Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome

Sheyn, David; Ju, Mingxuan; Zhang, Sixiao; Anyaeche, Caleb; Hijaz, Adonis; Mangel, Jeffrey; Mahajan, Sahil; Conroy, Britt; El-Nashar, Sherif A.; Ray, Soumya

doi:10.1097/aog.0000000000003517

Cited by 19 publications

(22 citation statements)

References 34 publications

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“…However, as more time persists, factors such as the number of drinks, and the volume of alcohol and water consumed become more useful as determinants in predicting the next urination. This study’s findings suggest that factors such as a child’s bladder size or an older adult’s UI can significantly impact the prediction of the next urination, which is consistent with previous research [ 6 , 7 , 24 ]. These results are in line with the literature finding that age can affect how much and how long a person can hold their urine [ 4 , 6 , 7 , 23 , 24 ].…”

Section: Conclusion Limitations and Future Worksupporting

confidence: 92%

Mitigating urinary incontinence condition using machine learning

Ali

Ahmed

Olivos

et al. 2022

BMC Med Inform Decis Mak

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Background Urinary incontinence (UI) is the inability to completely control the process of releasing urine. UI presents a social, medical, and mental issue with financial consequences. Objective This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI. Method A total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time. The participants were instructed to record input data (such as the time of consumption and the number of drinks) and output data (i.e., the time the individual urinated). Other factors, such as age and BMI, were also considered. The study was conducted in two phases: (1) data was prepared for modeling, including missing values, data encoding, and scaling; and (2) a classification model was designed with four output classes of the next urination time: < = 30 min, 31–60 min, 61–90 min, > 90 min. The model was built in two steps: (1) feature selection and (2) model training and testing. Feature selection methods such as lasso regression, decision tree, random forest, and chi-square were used to select the best features, which were then used to train an extreme gradient boosting (XGB) algorithm model to predict the class of the next urination time. Result The feature selection steps resulted in nine features considered the most important features affecting UI. The accuracy, precision, recall, and F1 score of the XGB predictive model are 0.70, 0.73, 0.70, and 0.71, respectively. Conclusion This research is the first step in developing a machine learning model to predict when a person will need to urinate. A precise predictive instrument can enable healthcare providers and caregivers to assist people with various forms of UI in reliable, prompted voiding. The insights from this predictive model can allow future apps to go beyond current UI-related apps by predicting the time of urination using the most relevant factors that impact voiding frequency.

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Section: Conclusion Limitations and Future Worksupporting

confidence: 92%

Mitigating urinary incontinence condition using machine learning

Ali

Ahmed

Olivos

et al. 2022

BMC Med Inform Decis Mak

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“…The scoping review identified many studies that developed or validated models to predict therapeutic response for which prevention of ADEs in patients not expected to benefit from treatment was stated as a motivation for model development. 18 (27%) of 67 studies addressed this use case [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] and focused on antineoplastics to treat patients with cancer (four [22%] of 18 studies), or antivirals with or without immuno modulators to treat patients with HIV or hepatitis C (five [28%]). Eight (44%) of 18 studies evaluated a single AI model.…”

Section: Reviewmentioning

confidence: 99%

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Syrowatka

Song

Amato

et al. 2022

The Lancet Digital Health

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“…[31][32][33][34][35][36] Machine learning tools have also been deployed to predict the presence or absence of sperm in testicular biopsy in patients with nonobstructive azoospermia, pregnancy loss after vitro fertilization-embryo transfer, the response of patients with overactive bladder syndrome to anticholinergic medications, and disease recurrence in women with early-stage endometrial or cervical cancer. [37][38][39][40][41] Though promising, these models have not yet been deployed clinically.…”

Section: Clinical Applications Of Machine Learning In Obstetrics and ...mentioning

confidence: 99%

Introduction to Machine Learning in Obstetrics and Gynecology

Shazly

Trabuco

Ngufor

et al. 2022

Obstetrics &Amp; Gynecology

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Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome

Abstract: Personal or nonessential information may be redacted at the editor's discretion.

Cited by 19 publications

References 34 publications

Mitigating urinary incontinence condition using machine learning

Mitigating urinary incontinence condition using machine learning

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Introduction to Machine Learning in Obstetrics and Gynecology

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