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

Author's Reply: Impact of Aging on Hepatic Malignancy Resection: is Age Really a Risk Factor for Mortality?

Surgical education has changed; the classical ‘see one, do one, teach one’ philosophy has become increasingly hard to practice. Simulation-based education (SBE) has evolved as an effective tool to address the learning needs of healthcare professionals while maintaining the health and safety of patients. The quality and size of published evidence showing the utility of SBE and its role in the acquisition of both technical and non-technical surgical skills continues to increase. However, although many simulated skills courses have proven to be beneficial,1 2 the optimum format for the delivery of laparoscopic simulation training is still unclear.

To better understand the role of training formats in SBE, the Royal Australasian College of Surgeons (RACS) implemented the Laparoscopic Simulation Skills Program (LSSP). The aim of the LSSP was to determine the best format for delivery of simulated laparoscopic skills training. The LSSP delivered a technical skills training programme in metropolitan and regional Australia. As part of this programme, a questionnaire was used to record participants’ perceptions on the utility of laparoscopic simulation training, barriers and motivators for attending training, as well as preferences for SBE delivery. The results of this survey are presented in this article to examine how participants’ opinions and choices direct their participation in the programme.

A postcourse questionnaire was …

https://stel.bmj.com/content/bmjstel/6/4/245.full.pdf

Examination of participant preferences in the design and delivery of simulation-based education programmes

Background The number of clinical studies evaluating the benefit of cytokine-induced killer cell (CIK) therapy, an adoptive immunotherapy, for colorectal cancer (CRC) is increasing. In many of these trials, CIK therapy was coadministered with conventional cancer therapy. The aim of this review is to systematically assess the available literature, in which the majority were only in Chinese, on CIK therapy for the management of CRC using meta-analysis and to identify parameters associated with successful CIK therapy implementation. Methods Prospective and retrospective clinical studies which compared CIK therapy to non-CIK therapy in patients with CRC were searched for electronically on MEDLINE, Embase, China National Knowledge Infrastructure, and Wanfang Data databases. The clinical endpoints of overall survival (OS), progression-free survival (PFS), OS and PFS rates, overall response rate (ORR), and toxicity were meta-analyzed using HR and relative ratio (RR), and subgroup analyses were performed using chi-square (χ2) test and I-squared (I2) statistics for study design, disease stage, cotherapy type, and timing of administration. Results In total, 70 studies involving 6743 patients were analyzed. CIK therapy was favored over non-CIK therapy for OS (HR=0.59, 95% CI: 0.53 to 0.65), PFS (HR=0.55, 95% CI: 0.47 to 0.63), and ORR (RR=0.65, 95% CI: 0.57 to 0.74) without increasing toxicity (HR=0.59, 95% CI: 0.16 to 2.25). Subgroup analyses on OS and PFS by study design (randomized vs non-randomized study design), disease stage (Stage I–III vs Stage IV), cotreatment with dendritic cells (DCs) (CIK vs DC-CIK therapy), or timing of therapy administration (concurrent vs sequential with coadministered anticancer therapy) also showed that the clinical benefit of CIK therapy was robust in any subgroup analysis. Furthermore, cotreatment with DCs did not improve clinical outcomes over CIK therapy alone. Conclusion Compared with standard therapy, patients who received additional CIK cell therapy had favorable outcomes without increased toxicity, warranting further investigation into CIK therapy for the treatment of CRC.

/pdf/use-of-cytokine-induced-killer-cell-therapy-in-patients-with-en9nogwu.pdf

Use of cytokine-induced killer cell therapy in patients with colorectal cancer: a systematic review and meta-analysis

Acquisition of specific laparoscopic skills for laparoscopic hepatopancreatobiliary surgery

Editorial (Manuscript invitation only) states and territories, and since the state and territory jurisdict Australian and New Zealand A (ANZASM). According to the 2 Australian public hospitals and were participating in the audit.6 Private hospitals in New Sou have been slow to embrace the a Without comprehensive audit, it will remain difficult to highlight problems leading to poor surgical outcomes

Guy J. Maddern

Papers

Author's Reply: Impact of Aging on Hepatic Malignancy Resection: is Age Really a Risk Factor for Mortality?

Examination of participant preferences in the design and delivery of simulation-based education programmes

Use of cytokine-induced killer cell therapy in patients with colorectal cancer: a systematic review and meta-analysis

Acquisition of specific laparoscopic skills for laparoscopic hepatopancreatobiliary surgery

Public reporting of surgeons' performance.