RESUMEN
Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model's efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model's performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives.
Asunto(s)
Neoplasias de la Mama , Aprendizaje Automático , Humanos , Neoplasias de la Mama/diagnóstico por imagen , Femenino , Algoritmos , Sensibilidad y Especificidad , Diagnóstico por Computador/métodos , Redes Neurales de la ComputaciónRESUMEN
Stuck valve is a very rare and severe complication that occurs in mechanical valve replacement patients with ineffective anticoagulation. However, with COVID-19 restriction measures, it became challenging to regularly assess INR to make sure it falls within the target therapeutic range to prevent this complication. We present a series of 10 patients who either underwent transthoracic echocardiography for a suspected stuck valve or were seen at the outpatient valve clinic with the residual consequences of a stuck valve during the COVID-19 restriction measures in our institute. Stuck prosthetic valves incident has increased significantly during this period, particularly those in the mitral position for which urgent replacement and prolonged hospitalization were necessary. Particularly with the COVID-19 restrictions in place, these cases highlight the need for physicians to be aware of the dramatic increase in the incidence of stuck prosthetic valves in patients on chronic warfarin therapy.