Prevalência e fatores associados ao uso de medicamento indutor do sono entre mulheres assistidas na Atenção Primária à Saúde: estudo transversal em Vitória, Espírito Santo, 2014

Leite, Franciéle Marabotti Costa; Xavier, Jasmine Cristina Soares; Silva, Ranielle de Paula; Wandekoken, Kallen Dettmann; Tavares, Fábio Lúcio; Amorim, Maria Helena Costa

doi:10.1590/s1679-49742022000100016

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, poor quality of sleep has been associated with decreased productivity at work and at school, increased stress, and decreased quality of life [16][17][18][19]. To combat the debilitating consequences of sleep disorders, a plethora of pharmacologic treatments have been introduced to the market and prescribed by physicians [20][21][22][23][24][25][26]. While medications have shown efficacy in decreasing sleep latency, significant side effects have been associated with these medications [27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

View full text Add to dashboard Cite

Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

show abstract

Section: Introductionmentioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

Prevalência e fatores associados ao uso de medicamento indutor do sono entre mulheres assistidas na Atenção Primária à Saúde: estudo transversal em Vitória, Espírito Santo, 2014

Cited by 1 publication

References 19 publications

Use of machine learning to identify risk factors for insomnia

Use of machine learning to identify risk factors for insomnia

Contact Info

Product

Resources

About