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IntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFA lifestyle educational course as an adjunct to biologic administration in patients with severe asthma: A feasibility study.
PEC Innov
June 2025
Department of Respiratory Medicine, Royal Devon and Exeter Hospital, University of Exeter, Exeter, United Kingdom.
Objective: To assess the feasibility and acceptability of adapting a psychoeducation course (Body Reprogramming) for severe asthma and finding suggestions for improvement.
Methods: Severe asthma patients were recruited from a single centre and enrolled in an online group-based course. Each course consisted of four sessions: introduction to BR, stress, exercise, and diet.
Unveiling a Large Pelvic Mass in a Middle-Aged Woman: The Consequences of Neglected Gynecological Care.
Cureus
December 2024
Obstetrics and Gynecology, Cape Fear Valley Health, Fayetteville, USA.
Pelvic masses in women can originate from both gynecological and non-gynecological sources, necessitating careful evaluation to ensure appropriate treatment. Gynecological masses can range from functional ovarian cysts and tubo-ovarian abscesses to malignant and benign tumors. This case report presents a mucinous borderline ovarian tumor (BOT), a rare type of ovarian neoplasm.
View Article and Find Full Text PDF[Bronchiectasis in patients with severe asthma : a narrative literature review].
Rev Med Liege
January 2025
Service de Pneumologie, CHU Liège, Belgique.
Asthma is a common respiratory disease, accounting for 3 to 10 % of severe cases. Among these, bronchiectasis is more frequent (prevalence between 15.5 % and 67.
View Article and Find Full Text PDFCausal analysis for multivariate integrated clinical and environmental exposures data.
BMC Med Inform Decis Mak
January 2025
Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Electronic health records (EHRs) provide a rich source of observational patient data that can be explored to infer underlying causal relationships. These causal relationships can be applied to augment medical decision-making or suggest hypotheses for healthcare research. In this study, we explored a large-scale EHR dataset on patients with asthma or related conditions (N = 14,937).
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