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

Airway fire during formation of tracheostomy.

Background: Surgical decision-making tools may help surgeons achieve better outcomes by providing more personally relevant information to patients. This paper describes approaches to developing statistical tools capable of estimating the probability of morbidity and mortality after cardiovascular surgery. Our aim is to inform surgeons about the important stages that contribute to the development of decision tools.



Methods:  The key elements described include study design (data quality, cohort size, etc.) and statistical methodology for developing and testing decision tools. Mention is made of the delivery of decision tools, simplicity of use, ease of interpretation of results and accessibility. Information specific to cardiac and vascular surgery is included.



Results:  Development of useful and effective decision tools is dependent on robust and reliable data, unambiguous outcome requirements and considerable statistical expertise. Decision tools must also be extensively tested for validity and reliability, both internally and with external data.



Conclusion:  Understanding the development and assumptions that underlie surgical decision tool development will help cardiovascular surgeons appreciate the value of applying such techniques at a clinical level.

Developing tools to predict outcomes following cardiovascular surgery.

Background: Inflammatory conditions of the biliary tree are infrequently reported with the exception of sclerosing cholangitis. These conditions, especially when affecting the common bile duct (CBD), can be mistaken for tumours; their existence should be considered in the differential diagnosis of biliary lesions and, in particular, biliary strictures. Few descriptions of inflammatory conditions exist in the published literature.



Methods: A comprehensive search was undertaken of the last 20 years of published literature.



Results: Potential aetiological factors include infection, infestation, traumatic bile duct injury, foreign body or gallstone reaction and the host inflammatory response.



Conclusions: These conditions typically mimic malignant tumours of the CBD but knowledge of the aforementioned processes might alter the surgical approach to biliary strictures (especially after previous cholecystectomy), with greater emphasis on surgical exploration.

Inflammatory conditions of the common bile duct

Simple Summary Cancer cells frequently have an altered metabolism to support their increased proliferative and invasive activity. Perhexiline, a drug used to treat some cardiovascular diseases, inhibits some of the reported changes in the metabolism of cancer cells. We show that treatment with this drug either as a racemate or its enantiomers can kill colorectal cancer cells. The drug has been used clinically for a long time and has potential to be repurposed for use in the management of colorectal cancer. Abstract Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Perhexiline, a prophylactic anti-anginal drug, has been reported to have anti-tumour effects both in vitro and in vivo. Perhexiline as used clinically is a 50:50 racemic mixture ((R)-P) of (−) and (+) enantiomers. It is not known if the enantiomers differ in terms of their effects on cancer. In this study, we examined the cytotoxic capacity of perhexiline and its enantiomers ((−)-P and (+)-P) on CRC cell lines, grown as monolayers or spheroids, and patient-derived organoids. Treatment of CRC cell lines with (R)-P, (−)-P or (+)-P reduced cell viability, with IC50 values of ~4 µM. Treatment was associated with an increase in annexin V staining and caspase 3/7 activation, indicating apoptosis induction. Caspase 3/7 activation and loss of structural integrity were also observed in CRC cell lines grown as spheroids. Drug treatment at clinically relevant concentrations significantly reduced the viability of patient-derived CRC organoids. Given these in vitro findings, perhexiline, as a racemic mixture or its enantiomers, warrants further investigation as a repurposed drug for use in the management of CRC.

/pdf/the-antianginal-drug-perhexiline-displays-cytotoxicity-2gg88ge3.pdf

The Antianginal Drug Perhexiline Displays Cytotoxicity against Colorectal Cancer Cells In Vitro: A Potential for Drug Repurposing

Context Postoperative recovery rooms have existed since 1847, however, there is sparse literature investigating interventions undertaken in recovery, and their impact on patients after recovery room discharge. Objective This review aimed to investigate the organisation of care delivery in postoperative recovery rooms; and its effect on patient outcomes; including mortality, morbidity, unplanned intensive care unit (ICU) admission and length of hospital stay. Data sources NCBI PubMed, EMBASE and Cumulative Index to Nursing and Allied Health Literature. Study selection Studies published since 1990, investigating health system initiatives undertaken in postoperative recovery rooms. One author screened titles and abstracts, with two authors completing full-text reviews to determine inclusion based on predetermined criteria. A total of 3288 unique studies were identified, with 14 selected for full-text reviews, and 8 included in the review. Data extraction EndNote V.8 (Clarivate Analytics) was used to manage references. One author extracted data from each study using a data extraction form adapted from the Cochrane Data Extraction Template, with all data checked by a second author. Data synthesis Narrative synthesis of data was the primary outcome measure, with all data of individual studies also presented in the summary results table. Results Four studies investigated the use of the postanaesthesia care unit (PACU) as a non-ICU pathway for postoperative patients. Two investigated the implementation of physiotherapy in PACU, one evaluated the use of a new nursing scoring tool for detecting patient deterioration, and one evaluated the implementation of a two-track clinical pathway in PACU. Conclusions Managing selected postoperative patients in a PACU, instead of ICU, does not appear to be associated with worse patient outcomes, however, due to the high risk of bias within studies, the strength of evidence is only moderate. Four of eight studies also examined hospital length of stay; two found the intervention was associated with decreased length of stay and two found no association. PROSPERO registration number This protocol is registered on the International Prospective Register of Systematic Reviews (PROSPERO) database, registration number CRD42018106093.

https://bmjopen.bmj.com/content/bmjopen/10/3/e027262.full.pdf

'Organisation of delivery of care in operating suite recovery rooms within 48 hours postoperatively and patient outcomes after adult non-cardiac surgery: a systematic review'

Guy J. Maddern

Papers

Airway fire during formation of tracheostomy.

Developing tools to predict outcomes following cardiovascular surgery.

Inflammatory conditions of the common bile duct

The Antianginal Drug Perhexiline Displays Cytotoxicity against Colorectal Cancer Cells In Vitro: A Potential for Drug Repurposing

'Organisation of delivery of care in operating suite recovery rooms within 48 hours postoperatively and patient outcomes after adult non-cardiac surgery: a systematic review'