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DD- Learning To F.avi [VERIFIED]

Machine learning (ML) approaches have emerged as powerful tools in medicine. This review focuses on the use ML to assess risk of events in patients with heart failure (HF). It provides an overview of the ML process, challenges in developing risk scores, and strategies to mitigate problems.

DD- learning to f.avi

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Machine learning approaches can be used to develop risk scores that are superior to ones based on standard statistical methods. Careful attention to detail in curating data, selecting covariates, and trouble-shooting the process is required to optimize results.

Machine learning (ML) is a subset of artificial intelligence that is defined as the study of computer algorithms that improve automatically through experience. These algorithms are built on sample training data with the goal of making predictions or decisions without being explicitly programmed for this purpose. Machine learning enables computerized analysis of large amounts of data over relatively short periods of time and can be used to address questions in a variety of fields including medicine. There are several different approaches to ML. These include unsupervised learning which uses only input data to find grouping between the data points and supervised learning that builds models from training data sets containing both inputs and desired outputs.

Simple visualization how a machine learning algorithm is able to learn how to distinguish between two populations. Two computer-generated populations (a) red and (b) blue, characterized by a pair of covariates (X,Y). A Neural Network (NN) is trained to distinguish between the two. For each (X,Y) pair, the NN provides a score that can be used in this or a different population to give a red vs. blue probability. In panel (c) the output of the NN is visualized (in a third dimension - color) as a function of (X,Y) from deep red (very likely red) to deep blue (very likely blue). As additional covariates are added. Separation of the populations increases.

Researchers have also applied ML to predict morbidity and mortality in patients with known HF (Table 1). Although numerous models have been created to predict HF readmissions, most have demonstrated only limited discriminative properties. Frizzell et al. used several ML algorithms to predict all-cause readmissions 30 days after discharge from a HF hospitalization in patients included in the GWTG-HF registry [22]. All of the models developed in this study showed modest discriminatory power, with C statistics consistently around 0.62. Awan et al. demonstrated a similar AUC of 0.62 using a multi-layer perceptron-based approach to predict risk of 30 day HF readmission or death in a population of patients above age 65 years admitted with HF [25]. Golas et al. were able to demonstrate modest improvement in risk prediction using several deep learning algorithms in a population of HF patients admitted within a large healthcare system [24]. The model developed using deep unified networks from >3500 variables from the electronic health record (EHR) demonstrated the best performance with an AUC of 0.705 for prediction of 30-day readmission. To our knowledge, these models have not been externally validated nor directly compared to traditional risk assessment tools, so it is not known if they can be applied to broad HF populations.

Finally, although this review has focused on the use of ML to develop hospitalization and mortality risk scores for clinical events in patients with HF, they can be applied to a variety of situations in either this or other populations. There is great need for novel approaches for calculating risk of other adverse events known to occur in patients with HF such as stroke, atrial fibrillation and sudden cardiac death as well as the risk of adverse consequences of specific therapies designed to treat these conditions. Machine learning approaches may also be useful in future clinical trials in HF by helping to determine which patients to enroll.

Background: Occupational therapists working with school-aged children are often in the position of recommending technology to enhance written productivity. The outcome of using technology on the writing of children with learning disabilities has not been reviewed critically, and this knowledge is necessary for evidence-based practice.

Associate Professor Curtin Medical School, Curtin University. Emergency physician MA (Oxon) MBChB (Edin) FACEM FFSEM Sir Charles Gairdner Hospital. Passion for rugby; medical history; medical education; and asynchronous learning #FOAMed evangelist. Co-founder and CTO of Life in the Fast lane Eponyms Books Twitter 041b061a72

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