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 The data within the Australian and New Zealand Audit of Surgical Mortality (ANZASM) provides a unique opportunity to consider the contributing factors to perioperative deaths as determined by peer review. Consideration of the factors contributing to mortality after hepatectomy can provide greater insight into how deaths can be prevented. The objective of this study was to determine the reasons for patient deaths post-hepatectomy in Australia. Methods ANZASM data from 1 January 2010 to 30 Jun 2017 was reviewed and all deaths following hepatectomy were selected for analysis. Assessors determinations of whether management could have been improved were reviewed, and then classified into groups of significant clinical events using thematic analysis with a data driven approach. Results The study included 88 deaths reported to ANZASM after hepatectomy. The assessors questioned the decision to operate in 23/88 (25%) patients with a further nine (10%) patients insufficiently investigated prior to resection. ANZASM assessors determined that there was a delay in recognising a significant complication in 16/88 (18%) patients. Conclusion Multi-disciplinary decision making is strongly recommended when deciding which patients to treat with hepatic resection. Optimal care post-hepatectomy includes early recognition of complications and enactment of an adequate rescue plan.

Peer review of mortality after hepatectomy in Australia

Bimodal electric tissue ablation (BETA)--effect of reversing the polarity of the direct current on the size of ablation.

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Guy J. Maddern

Papers

Peer review of mortality after hepatectomy in Australia

Bimodal electric tissue ablation (BETA)--effect of reversing the polarity of the direct current on the size of ablation.

Rural Surgery: Challenges and Solutions for the Rural Surgeon

Brain metastasis in advanced colorectal cancer: Results from the South Australian metastatic colorectal cancer (SAmCRC) registry

A temporary chef de clinique.