Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis

Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Wei, Chin-Ping; Yu, Chunhui; Chan, Allen; Wang, Hue-Yu

doi:10.1109/tsmca.2007.897700

Cited by 14 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal is to maximize the reduction in the standard deviation by testing the possible splits over the training data that reach a particular node, and then splitting it up further Table 3. Results of parameter optimization using the grid-search method C γ SVM 10,947.4705 0.0010 on each branch until only a few instances remain (Hu et al 2007). Then the tree is pruned of unwanted nodes, and a smoothing procedure is applied to avoid sharp discontinuities between adjacent linear models at the leaves of the pruned tree.…”

Section: Dt Implementationmentioning

confidence: 99%

Prediction of Government-Owned Building Energy Consumption Based on an Rrelieff and Support Vector Machine Model

Son

Kim

et al. 2015

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

show abstract

Section: Dt Implementationmentioning

confidence: 99%

Prediction of Government-Owned Building Energy Consumption Based on an Rrelieff and Support Vector Machine Model

Son

Kim

et al. 2015

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

show abstract

“…After removing duplicated articles, 3,346 studies were screened by the title and/or abstract, 3,175 irrelevant studies were excluded and 171 articles were included for full‐text review. Finally, 64 articles related to precision dosing using ML were included for analysis 11–74 . The PRISMA flow diagram representing the study selection process and review results is presented in Figure .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N=23, 35.9%) and anti‐infectives (N=21, 32.8%), and there is currently only very limited attention for special populations such as children (N=22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

show abstract

“…As illustrated in Figure 2, the proposed method contains two stages: relevant article retrieval through multilabel classification and judgement category forecast based on sentiment analysis using the classifier. SVM was chosen as the classification algorithm because of its strong performance in many previous text classification studies [50–52].…”

Section: Methodsmentioning

confidence: 99%

A two-phase sentiment analysis approach for judgement prediction

Liu

Chen

2017

Journal of Information Science

View full text Add to dashboard Cite

Factual scenario analysis of a judgement is critical to judges during sentencing. With the increasing number of legal cases, professionals typically endure heavy workloads on a daily basis. Although a few previous studies have applied information technology to legal cases, according to our research, no prior studies have predicted a pending judgement using legal documents. In this article, we introduce an innovative solution to predict relevant rulings. The proposed approach employs text mining methods to extract features from precedents and applies a text classifier to automatically classify judgements according to sentiment analysis. This approach can assist legal experts or litigants in predicting possible judgements. Experimental results from a judgement data set reveal that our approach is a satisfactory method for judgement classification.

show abstract

Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis

Cited by 14 publications

References 30 publications

Prediction of Government-Owned Building Energy Consumption Based on an Rrelieff and Support Vector Machine Model

Prediction of Government-Owned Building Energy Consumption Based on an Rrelieff and Support Vector Machine Model

Machine Learning: A New Approach for Dose Individualization

A two-phase sentiment analysis approach for judgement prediction

Contact Info

Product

Resources

About