Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes.
Machine Learning (Paper)
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks.
This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.
Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.
Phyton’s most notable points are:
-Is a great library ecosystem (Scikit-learn, Pandas, Matplotlib, NLTK, Scikit-image, PyBrain, Caffe, StatsModels, TensorFlow, Keras, etc).
-Has a low entry barrier, has flexibility, is a platform independence, has readability, good visualization options, good community support and growing popularity.
“dabl tries to reduce the turnaround time required for a quick baseline estimate of a supervised learning problem. It does so by automating the task of iterating through different techniques of data preprocessing, feature engineering, parameter tuning and model building to generate efficacious baseline models”.
(Deep Neural Networks in Geophysics) Esta tesis investiga las propiedades fundamentales de las redes neuronales en aplicaciones geofísicas. Incluye la reutilización de redes neuronales entrenadas, que son excelentes para identificar imágenes y aplicarlas para identificar capas de rocas y eventos geológicos en imágenes geofísicas. Esta tesis profundiza para evaluar si la teoría de incluir información específica de …