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

752Background: With improved healthcare we now face an ageing population worldwide. More patients with metastatic colorectal cancer (mCRC) will present at advanced age. Patients with mCRC now have the potential of living longer due to surgery, chemotherapeutic agents and monoclonal antibodies. Australian data (2011) indicated 1.3% of the population are aged over 90 years*. Medical oncologists are now being referred patients in their 90’s and the optimal management for this group is unknown. Methods: The population based South Australian Clinical Registry for mCRC includes all patients with metastatic CRC diagnosed since the 1st February 2006. We examined cancer characteristics, treatments administered and outcomes for patients aged > 90 years. Results: 130 patients of 4199 (3%) were aged 90 years or older. The median age was 92.1 years (range 90-104.8 years). 61% were female, 70% presented with synchronous disease. Organ involvement was as follows; 58% liver, 32% lung, 8% peritoneal and 7% bone. Primary s...

Nonagenarian patients with metastatic colorectal cancer: Results from the South Australian metastatic colorectal cancer registry.

Bimodal electric tissue ablation (BETA): a study on ablation size when the anode is placed on the peritoneum and the liver.

Despite advances in medical management and endoscopic therapy, gastrectomy remains an important yet high‐risk procedure for a range of benign and malignant upper gastrointestinal pathologies. No study has previously analysed Australian gastrectomy perioperative mortality rate (POMR). This retrospective, population‐based cohort study was conducted to determine the Australian national gastrectomy POMR, allowing state‐based and regional trends and outcomes to be assessed.

Gastrectomy mortality in Australia

BACKGROUND
Interhospital transfers of surgical patients are an independent risk factor for mortality. The Australian and New Zealand Audit of Surgical Mortality (ANZASM) aims to improve surgical care through assessment of all cases of surgical mortality. This study aimed to describe common clinical management issues that contributed to interhospital transfer patient mortality.


METHODS
Data for all surgical patient mortality in Australia (except New South Wales) that underwent interhospital transfer between 1 January 2010 and 31 December 2019 were extracted from ANZASM. The surgeons' reports and assessors' evaluations were examined to identify clinical management issues. Thematic analysis was performed to develop pertinent themes and subthemes.


RESULTS
Some 8679 patients were identified over the 10-year period. Of these, 2171 (25.0 per cent) had 3259 clinical management issues identified. Prominent themes were operative design (n = 466, 14.3 per cent), decision to operate (n = 425, 13.0 per cent), medical conditions (n = 344, 10.6 per cent), diagnosis (n = 326, 10 per cent), transfer (n = 293, 10.0 per cent), intraoperative issues (n = 278, 8.5 per cent), inadequate assessment (n = 238, 7.3 per cent), communication (n = 224, 6.9 per cent), delay in recognizing complications (n = 180, 5.5 per cent), coagulopathy (n = 151, 4.6 per cent), insufficient monitoring (n = 127, 3.9 per cent), infection (n = 107, 3.3 per cent), and hospital resources (n = 100, 3.1 per cent). Assessors considered 58.4 per cent of clinical management issues (n = 1903) probably or definitely preventable.


CONCLUSION
This study identified 13 themes of potentially avoidable management issues present in surgical mortality following interhospital transfers. Quality-improvement initiatives targeting these areas may improve surgical patient outcomes.

Guy J. Maddern

Papers

Nonagenarian patients with metastatic colorectal cancer: Results from the South Australian metastatic colorectal cancer registry.

Bimodal electric tissue ablation (BETA): a study on ablation size when the anode is placed on the peritoneum and the liver.

Gastrectomy mortality in Australia

Surgical interhospital transfer mortality: national analysis.

Metastatic malignant fibrous histiocytoma: a rare cause of small bowel intussusception