New research in Scientific Reports conducted by Washington University shows how comprehending brain activity as a network rather than by electroencephalography readings, provides more accurate identification of epileptic seizures in real-time. The study, which mixes machine learning with systems theory, was steered by lead author Walter Bomela. "Our technique allows us to get raw data, process it and extract a feature that's more informative for the machine learning model to use," Bomela stated in a news release. "The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with much less computing resources." As explained by researchers, using an EEG, epileptic seizures can be observed through irregular brain activity in the form of spikes and waves during the measurement of electrical output.
A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body . An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases .
The rumours that AI (and ML) will revolutionise healthcare have been around for a while . And yes, we have seen some amazing uses of AI in healthcare [see, e.g., 2,3]. But, in my personal experience, the majority of the models trained in healthcare never make it to practice. Let's see why (or, scroll down and see how we solve it). Note: The statement "the majority of the models trained in … never make it to practice" is probably true across disciplines. Healthcare happens to be the one I am sure about.
IMAGE: This gif was recorded during two seizures, one at 2950 seconds, the other at 9200. The top left animation is of EEG signals from three electrodes. The top right is... view more Researchers from Washington University in St. Louis' McKelvey School of Engineering have combined artificial intelligence with systems theory to develop a more efficient way to detect and accurately identify an epileptic seizure in real-time. Their results were published May 26 in the journal Scientific Reports. The research comes from the lab of Jr-Shin Li, professor in the Preston M. Green Department of Electrical & Systems Engineering, and was headed by Walter Bomela, a postdoctoral fellow in Li's lab.
Deep learning (DL) models are known for tackling the nonlinearities associated with data, which the traditional estimators such as logistic regression couldn't. However, there is still a cloud of doubt with regards to the increased use of computationally intensive DL for simple classification tasks. To find out if DL really outperforms shallow models significantly, the researchers from the University of Pennsylvania experiment with three ML pipelines that involve traditional methods, AutoML and DL in a paper titled, 'Is Deep Learning Necessary For Simple Classification Tasks.' The UPenn researchers stated that a support-vector machine (SVM) model might predict more accurately susceptibility to a certain complex genetic disease than a gradient boosting model trained on the same dataset. Moreover, choosing different hyperparameters within that SVM model can vary performances.
Throughout this article, you will become good at spotting, understanding, and imputing missing data. We demonstrate various imputation techniques on a real-world logistic regression task using Python. Properly handling missing data has an improving effect on inferences and predictions. This is not to be ignored. The first part of this article presents the framework for understanding missing data.
I don't know whether you know it or not… but there are a lot of misconceptions surrounding artificial intelligence. While some assume it means robots coming to life to interact with humans, other ones believe it is a superintelligence that soon will take over the world. Well, I consider this to be very discouraging. Not for me to explain the importance of knowing what AI is and what it can really do (especially if you are thinking about establishing your own AI expertise, or you are already using it). Today, I offer to take care of terminology and don't be so naive anymore. In this article, I'll aim to highlight some of the most necessary concepts in a clear, straightforward way. So, feel free to grab your coffee and a comfortable chair, and just dive in.
Learn how to create Machine Learning model from scratch that uses Multinomial Logistic Regression. We are going to have a look at Multinomial Logistic Regression one of the classic supervised machine learning algorithms capable of doing multi-class classification, i.e., predict an outcome for the target variable when there are more than 2 possible discrete classes of outcomes. When it comes to real-world machine learning, around 70% of the problems are classification-based, where, on the basis of the available set of features, your model tries to predict that out of a given set of categories(discrete possible outcomes), what category does your target variable might belong to. This is a project-based guide, where we will see how to code an MLR model from scratch while understanding the mathematics involved that allows the model to make predictions. For the project, we will be working on the famous UCI Cleveland Heart Disease dataset.
Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. Many kinds of research have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is K-Means clustering algorithm. Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is.
Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability.