Data Synthesis Essay

Throughout today’s society, media contributes to almost everyone’s daily life. From informative news channels to comical television shows, media proves to be effective in advertisement, releasing messages and informing the audience. Although media proves to be wildly effective in advertising, releasing messages and informing the audience, periodically destructive and misleading messages are provided to the audience and directly influencing women. Cultural critics widely agree that media tends to negatively influence women and all the critics point to research which supports the belief that women are portrayed as subordinate to men, having no

It has recently been brought to my attention that our school is looking to adopt a sustainable menu into our meal program and I would like to give my viewpoint on the matter. As one many students here at Oxford Academy this change will affect me directly. Although people may at first oppose this conversion I think if we are able to overcome the first obstacles, this change would bring a great positive impact to the environment and the health of our students.

In Amy Chua’s essay, “The Right Road to America” she argues the point that immigration is a great value of this country and that America was built from immigrants. One thing differs from Chua’s essay than from others because she not only gives highly credible facts on how immigrants have helped our country, but she also provides ways to improve the issues of immigration into America. Amy Chua is a highly credible professor at Yale and states throughout her essay that immigrants who possess American values deserve to be here in America and those who don’t should leave. America had always been a country that accepted immigration and allowed all

Clinical decision making is a complicated process that depends on human capability to provide full attention to memorize, and create enormous amounts of data to all areas. IT systems are able to access information, arrange them, and recognize links between them. Clinicians often ‘know’ information such as a patient’s allergies, drug interaction and if that drug is on recall.

The role of knowledge base in a Clinical Decision Support System is to reduce medical errors and provide the best quality healthcare available. Knowledge base refers to the practice of medicine based on the best available scientific evidence (JAMIA). Knowledgebase continues to expand and it is crucial in developing clinical decision support systems by encoding concepts coming from experts in the field to provide a way to analyze the data and generate expert advice. With a knowledgebase system in place healthcare providers will have a more focused diagnosis of the disease and the treatment that will need to take

In my experiment, I positioned my 3 tiles right next to each other labeled ( Grapefruit, Orange, and Lemon,) then I took my spray bottles and sprayed the syrup 3 times on each tile and I took my paper towels and swiped up the excess syrup. I recorded my results and they seemed consistent especially the 3 trial which shows that all the fruits had the best results during that specific trial ( Lemon=85%, Orange=80%, Grapefruit=75%). What I did was I accidently, without thinking folded my paper towel and swiped the tile, what I concluded was that I recommend taking your paper towel during the experiment and folding it to get better results.

Hospital Readmissions are costly to the government, hospitals, and patients. Some of these pricey readmissions can be avoided by providing the appropriate care for the patients that are at a higher risk of readmission. Many studies have used predictive analytical approaches to discover which patients are more likely to be readmitted. Most of said studies use some form of regression to analyze all patients. We took a different route and used an advanced decision tree (Gradient Boosted Tree) as we found it to be more fitting for the data we collected. With COPD as a constant factor in our patients, we were able to develop a specific model for a subsection of all hospital readmissions. With COPD being a fix variable, our model was able to dig deeper into other

College is a great opportunity for high school graduates through it may not seem exciting or entertaining but it will be worth it once students start finding jobs after their studies. College is a great start for anyone's future because it would impact each person's knowledge in the future and develop students characteristics once they start to find a well-paying job.

Statistical and qualitative analyses can determine extensive medical data and expose concealed patterns and trends (Koh & Tan, 2011; Soni, Ansari, Sharma, & Soni, 2011). Sharek et al. (2011) and Kurosaki et al. (2012) also proposed that using statistical techniques to analyze EHR data can help to categorize patients at high-risk, and hence promote evidence-based healthcare. Data mining is a predictive and descriptive analytic tool that improves quality patient care through preventions and early diagnosis.

Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. Current scales calculate scores from a fixed set of conditions collected within a specific time window. However, modern monitoring technologies generate complex, temporal, and multimodal patient data that conventional prediction scales cannot fully utilize. Thus, a more sophisticated model is needed to tailor individual characteristics and incorporate multiple temporal modalities for a personalized risk prediction. Furthermore, most scales focus on adult patients. To address this need, we propose a new ICU risk prediction system, called icuARM-II, using a large-scaled pediatric ICU

Usually the linear energy transfer (LET) is expressed in units of KeVµm-1 in water. The conversion of units between KeVµm-1 and MeVcm-1 is 1 KeVµm-1 = 10MeV cm-1.1

The Why of applying predictive analytics in healthcare: If predictive analytics is applied extensively to the rapidly growing healthcare industry, limitless advantages can be realized. Some of the advantages are: 1) Improved real-time decisions about treatment and support, consumer commitment 2) Effortless revenue management with focus on global as well as local markets 3) Standardized clinical processes, guidelines and protocols greatly improving operational efficiency 4) Reduction in fraud claims, security threats greatly helping insurance companies 5) Mining for unknown variables that determine quality such as “hidden” re-admission factors or finding out

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

In this experiment, we found that the performance of random forests is optimal with around 70% accuracy on publicly available “Diabetes 130-US hospitals for year 1999-2008” dataset. The proposed model also indicates that information regarding diagnosis, age, race, medications, admission types, lab procedures are highly influential for readmission classification. In this work, the result is evaluated in different criteria. The best AUC and f-measure values obtained on the experiment dataset were 70.1% and 66.6% the classification of the readmitted patients from non-readmitted patients using random forests. It can be observed Random Forests is performed optimal than Naïve Bayes and C4.5.

A set of cases was taken and the program was trained with these data sets such that the probabilities of all the classes with all the conditions were calculated. Result was stored in database and when the test data was given we got the probabilities for the various classes for the given symptom values on the basis of which we inferred that the patient fell into the class with the highest probability. This is what is called the Naïve Bayes‟ classification. This is a very powerful technique that is instrumental in helping us predict the category a patient falls into.