A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach

Tsai, Pao‐Feng; Wang, Chih-Hsuan; Zhou, Yang; Ren, Jiaxiang; Jones, Alisha L.; Watts, Sarah O.; Chou, Chiahung; Ku, Wei‐Shinn

doi:10.1016/j.apnr.2021.151504

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…ANN are often used for big data analysis, so further analysis should be continued with more subjects. Studies using ANN for small samples have been published, so we should continue to monitor studies using ANN 34 , 35 . This study was able to extract factors that predict low levels of physical activity based on reliable physical activity assessment results in community-dwelling older adults with CP.…”

Section: Discussionmentioning

confidence: 99%

Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study

Hida,

Imai,

Nakamura

et al. 2023

Sci Rep

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Low levels of physical activity in individuals with chronic pain can lead to additional functional impairment and disability. This study aims to investigate the predictors of low physical activity levels in individuals with chronic pain, and to determine the accuracy of the artificial neural network used to analyze these predictors. Community-dwelling older adults with chronic pain (n = 103) were surveyed for their physical activity levels and classified into low, moderate, or high physical activity level groups. Chronic pain-related measurements, physical function assessment, and clinical history, which all influence physical activity, were also taken at the same time. Logistic regression analysis and analysis of multilayer perceptron, an artificial neural network algorithm, were performed. Both analyses revealed that history of falls was a predictor of low levels of physical activity in community-dwelling older adults. Multilayer perceptron analysis was shown to have excellent accuracy. Our results emphasize the importance of fall prevention in improving the physical activity levels of community-dwelling older adults with chronic pain. Future cross-sectional studies should compare multiple analysis methods to show results with improved accuracy.

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Section: Discussionmentioning

confidence: 99%

Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study

Hida,

Imai,

Nakamura

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In the ELIPSIS study, the authors also found a statistically significant reduction in average daytime activity during VOC compared to the days without pain in the home setting (28). In Tsai et al, daily step count was a unique predictor for pain intensity and pain interference in patients with chronic pain (29). This potentially can be explained by the fact that patients who were in pain are often less active due to the pain (30).…”

Section: Related Workmentioning

confidence: 98%

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Vuong,

Utkarsh,

Stojancic

et al. 2023

Front. Digit. Health

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BackgroundIn sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable.MethodsPatients with SCD (≥18 years) who were admitted for a vaso-occlusive crisis were enrolled at a single academic center. Participants were instructed to report daily pain scores (0–10) in a mobile app (Nanbar) and to continuously wear an Apple Watch up to 30 days after discharge. Data included heart rate (in rest, average and variability) and step count. Demographics, SCD genotype, and details of hospitalization including pain scores reported to nurses, were extracted from electronic medical records. Physiological data from the wearable were associated with pain scores to fit 3 different machine learning classification models. The performance of the machine learning models was evaluated using: accuracy, F1, root-mean-square error and area under the receiver-operating curve.ResultsBetween April and June 2022, 19 patients (74% HbSS genotype) were included in this study and followed for a median time of 28 days [IQR 22–34], yielding a dataset of 2,395 pain data points. Ten participants were enrolled while hospitalized for VOC. The metrics of the best performing model, the random forest model, were micro-averaged accuracy of 92%, micro-averaged F1-score of 0.63, root-mean-square error of 1.1, and area under the receiving operating characteristic curve of 0.9.ConclusionOur random forest model accurately predicts high pain scores during admission for VOC and after discharge. The Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of pain scores.

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“…Stepwise regression is a popular approach [18,19] and most statistical software offer it, which clearly illustrates its demand and ironically may inspire researchers to adopt it (Table 4). Efroymson [20] presented automated stages to choose the explanatory factors for a multiple regression model from a set of candidate variables.…”

Section: Input Data Analysismentioning

confidence: 99%

Accuracy of Hourly Demand Forecasting of Micro Mobility for Effective Rebalancing Strategies

Boonjubut

Hasegawa

2022

Management Systems in Production Engineering

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The imbalance in bike-sharing systems between supply and demand is significant. Therefore, these systems need to relocate bikes to meet customer needs. The objective of this research is to increase the efficiency of bike-sharing systems regarding rebalancing problems. The prediction of the demand for bike sharing can enhance the efficiency of a bike-sharing system for the operation process of rebalancing in terms of the information used in planning by proposing an evaluation of algorithms for forecasting the demand for bikes in a bike-sharing network. The historical, weather and holiday data from three distinct databases are used in the dataset and three fundamental prediction models are adopted and compared. In addition, statistical approaches are included for selecting variables that improve the accuracy of the model. This work proposes the accuracy of different models of artificial intelligence techniques to predict the demand for bike sharing. The results of this research will assist the operators of bike-sharing companies in determining data concerning the demand for bike sharing to plan for the future. Thus, these data can contribute to creating appropriate plans for managing the rebalancing process.

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A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach

Cited by 6 publications

References 39 publications

Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study

Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease

Accuracy of Hourly Demand Forecasting of Micro Mobility for Effective Rebalancing Strategies

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