Design and evaluation of a decision support system for pain management based on data imputation and statistical models

Pombo, Nuno; Rebelo, Paulo; Araújo, Pedro; Viana, José Antônio

doi:10.1016/j.measurement.2016.07.009

Cited by 12 publications

(8 citation statements)

References 89 publications

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“…The output variable is a binary and/or categorical response Decision trees (DT) [24], [25], [26], [27], [28], [29], [16], [30], [31] Generates understandable rules with both categorical and continuous variables for prediction and classification purposes.…”

Section: Classificationmentioning

confidence: 99%

“…Generalized linear mixed models (GLMMs), least absolute shrinkage and selection operator (Lasso), Linear regression (LR), Logistic regression (LR), Bayesian regression [42], [32], [43], [33], [34], [35], [27], [44], [45], [46], [39], [41], [31] Model that estimates the relationship between one dependent variable and one or more independent variables using a line.…”

Section: Regressionmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2023

Preprint

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Background/objectivePain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.MethodsA comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including “Cancer”, “Pain”, “Pain Management”, “Analgesics”, “Opioids”, “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, and “Neural Networks” published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.ResultsThis systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients’ pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).ConclusionImplementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

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Section: Classificationmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…García-Laencina et al [12] proposed using k-nearest neighbor, mode, and expectation-maximization imputation for five-year survival prediction of breast cancer patients with unknown discrete values. Pombo, Rebelo, Araújo, and Viana [13] combined data imputation and statistics to design a clinical decision support system; the next year, they also proposed a patient-oriented method of a pain evaluation system [14] that produced tailored alarms, reports, and clinical guidance on the basis of collected patient-reported data, which was a clinical decision support systems.…”

Section: Medical Data Imputationmentioning

confidence: 99%

A novel clustering-based purity and distance imputation for handling medical data with missing values

Cheng

Huang

2021

Soft Comput

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Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation-maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly remove instances with missing values, this approach has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations, and so on.Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types.

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“…Burst and other fatigue failures in pipelines are detected (Choi et al, 2016: 1–12) by employing Kalman filter (KF) and its derivatives. There are better review works supporting expectation maximization (EM) and other related algorithms to deal the missing data problem for WDS application (Li et al, 2018: 39–44; Ye and Fenner, 2014: 417–424), traffic monitoring (Zefreh and Torok, 2018: 193–198) and health care monitoring (Leturiondo et al, 2017: 152–162; Pombo et al, 2018: 480–489). Significant contributions are also used to estimate the missing data instances using artificial intelligence (AI) techniques (Sincak et al, 2014: 8597–8611).…”

Section: Introductionmentioning

confidence: 99%

An enhanced dynamic soft sensor–based online estimation of missing data for water distribution system with inherent disturbances

Hussain

Sivakumaran

Sankaranarayanan³

et al. 2023

Transactions of the Institute of Measurement and Control

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Interruption of water supply is an unfavourable event in water distribution system (WDS). The interruptions are mainly due to water hammer (WH) transients that are caused by sudden pump/valve failures. In this paper, the effects of hammer are mathematically realized considering the effect of valve coefficient integrated with the system. Due to economic and technical limitations, the system is generally equipped with some obsolete instruments for continuous monitoring, which creates a missing data problem. The problem of missing data estimation could be addressed using a novel customized Kalman filter (CKF), a customized version of conventional Kalman filter (KF) based on process model. Initially, to avoid the intricacies of obtaining the optimal estimates of missing instants, the vacancies in the data are filled assigned by the average values of available adjacent data. The measured quantities are considered distorted with state-, input-dependent and independent uncertainties. The effect of WH on a prototypical WDS is studied for different modes of operation, and the missing data points are reconstructed for regular and irregular sampled data set using the proposed CKF. The performance of the proposed algorithm is also compared with the conventional extended Kalman filter (EKF). The study indicates that the proposed missing data estimation algorithm is highly reliable to identify the missing instances in the states of distribution system.

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Design and evaluation of a decision support system for pain management based on data imputation and statistical models

Cited by 12 publications

References 89 publications

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

A novel clustering-based purity and distance imputation for handling medical data with missing values

An enhanced dynamic soft sensor–based online estimation of missing data for water distribution system with inherent disturbances

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