Clinical concepts in mimic three represent definitions and models that provide information about the severity of an illness. Normally, this is based on a pre specified set of clinical variables provided by experts in the field. In this video, we are going to discuss the key concepts implemented with mimic three and how this approach enables corporate disability. Clinical concepts have been developed well before the success of the planning models in order to quantify the severity of philness or an organ failure. In this way, they could predict specific patient events and allow for timely intervention and treatment to improve outcomes as well to provide better administration of care. Clinical concepts are constructed based on expert knowledge. In other words, experts have highlighted the clinical variables related to an event based on their intuition. The derivation of clinical concept based on an electronic health record databases challenging and a resource intensive task. For this reason, it requires close collaboration between data scientists and domain experts. Concepts in mimic includes civility of fitness, course, organ dysfunction scores, timing of treatment, definition of sepsis and comorbidities. Mimic provides an ideal substrate to build clinical concepts in a collaborative way between those that are familiar with a database. And how data capture within the clinical workflow and data scientist or statisticians. These models are the precursor of machine learning models and in fact some of them exploit machine learning, such as regression to refine that initial intuition of experts. The fact that these concepts are provided in the kick up mimic repository helps to remove barriers to the data scientists that are unfamiliar with the clinical environment. In mimic severity of illness scores have been developed to provide an assessment of the patient security. This scores look into the disturbance of normal bodily functioning in particular at the time of admission of the patient to the intensive care unit. Whereas these methods are initially based upon expert opinion. Subsequently they're validated on real clinical data and ultimately they developed using entirely data driven methods. Each of these cores comprises of at least 10 independent components and they are generally calculated using data from the first 24 hours of the patients stay. The acute physiology score is also known as acute physiology, age and chronic health. It was originally developed at George Washington University in 1981. The system consisted of a logistic regression model with hospital mortality as the independent variable and a set of dependent variables including comorbidities, age, gender and so on. The score aimed at risk stratification for severely ill hospitalized patients. And secondly, it also estimated the risk for hospital mortality for individual ICU patients. More recent versions allow also of evaluation of non linear effects based on cubic supplying terms. Further modification allowed for developing the regression models for patients of population as well as involving demographic parameters in the estimation. The acute physiology and chronic health evaluation score has been used in several countries, including the US. In order to benchmark the performance of hospital for use in quality improvement initiatives. The score has been also used to predict the expected patient length of stay. The simplified acute physiology score aimed at simplifying its predecessor the acute physiology score by reducing the number of physiological parameters that were required to almost 1/3 of the original model. The second version of the simplified acute physiology score had two aims. It utilized universe, create feature selection in order to identify features and correlated with hospital mortality and removed them. This was a significant improvement over the previous version that was done based on clinical judgment only. The score also allowed the estimation of the risk of mortality as a probability which ranged between 0 and 1. For the third version of the simplified acute physiology score, they were used data from intensive care units worldwide. This was the major improvement over the second version which was developed based on intensive care unit data only in Western Europe. The oxford acute civility of fullness core uses a genetic algorithm for features, election and in this way it improves with relation to the acute physiology score. It reduces the data while it retains similar or even better performance. Here we see the distribution of some of the illness a very discord with cast on the mimic database. In particular we see that most of them are similarly distributed with relation to the proportion of patient. However, the simplified acute physiology score has a considerably different distribution. The scores are provided in the mimic code repository in order to enhance the reproducibility. Another concept in mimic is the organ dysfunctions course. Organ failure is a mark of acute illness and is quantified in numerous ways. Some scores assess multiple organ systems, for example, sequential organ failure assessment or logistic organ dysfunction system. Both of these cores assess six organ systems for failure. There are also scored for specific organs. For example, the model for end stage liver disease, the kidney disease, improving global outcomes as well as the acute kidney injury network. Similarly to the stability of fullness course, organ dysfunction scores are used for protection and prognosis. For example, the model for end stage liver disease is used to determine the prognosis and received of liver transplantation. The organ failure scores follows similar development pathway with illness civility scores. There are initially based upon expert opinion and subsequently they have been validated on real clinical data. Finally, they are developed using entirely data driven methods. Interestingly modified organ dysfunction score and logistic organ dysfunction systems is based on statistical approaches. However, the most commonly used organ failure score is the sequential organ failure assessment, which is entirely based on expert judgement. We should highlight that this approach is do not achieve sufficient performance for using an individual patient level. They're mostly used on a population level to benchmark patient populations and provide information for quality improvement. Another important concept, which is also implemented in mimic, relates the timing of treatment as well as saturation. Researchers needs to understand what is the optimum intensity of an administered intervention. Sepsis is a life threatening organ dysfunction caused by a dis regulated host response to infection. It is a major and economically significant disease in the intensive care unit. Therefore, early detection of sepsis is another promising area for predictive tool applications. Mimic three provides a case election for sepsis based on ICD-9 codes. Based on these definitions early warning systems can be developed using high resolution physiological data and machine learning algorithms. In contrast to the models we discussed before with relation to severity of illness as well as organ dysfunction scores. Sepsis definitions required randomized clinical trials in order to validate them and use them and adapt them worldwide. Comorbidities refer to chronic conditions that many patients have prior to their admission in the intensive care unit. This affects their probability of surviving significant illness. In mimic there are 29 categories defined based on ICD-9 codes. For decades prognostic models of mortality were based on relatively simple estimates of severity of illness course. This course provides the ability to run population studies and answer questions with relation to the quality of care and treatment. However, their predictive power is limited and they cannot provide very useful inside with relation to specific patient outcomes. Part of the limitation is also that there is a variant definition of diseases and heterogeneity of patient populations more advanced machine learning methods promised to deliver better warning systems. Nevertheless, farther larger randomized clinical trials are required to validate these models. Summarizing concepts in mimic three, implement well known scores of fullness. In order dysfunction, mimic three can provide an ideal substrate to compare their performance and enable reproducibility. However, these concepts are based mainly on experts knowledge and their prognostic power is limited. With the availability of big data there is a hope that state of the art machine learning approaches. They will be able to provide personalized prediction of outcome. In this way, there is the potential to outperform clinical concepts that they have been developed the last few decades.
