., 2016; Ltifi et al., 2016). Owing to many patient monitoring parameters, ML has been used to assess patient prognosis (Holmgren et al., 2019; Shillan et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Chang

Fan

et al. 2021

J Nurs Manag

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Aim This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs. Background Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear. Methods Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model. Results Data of 300 patients were analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B and C were <1.44, 1.44–2.03 and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests. Conclusions Classifying patients based on disease severity and care needs enables the development of tailored nursing management for each subgroup. Implications for Nursing Management The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.

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Section: Discussionmentioning

confidence: 99%

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Chang

Fan

et al. 2021

J Nurs Manag

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“…Example inputs and output for a task are provided to 'the machine' and, using learning algorithms, a model is created so that new information can be interpreted. Machine learning approaches can provide accurate predictions based on large, structured datasets extracted from EHRs [5,6]. There have been rapid developments in machine learning methodology, but many methods still require large datasets to model complex and non-linear effects, and thereby improve on prediction rules developed using standard statistical methods [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

et al. 2019

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Background Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients’ journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians. Methods Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted. Results Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43 [16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108–4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000–10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]). Conclusions The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice. Electronic supplementary material The online version of this article (10.1186/s13054-019-2564-9) contains supplementary material, which is available to authorized users.

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“…However, the parameter computation process is often computationally demanding and timeconsuming, and developing the structure of the grey model requires expert knowledge [10,15]. This is where black-box models, or purely data-driven models, are beneficial as they are easy to build and computationally efficient [15,[27][28][29], especially when a large amount of historical data is available to train the models. Multiple linear regression and self-regression methods were combined to predict building monthly energy consumption [30].…”

Section: Introductionmentioning

confidence: 99%

Optimised building energy and indoor microclimatic predictions using knowledge-based system identification in a historical art gallery

Ganguly

Ahmed

Wang

2019

Neural Comput & Applic

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This paper presents a system identification (SID) model for an historical art gallery of great cultural significance. These buildings require tight indoor temperature and moisture controls that demand significant energy from air handling units. Complex dynamic building systems, stringent conservation restrictions, and lack of detailed monitoring make diagnosing and optimising their energy use difficult. Building simulation software programmes have proven to be effective, but have tended to rely on data generated by simulation models. This study shows how artificial neural network (ANN) models trained with historical real data can predict a building's energy use and the optimal indoor microclimate necessary for conservation. Four ANN target-data scenarios were designed for optimised model predictions, and 12 ANN training algorithms were tested with six architectural scenarios collecting daily and hourly data. The ANN models used a randomised 80% sample of the database, with the remainder (20%) validating the models. The model displayed a high coefficient of correlation (0.99), with the mean square error and mean absolute error less than 0.1% and 2%, respectively. This ANN-based SID tool efficiently represents a complex building system and could be an ideal method for investigating optimisation strategies prior to their implementation. Keywords System identification Á Artificial neural networks Á Energy prediction model Á Indoor microclimatic control Á Optimisation Á Historical art gallery building

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Enhanced visual data mining process for dynamic decision-making

Cited by 26 publications

References 57 publications

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Machine learning‐based patient classification system for adult patients in intensive care units: A cross‐sectional study

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Optimised building energy and indoor microclimatic predictions using knowledge-based system identification in a historical art gallery

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