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 Appendicectomy is a safe surgical procedure with minimal risks. Low mortality rates (0.04-0.24%) have been reported from a number of countries. Appendicectomy mortality rates have not been reported in Australia in recent years. The aim of the study was to conduct an appendicectomy mortality audit, to identify clinical management issues and to calculate the mortality rate of appendicectomy in Australia. METHODS This study analysed data from a peer review of appendicectomy deaths in Australian hospitals of all states and territories, from 2006 to 2017. Additional procedural data were obtained from the Australian Institute of Health and Welfare for the calculation of mortality rates. RESULTS The final analysis included 82 patients of whom 62 were elderly (age >65 years) with 47 males and 35 females. Two or more comorbidities were present in 68 patients. Open operation was performed in 62 patients, with complicated appendicitis found in 50 cases. The majority of management issues were non-surgical. Of the 16 surgical management issues identified, 11 issues occurred post-operatively in the elderly, which were potentially preventable. The overall mortality was 0.02% with 0.01% in the young (age <65 years) and 0.20% in the elderly. CONCLUSION Australia's appendicectomy mortality rate was very low and comparable to international figures. Surgical management of appendicectomy was satisfactory in general. However, post-operative care in the elderly was found to be poor at times, with room for improvement. Further research is needed whether improvements in post-operative care could alter the clinical course to death in the elderly.

Appendicectomy mortality: an Australian national audit.

/pdf/covid-19-and-the-ukraine-russia-conflict-warnings-from-3v0dobw9.pdf

COVID-19 and the Ukraine–Russia conflict: warnings from history

Mitchell raises issues that strongly reflect on the professional standing of the medical profession. His particular focus is on the pharmaceutical industry, and on much needed changes as highlighted by Medicines Australia.2 Similar concerns exist in the medical technology industry where the transparency of relationships is becoming increasingly important. As professionals, these issues of integrity, transparency and trust are fundamental. The doctor–patient relationship must be our primary focus, and should not be violated by any perceived or real conflict of interest.

Winds of change: growing demands for transparency in the relationship between doctors and the pharmaceutical industry.

An assessment of outcome has been made after highly selective vagotomy performed by two surgeons on 174 patients The median time to followup was 36 months (range 3-94) The group comprised 74% males and 26% females (median age 45 years, range 19-75) Assessment was by review of medical history, endoscopy (irrespective of presence or absence of symptoms) and questionnaire Symptomatic ulcer recurrence had already been diagnosed in 16 patients (9%) There were 2 unassociated deaths and 14 patients remained untraced The remaining 142 patients were assessed by questionnaire and asked to undergo an endoscopy One hundred and nineteen agreed to endoscopy and ulcers were found in 11 patients (9%) Five were sited in the duodenum and 6 in the stomach Duodenitis was found in 30% of patients Patients with ulcers in the pyloric region were at significantly greater risk than the general group for developing a recurrence Patients who agreed to endoscopy (119) underwent Visick grading prior to endoscopy A poor correlation was found between ulcer recurrence and Visick score The "asymptomatic" recurrence rate of 9% following highly selective vagotomy should be considered when assessing the outcome of the surgery

Guy J. Maddern

Papers

Appendicectomy mortality: an Australian national audit.

COVID-19 and the Ukraine–Russia conflict: warnings from history

Winds of change: growing demands for transparency in the relationship between doctors and the pharmaceutical industry.

Symptomatic and asymptomatic ulcer recurrence following highly selective vagotomy.

Endoscopic retrograde cholangiopancreatography-induced haemolytic uraemic syndrome.