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

EASL-EORTC clinical practice guidelines : management of hepatocellular carcinoma

Background and Aims:The lack of consensus on how to define and grade adverse postoperative events has greatly hampered the evaluation of surgical procedures. A new classification of complications, initiated in 1992, was updated 5 years ago. It is based on the type of therapy needed to correct the co

The Clavien-Dindo classification of surgical complications: five-year experience.

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Modern Applied Statistics With S

Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

Background: Perioperative hypothermia increases the morbidity of surgery However, the true incidence of hypothermia during prolonged laparoscopic surgery is still unknown To investigate this issue, we compared the temperature change between patients undergoing open and laparoscopic colorectal surgery

Core temperature changes during open and laparoscopic colorectal surgery.

Systematic review of endovenous laser therapy versus surgery for the treatment of saphenous varicose veins.

Background:

Ultrasonography during abdominal surgery has been reported since the 1960s, but its use did not become widespread until the recent availability of high-frequency, high-resolution transducers. This review discusses the application of intraoperative ultrasonography to open and laparoscopic abdominal surgery.



Methods:

A literature search (Medline) was undertaken. All papers pertaining to the subject matter that were located were included in the review.



Results:

Intraoperative ultrasonography influences surgical strategy in up to 50 per cent of liver resections for malignancy. It is the single most sensitive technique for the detection of occult hepatic metastases at the time of primary colorectal resection. In pancreatic surgery, intraoperative ultrasonography is of value in the localization of islet cell tumours and in the assessment of resectability of adenocarcinoma. The technique may also have a role in staging laparoscopy, and in the operative management of kidney and gastrointestinal diseases.



Conclusion:

Ultrasonography is an ideal operative tool as it is safe, reproducible and requires no special patient preparation or positioning. It should be regarded as an essential component of major hepatobiliary and pancreatic procedures. The recent availability of flexible laparoscopic probes is likely to lead to a similar impact on minimal access surgery. © 1999 British Journal of Surgery Society Ltd

Intraoperative abdominal ultrasonography

This systematic review assessed if outcomes in adult intensive care units (ICUs) are related to hospital and ICU patient volume. A systematic search strategy was used to identify studies reporting on volume–outcome relationship in adult ICU patients till November 2010. Inclusion of articles was established through a predetermined protocol. Two reviewers assessed studies independently and data extraction was performed using standardized data extraction forms. A total of 254 articles were screened. Of these 25 were relevant to this study. After further evaluation a total of 13 studies including 596,259 patients across 1,068 ICUs met the inclusion criteria and were reviewed. All were observational cohort studies. Four of the studies included all admissions to ICU, five included mechanically ventilated patients, two reported on patients admitted with sepsis and one study each reported on patients admitted with medical diagnoses and post cardiac arrest patients admitted to ICU, respectively. There was a wide variability in the quantitative definition of volume and classification of hospitals and ICUs on this basis. Methodological heterogeneity amongst the studies precluded a formal meta-analysis. A trend towards favourable outcomes for high volume centres was observed in all studies. Risk-adjusted mortality rates revealed a survival advantage for a specific group of patients in high volume centres in ten studies but no significant difference in outcomes was evident in three studies. The results indicate that outcomes of certain subsets of ICU patients—especially those on mechanical ventilation, high-risk patients, and patients with severe sepsis—are better in high volume centres within the constraints of risk adjustments.

Does patient volume affect clinical outcomes in adult intensive care units

Rodney Cooter, Wendy Babidge, Keith Mutimer, Peter Wickham, David Robinson, George Kiroff, Andrew Chapman and Guy Maddern

Guy J. Maddern

Papers

Core temperature changes during open and laparoscopic colorectal surgery.

Systematic review of endovenous laser therapy versus surgery for the treatment of saphenous varicose veins.

Intraoperative abdominal ultrasonography

Does patient volume affect clinical outcomes in adult intensive care units

Ultrasound-assisted lipoplasty