Alzheimer’s Methodology 1

Alzheimer’s Methodology 1

Alzheimer’s Methodology 5

Alzheimer’s Disease Methodology

Alzheimer’s Disease Methodology

Data on Alzheimer’s disease patient visits show that the rate and patterns of health seeking by these patients are varied and each case has different presentation. The care for Alzheimer’s disease varies from patient to patient. This is because of the difference in the presentation of the cases. Also, some patients, especially those with milder forms of the disease visit the medical facility more frequently than others who have advanced stages and have to be brought to the facility by their care givers. The data sheet for this study has been provided in a separate excel sheet document.

Data on Alzheimer’s disease patient visits show that the rate and patterns of health seeking by these patients are varied and each case has different presentation. Even with the provision of large amounts of literature about the progression of the disease, studies have failed to link the approaches to prognosis and as such have failed to help create a time-relevant model based on the medical records of patients, an approach that aids in predicting the future status of the disease (Kielb et.al. 2017).

Based on previous research done in this area a larger section of the patient population (52%) has to depend on family for care. The data obtained in our study through our data sheet and the questionnaires show no remarkable link in the phases of the disease in different patients. The average delivery of service was done within 22.5 minutes of the visit.

Out of the 3883 patients who visited the healthcare facility, 122 were patients with definitive diagnoses or with important negatives pointing to Alzheimer’s disease. Of the total 122 patients categorized as having Alzheimer’s disease, 58.2% presented to the hospital for the first time. Also, 41.8% of the total cases presented for the second or third time within the same month. This shows significant need for healthcare among these patients.

The doctors on call and the attending nurses filled the questionnaires and it was determined that care was provided on need basis. Those patients who presented with advanced symptoms of mood instability, mental disturbance and physical violence were handled first; otherwise, all patients were seen on a come first served first basis. The breakthrough of use of predictive models in treatment of Alzheimer’s depends on the amount of research done in this field. The research will provide a wide variety of information that is necessary in the reliability of predictive approaches. This approach can be a breakthrough not only in the management of Alzheimer’s disease but also many other chronic medical conditions (Hampel et.al. 2016).

This research closely relates to the data by the National Alzheimer’s Coordinating Center. This agency conducted research using 5432 participants who were patients of Alzheimer’s disease. The research starting in 2005 August and ending in 2017, May gave resourceful insights into the whole issue. It was found that there can be several predictive models that can be used in the management of Alzheimer’s.

The patient’s cognitive functions are altered and their social interactions are distorted. Some common signs include mild amnesia at the onset of the disease which develops into severe loss of memory; disintegration form the surrounding and loss of ability to function normally, apathy, depression, irritability, mood disturbance, altered sleeping habits, and delusions (Alzheimer’s Association. 2017).

The short-term memory in these patients was found to be averting after some time. The adopted method used recurrent neural networks, which are based on the concept of many-to-one that refers to the barbarization of neural dendrites. The approach was found to be very reliable with results that indicate the efficiency of over 97% in the prediction of the state of patients suffering from Alzheimer’s disease. This method can be said to be superior to the classic baseline approaches (Hassenstab et.al. 2015).

By the analysis of the approach, we see that the RNN approach is effective in solving the Alzheimer’s disease prediction. This is through the measurement of progression by leveraging both the medical patterns and inherent temporal patterns of the Alzheimer’s patients based on their past visits (Hampel et.al. 2016).

Our study was faced with several problems such as getting the proper diagnosis for the walk in patients and the complex nature of use of questionnaires in gathering information from different people.

As a result of Alzheimer’s disease, patients may become non-functional and completely dependent on help from friends and family. This causes a major impact on the economy of the country as a whole in terms of lower productivity and lack of income generation (Werner et.al. 2016). For these reasons, this research is vital and should serve as a starting point for even more inquisitive researches that will help solve the suffering that comes with Alzheimer’s disease.

References

Besser, L. M., Kukull, W. A., Teylan, M. A., Bigio, E. H., Cairns, N. J., Kofler, J. K., … & Nelson, P. T. (2018). The revised National Alzheimer’s Coordinating Center’s Neuropathology Form—available data and new analyses. Journal of Neuropathology & Experimental Neurology, 77(8), 717-726.

Hampel, H. O. B. S., O’Bryant, S. E., Castrillo, J. I., Ritchie, C., Rojkova, K., Broich, K., … & Escott-Price, V. (2016). PRECISION MEDICINE-the golden gate for detection, treatment, and prevention of Alzheimer’s disease. The journal of prevention of Alzheimer’s disease, 3(4), 243.

Hassenstab, J., Monsell, S. E., Mock, C., Roe, C. M., Cairns, N. J., Morris, J. C., & Kukull, W. (2015). Neuropsychological markers of cognitive decline in persons with Alzheimer disease neuropathology. Journal of Neuropathology & Experimental Neurology, 74(11), 1086-1092.

Kielb, S., Rogalski, E., Weintraub, S., & Rademaker, A. (2017). Objective features of subjective cognitive decline in a United States national database. Alzheimer’s & Dementia, 13(12), 1337-1344.

Werner, P., Savva, G. M., Maidment, I., Thyrian, J. R., & Fox, C. (2016). Dementia: introductio