Machine Learning: Subfield Of Computer Science

Introduction

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in Artificial Intelligence(AI). Machine learning explores the study and construction of algorithms that can learn from and make guesses on data. Such algorithms operate by building a model from inputs to make data-driven predictions or guesses, rather than following strictly static program instructions.

Machine learning is also closely related to computational statistics, a discipline that aims at the design of algorithm for implementing statistical methods on computers.

Machine learning and statistics are closely related fields. The ideas of machine learning, from professional principles to theoretical

Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best comprehended. Supervised learning involves learning from a training set of data. Every point in the training is a pair of input-output sequence, where the input maps to an output. The learning problem consists of inferring the function that joins the input and the output in a predictive fashion, such that the learned function can be used to predict present output from future

Comparing clinical forecaster of deep venous thrombosis versus pulmonary embolus after severe injury: a new paradigm for posttraumatic venous thromboembolism.

INSPIRATION:

The orignal information we have is that deep venous thrombosis (DVT) and pulmonary embolus (PE) are different temporary phases of a single disease process, most often labeled as the composite end point venous thromboembolism (VTE). However, it was later seen that after severe blunt injury, DVT and PE may represent independent thrombotic disease rather than different stages of a single pathophysiologic process and therefore exhibit different clinical risk factor profiles.

METHODS:

A large, multicenter prospective cohort of severely injured blunt trauma patients was examined to compare clinical risk factors for DVT and PE, including indicators of injury severity, shock, resuscitation parameters, comorbidities, and VTE prophylaxis. Independent risk factors for each outcome were determined by cross-validated logistic regression modeling using advanced exhaustive model search procedures.