2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.

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

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Data Mining Practical Machine Learning Tools and Techniques

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2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure.

We describe the case of a 70-year-old woman with persistent acantholytic dermatosis The peculiarity of the case lies in the unusual localization of this dermatosis on the lower lip A possible relationship with chronic solar damage is discussed

Persistent acantholytic dermatosis of the lip related to solar damage

Use of loop diuretics in chronic heart failure: do we adhere to the Hippocratian principle 'do no harm'?

Evolving Epidemiology of Hypertrophic Cardiomyopathy: Shifting the Focus From Instant to Lifetime Risk Awareness.

Objectives To evaluate the safety and efficacy of the Orsiro sirolimus-eluting stent (Biotronik) in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention (pPCI). Specific drug-eluting stent (DES) platforms might influence pPCI success rate in the mid-to-long term. Orsiro, a hybrid sirolimus DES with thin struts and a biodegradable polymer, may potentially cause less stent malapposition, stent-induced inflammation, and mechanical damage, improving clinical outcomes. Methods We retrospectively enrolled all patients who received 1 or more Orsiro DES in the target vessel of pPCI at 9 Italian centers from January 2012 to March 2016. The primary endpoint was a device-oriented composite endpoint (DOCE) of cardiac death, any myocardial infarction clearly attributable to the intervention culprit vessel (TVMI), and ischemic-driven target-lesion revascularization (ID-TLR) at 1-year follow-up. Secondary endpoints were: (1) DOCE at 6-month and 3-year follow-up; (2) any definite/probable stent thrombosis; and (3) any major bleeding. Results The study cohort comprised 353 patients. At 1-year follow-up, we observed a 3.7% cumulative incidence of DOCE, consisting of 11 cardiac deaths (3.1%), 2 TVMIs (0.6%), and 2 ID-TLRs (0.6%). There was only 1 definite stent thrombosis (0.3%) and 8 bleedings (2.4%). Kaplan-Meier analysis showed DOCE-free survival rates of 96.6% at 6 months, 96.3% at 1 year, and 93.8% at 3 years. Conclusions Our findings support the real-world safety and efficacy of the Orsiro stent for pPCI.

Marco Canepa

Papers

Persistent acantholytic dermatosis of the lip related to solar damage

Use of loop diuretics in chronic heart failure: do we adhere to the Hippocratian principle 'do no harm'?

Evolving Epidemiology of Hypertrophic Cardiomyopathy: Shifting the Focus From Instant to Lifetime Risk Awareness.

Hard Events AfteR Orsiro Sirolimus-Eluting Stent (HEROES) in STEMI: A Multicenter Registry.

The impossible quest to make cardiac amyloidosis diagnosis easy