3d Heart Failure

In Falcon’s report “Heart Disease” he anatomically describes the heart as “a fist sized organ located in the lower left quarter of the chest…[consisting] of four chambers: the right and left atria on top and the right and left ventricles at the bottom” (Falcon). While the heart is one of the smaller organs in the body, it has an enormous and important job to do; deliver nutrient carrying blood to the tissues in the human body. In people with heart disease, the heart is impeded and cannot efficiently deliver nutrients and oxygen to structures such as our muscles or our brain. This is one of the reasons that heart disease is so deadly; when our body structures are deprived of necessary nutrients, they begin to breakdown. The term “heart disease” encompasses a wide variety of

Heart failure can be attributed to either right sided, left or both. Left-sided heart failure is of two types, systolic failure and diastolic failure. Systolic failure is the when the left ventricle loses its ability to contract normally. The heart cannot pump with enough force to push enough blood into circulation. Diastolic failure is when the left ventricle loses its ability to relax normally. Which results in the heart not being able to fill with blood during the resting period. Both result in a decrease in cardiac output. (AHA, 2012). A decrease in the cardiac output into the systemic circulation causes blood to accumulate in the left ventricle, left atrium, and pulmonary circulation. This increase

Heart Failure is a progressive heart disease when the muscle of the heart is weakened so that it cannot pump blood as it should; the blood backs up into the blood vessels around the lungs and the other parts of the body (NHS Choice, 2015). In heart failure, the heart is not able to maintain a normal range cardiac output to meet the metabolic needs of the body (Kemp and Conte, 2012). Heart failure is a major worldwide public health problem, it is the end stage of heart disease and it could lead to high mortality. At present, heart failure is usually associated with old age, given the dramatic increase in the population of older people (ACCF/AHA, 2013). In the USA, there are about 5.7 million adults who have heart failure, about half of the people die within 5 years of diagnosis, and it costs the nation an estimated $30.7 billion each year (ACCF/AHA, 2013).

After a period of time, the heart muscles of the left ventricle begin to weaken. The weakening of the left ventricle will lead to decreased empting of the heart (systolic heart failure) which results in decreased cardiac output again. Since the left ventricle does not empty completely, blood begins to back up into the left atrium and then to the pulmonary circulation thus resulting in pulmonary congestion and dyspnea (Story 2012, 104). If left untreated, the blood will back up and affect the right side of the heart causing biventricular heart failure (both right and left heart failure). In right sided heart failure, the right ventricle weakens and cannot empty completely. This incomplete emptying causes blood to back up into the systemic circulation causing systemic edema (Lewis et al. 2014, 771).

Recently technology has become a significant part of society, specifically for the medical field. People in the past have expressed concerns about the security and safety of implementing artificial intelligence (AI) into the medical field. Artificial intelligence is a computer system with human capabilities, such as decision making. Research has shown that AI could increase the efficiency and quality of patient care in the medical field. AI could greatly improve efficiency by using software that can analyze all of the symptoms the patient has and the patient’s family history in a shorter period of time than a human doctor could. For the time period from 2000 to 2010 the conversation about artificial intelligence was focused on the ethical

These discoveries prompted change and allowed the National Institute of Health and Clinical Excellence (NICE), to set standards to reduce unnecessary detriment to patients. Many tools were introduced to assist in the consistency and accuracy of observations of patients’ physiological conditions. ViEWS (VitalPac Early Warning Score) is a standardized and high-tech scoring system that helps recognize and respond to deteriorating patients. It is the basis of the newest warning system, appropriately named National Early Warning System or NEWS (Featherstone, Prytherch, Schmidt & Smith, 2010).

R E V I E W S H E E T 30 Anatomy of the Heart

This article is one of the most interesting in discussion of a practical involvement of artificial intelligence into the current healthcare system. The matter here is that this article consists predominantly with the critics of contemporary technology, underlining the importance of the further investigations of it. But at the same time, Salvado claims that artificial intelligence, as the technology of a future has a considerable number of advantages.

Similarly to how a problematic mitral valve can lead to left-sided heart failure, a faulty tricuspid valve may also do the same to the right side. Left-sided heart failure as a general rule of thumb inevitably leads to right-sided heart failure. Other causes of right ventricular failure include right ventricle infarction, massive pulmonary embolism, pulmonary hypertension, and chronic obstructive pulmonary disease or COPD for short.

Heart failure, HF, is a result of one’s heart inefficiently pumping blood out to the body (Lewis, Dirksen, Heitkemper and Bucher, 2014, p.766). A healthy heart will pump blood out of the left and right ventricles rhythmically and simultaneously, creating an even flow of blood from the heart to the pulmonary arteries and the aorta (Lewis et al., 2014, p.769). Someone with heart failure has a ventricular dysfunction in either one or both ventricles; the ventricles are not filling or contracting properly. The failure of one ventricle to properly function leads to an overcompensation of the opposite ventricle as well as a disruption in normal blood flow that leads

These measurements include the assessment of risk factors[61], quality of care[62], diagnostic criteria[63], etc. Most of these studies used rule-based method[62, 63] to detect clearly defined and less complex (fewer expression variations) measurements, such as glucose level and body mass index. For some ambiguous and complex measurements, such as coronary artery disease and obesity status, machine learning plus external terminologies[61] are often

As the population ages heart failure is expected to increase exceptionally. About twenty-two percent of men and forty-four percent of women will develop heart failure within six years of having a heart attack. “Thirty years ago patients would have died from their heart attacks!” (Couzens)

Heart Contractions and Blood Flow – An animation of the showing how the heart pumps.

Machines with AI could be used in medical facilities to diagnose illnesses or to search for symptoms or the presence of anomalies in the bodies of patients (Keiser, 2017). Doctors constantly lament the delays in diagnosing diseases in a developing country. The use Artificial

This study utilized the Worchester Heart Attack Study data and R Studio software to predict the mortality factors for heart attack patients. The medical data include physiological measurements about heart attack patients, which serve as the independent variables, such as the heart rate, blood pressure, atria fibrillation, body mass index, cardiovascular history, and other medical signs. This study employed the techniques of supervised learning and unsupervised learning algorithms, using classification decision trees and k-means clustering, respectively. In addition to performing initial descriptive statistics to estimate the general range of critical factors correlated with heart attack patients, R Studio was used to determine the weight of each of the significant factors on the prediction in order to quantify its influence on the death of heart attack patients. Furthermore, the software was used to evaluate the accuracy of the predicted model to estimate death of heart attack patients by using a confusion matrix to compare predictions with actual data. Finally, this study reflected on the effectiveness of the data mining software conclusions, compared supervised learning and unsupervised learning, and conjectured improvements for future data mining investigations.