Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Objective:To provide an update to the “Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock,” last published in 2008.Design:A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at ke

Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012.

2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC)

/pdf/2017-esc-guidelines-for-the-management-of-acute-myocardial-14hhjrfylv.pdf

2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC).

ACE
: angiotensin-converting enzyme
ACS
: acute coronary syndrome
ADP
: adenosine diphosphate
AF
: atrial fibrillation
AMI
: acute myocardial infarction
AV
: atrioventricular
AIDA-4
: Abciximab Intracoronary vs. intravenously Drug Application
APACHE II
: Acute Physiology Aand Chronic

/pdf/esc-guidelines-for-the-management-of-acute-myocardial-vy5rt9aiqe.pdf

ESC Guidelines for the Management of Acute Myocardial Infarction in Patients Presenting With ST-Segment Elevation

ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation : The Task Force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC).

/pdf/2015-esc-guidelines-for-the-management-of-acute-coronary-6did6h5nxk.pdf

2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: Task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC)

Su1078 The Symptom Association Probability (SAP) is Not Reliable in Patients With Documented Acid Reflux Who are Unresponsive to PPI Therapy

1007 Comparison of Upper and Lower Gastrointestinal Bleeding Risk Assessment Tools in Consecutive Patients With Hematochezia: The Glasgow-Blatchford Score Provides the Best Risk Stratification

577 A Prospective Randomized Trial of Aggressive Hydration to Prevent Post-ERCP Pancreatitis

Non-selective NSAIDs and COX-2 selective inhibitors: GI risk and use of proton pump inhibitor prophylaxis

Outcomes with Bloody vs. Coffee-Grounds Hematemesis.

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