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)

Bleeding With Direct Oral Anticoagulants: The Gastrointestinal Tract and Beyond.

The influence of size or number of biopsies on rapid urease test results: A prospective evaluation

GOALS To evaluate gastroenterologists' use of esophagogastroduodenoscopy (EGD) for positive fecal occult blood test (FOBT). BACKGROUND Colonoscopy is recommended when an FOBT performed for colorectal cancer screening is positive. Guidelines suggest no further evaluation if anemia and gastrointestinal (GI) symptoms are absent. METHODS Online surveys included 4 vignettes: positive FOBT in average-risk adults 50 years of age or older with/without iron-deficiency anemia and with/without upper GI symptoms. For each scenario, respondents were asked if they would perform colonoscopy only, EGD only, colonoscopy+EGD on same day, or colonoscopy followed by EGD on different day if colonoscopy was negative. RESULTS Surveys were returned by 778 (11%) of 7094 potential responders. In patients without anemia or upper GI symptoms, 65% performed colonoscopy only; 35% added EGD (9% same day, 25% different day). EGD was added in 91% with anemia, 96% with symptoms, and 100% with anemia+symptoms. In patients with positive FOBT alone (no symptoms or anemia), multivariate analysis revealed fear of litigation as the primary factor associated with adding EGD to colonoscopy (odds ratio=4.1; 95% confidence interval, 2.3-7.3). When EGD+colonoscopy were planned for positive FOBT, private practice was associated with performing EGD on a different day (odds ratio=6.3; 95% confidence interval, 2.9-13.5 for private versus academic setting). CONCLUSIONS One third of gastroenterologists perform EGD in addition to colonoscopy for a positive FOBT alone. Fear of litigation is the most important factor in deciding whether to add EGD to colonoscopy. When both procedures are planned, they are more likely to be performed on different days in a private practice setting than in an academic setting.

Gastroenterologists' practice patterns for positive fecal occult blood test.

Randomized comparison of ligation VS. Ligation plus sclerotherapy for esophageal variceal bleeding

Is there a role for combined sclerotherapy and ligation in the endoscopic treatment of gastroesophageal varices

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