Running Head: RESEARCH BUDGET 1 RESEARCH BUDGET 2 Research Budget Research Budget The research is to be conducted focusing on the predictive model in the management of Alzheimer’s disease. The budget aims at making the most out of every dollar spent (Patil, 2017) 100% sponsorship by the Alama Nursing School Period of the research project 05/15/2019 to 05/015/2020 Budget period 05/15/2019 to 05/15/2020 Senior Personnel Name Request by Agency PI Prof. A. Dorothy @ 1.5 SM months 10,000 Co-PI Dr. H. Hughes @ 2.0 SM months 8,000 Co-PI Dr. D. Mucahu @ 1.5 CY months 7,000 Other Personnel Data Analyst @ 5 CY months 12,000 3 Graduates @ 12 CY months 15,000 Total Personnel 52,000 Fringe Benefits for PI, Co-PI and Analyst 5,000 Materials 5,000 Equipment and supplies 9,000 Printing 1,000 Travel 3,200 Subcontract (Alama Nursing School) 2,000 Consultation 3,500 Refreshments 1,600 Direct Costs Total 82,300 Indirect Costs Facilities and Administrative Costs (F&A) @22% MTDC (19,000) 4,180 Total Costs (Direct + Indirect) 86,480 Total Project Cost = $86,480 Budget Justification Senior Personnel The senior personnel in this research include a single primary investigator and two co-primary investigators. Prof. Amanda Dorothy is a Senior Professor at the Alama Medical School. She is a decorated psychiatrist and a renowned researcher of Mental Health issues. She will serve as the PI at a summer salary for one and a half months at $10,000 Dr. Hillary Hughes is an Associate Professor and he is willing to assist in the project as co-PI with a request of two and a half months of summer salary of $8,000. Dr. Doreen Mucahu is also an Associate Professor and she is willing to work as the second Co-PI with a request of a one and a half months for $7,000 Other Personnel Mr. Robert Rowling’s is a full-time research data analyst and he is willing to devote half of his working hours to this research at the fee ($12,000). Three graduate students have been requested to assist in the research at a fee of $5,000 each. The students will assist in collecting data from the various entry point and administration of questionnaires. Fringe Benefits This is a convenience fee to be paid to those who commit to working with the researchers (Dawson, 2019). The fringe benefits were calculated at 20% of the salaries offered for the investigators and the data analyst. The graduates will not receive this part of the benefit, as they are not listed in the School policy. Equipment There is a need for acquisition of the analysis machine that will be used exclusively in this research project. The manufacturer has quoted $9,000 for the full package. Travel The primary investigators and the co-PIs will travel to a conference on methods of diagnosis and treatment of Alzheimer’s at an estimated cost of $1,500. For the local logistics of the investigators and the graduate students, we will require $1,700 for automobile mileage. Subcontracts It is important to have an external .

1. Running Head: RESEARCH BUDGET 1
RESEARCH BUDGET 2
Research Budget
Research Budget
The research is to be conducted focusing on the predictive
model in the management of Alzheimer’s disease. The budget
aims at making the most out of every dollar spent (Patil, 2017)
100% sponsorship by the Alama Nursing School
Period of the research project 05/15/2019 to 05/015/2020
Budget period 05/15/2019 to 05/15/2020
Senior Personnel
Name
Request by Agency
PI Prof. A. Dorothy @ 1.5 SM months
10,000
Co-PI Dr. H. Hughes @ 2.0 SM months
8,000

2. Co-PI Dr. D. Mucahu @ 1.5 CY months
7,000
Other Personnel
Data Analyst @ 5 CY months
12,000
3 Graduates @ 12 CY months
15,000
Total Personnel
52,000
Fringe Benefits for PI, Co-PI and Analyst
5,000
Materials
5,000
Equipment and supplies
9,000
Printing
1,000
Travel
3,200
Subcontract (Alama Nursing School)
2,000
Consultation
3,500
Refreshments
1,600
Direct Costs Total
82,300
Indirect Costs
Facilities and Administrative Costs (F&A) @22% MTDC
(19,000)
4,180
Total Costs (Direct + Indirect)

3. 86,480
Total Project Cost = $86,480
Budget Justification
Senior Personnel
The senior personnel in this research include a single primary
investigator and two co-primary investigators.
Prof. Amanda Dorothy is a Senior Professor at the Alama
Medical School. She is a decorated psychiatrist and a renowned
researcher of Mental Health issues. She will serve as the PI at a
summer salary for one and a half months at $10,000
Dr. Hillary Hughes is an Associate Professor and he is willing
to assist in the project as co-PI with a request of two and a half
months of summer salary of $8,000.
Dr. Doreen Mucahu is also an Associate Professor and she is
willing to work as the second Co-PI with a request of a one and
a half months for $7,000
Other Personnel
Mr. Robert Rowling’s is a full-time research data analyst and he
is willing to devote half of his working hours to this research at
the fee ($12,000).
Three graduate students have been requested to assist in the
research at a fee of $5,000 each. The students will assist in
collecting data from the various entry point and administration
of questionnaires.
Fringe Benefits
This is a convenience fee to be paid to those who commit to
working with the researchers (Dawson, 2019). The fringe
benefits were calculated at 20% of the salaries offered for the
investigators and the data analyst. The graduates will not
receive this part of the benefit, as they are not listed in the
School policy.
Equipment
There is a need for acquisition of the analysis machine that will
be used exclusively in this research project. The manufacturer
has quoted $9,000 for the full package.

4. Travel
The primary investigators and the co-PIs will travel to a
conference on methods of diagnosis and treatment of
Alzheimer’s at an estimated cost of $1,500. For the local
logistics of the investigators and the graduate students, we will
require $1,700 for automobile mileage.
Subcontracts
It is important to have an external data specialist to help in the
research (Lawes et.al. 2018). Specialized data analysis services
and the connection of the final report for compilation will be
provided by Dr. Annacitacia Johnwick and Dr. Maurice Mauhl
of the Amala University at a cost of $2,000.
Printing
An estimated $1,000 will be used to print the tools for data
collection and the journal materials for both reference and the
publication of the 12 pieces of the final report in print as
required by the sponsor.
F&A Costs
The facilities and administrative costs amount to $4,180 based
upon the rate of 22% of the MTDC of $19,000, which is
approved by the Amala University.
Consultation
The chief scientist at the LLC Inc. is willing to interpret
sections of the analysis of the results at his standard rates of
$500 per day for 6 days in addition to a $500 logistical fee, as
he will be working out of his office.
Conclusion
The budget has been designed to use every dollar for output in
terms of getting results of the study. The budget is subject to
change before approval in case of reviews or recommendations
from the sponsors to accommodate items that are vital for the
study (Greenville et.al. 2016).

5. References
Dawson, C. (2019). Introduction to Research Methods 5th
Edition: A Practical Guide for Anyone Undertaking a Research
Project. Robinson.
Greenville, A. C., & Emery, N. J. (2016). Gathering lots of data
on a small budget. Science, 353(6306), 1360-1361.
Lawes, M., Schultze, M., & Eid, M. (2018). Making the Most of
Your Research Budget: Efficiency of a Three-Method
Measurement Design With Planned Missing Data. Assessment,
1073191118798050.
Patil, S. G. (2017). How to plan and write a budget for research
grant proposal?. Journal of Ayurveda and integrative medicine.
Running Head: ALZHEIMER'S DISEASE RESULTS 1
ALZHEIMER'S DISEASE RESULTS 6
Alzheimer's Disease Results
Alzheimer's Disease Research Expected Results
The care for Alzheimer’s disease varies from patient to patient.
This is because of the difference in the presentation of the

6. cases. In addition, 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 caregivers. The data sheet for this study has
been provided in a separate excel sheet document.
Sample Size
Because of the importance of having a sizable sample size for
validity, researchers sought the advice of statisticians to help in
the sampling process. The determination of the sample size was
by effect size method where, the researchers were required to
have the participants being at least more than the effect size as
in the table below:
Alpha (α) = .05
Alpha (α) = .01
Effect Size (ES)
Effect Size (ES)
Sample size
Small (.2)
Moderate (.5)
Small (.2)
Moderate(.5)
20
0.10
0.34
0.03
0.14
40
0.14
0.60
0.05
0.35
60
0.19
0.78

7. 0.07
0.55
80
0.24
0.88
0.09
0.71
100
0.29
0.94
0.12
0.82
150
0.41
0.99
0.20
0.96
200
0.52
1.00
0.28
0.99
Based on the number of patients seen at the facility, which has a
mean of 3883 per month with 122 being Alzheimer’s patients
(3.142%), the researchers were required to have a sample size
of at least 20 patients for the 30 days of the research, which
projects to 600 patients.
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).

8. Based on previous research done in this area a larger section of
the patient population (52%) have 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. In addition, 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 could 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

9. 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.
This research finds that the use of predictive model in the
management of Alzheimer’s disease gives a more effective
management and a positive prognosis; hence, the alternate
hypothesis for this research is true.
Recommendations
Based on this research, the researchers recommend that
Alzheimer’s patients be managed by the predictive model right
from an early stage of the disease to ensure the best outcomes.
Because of Alzheimer's disease, patients may become non-
functional and completely dependent on help from friends and
family. This impacts 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

10. 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: introduction, epidemiology, and economic
impact. In Mental Health and Older People (pp. 197-209).
Springer, Cham.
Sheet1Data SheetAlzheimer's Disease ResearchResearch

11. Duration: 30daysDAY Total Number of PatientsAlzheimer's
Patients Revisits in the same monthAverage Waiting
Time(minutes)11314115214910123963232411240155136001661
52112071035412814073349133412110137963111164412312131
20271314251181413274121514831341612900321711631191812
88326191437321201323337211282122221077120239600112410
14225251203213261346132271332235281497420291368215301
253124Total 388312251Percentage Alzheimer's
Disease3.142Percentage Revisits of Alzheimer's
Disease41.8Average waiting time22.4666666667
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

12. 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

13. 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

14. 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: introduction, epidemiology, and economic
impact. In Mental Health and Older People (pp. 197-209).
Springer, Cham.

15. Literature Review
This article has used Predictive Modelling of the Progression of
Alzheimer’s Disease with Recurrent Neural Networks (RNN) to
identify the appropriate time for the patient regarding his/her
next visit for medical checkup. This model has the capability to
collect the various visits of a patient with different time
intervals to predict new medical visit dates with a more even
time interval. The study also discussed that more than 5.4
million people in the United States are living with Alzheimer’s
which cannot be fully treated or reversed with the current
medical and treatment system, however, with effective
prevention technology its prevalence can be controlled. Various
preventive or predictive models has been developed in the past
however, almost all of them had a number of issues which made
them less attractive. The use of this model helped in getting
99% accuracy based on the medical history of the patient
(Wang, Qiu & Yu, 2018). In the study conducted by Wang, Qiu
& Yu (2018) an RNN with LSTM cells-based progression model
was proposed to predict the future stages of the Alzheimer’s
disease. The model is found successful and authors showed their
interest to use this model for the prediction of other chronic
disorders.
Over the last five years, the research in the area of dementia

16. risk prediction and specially for the Alzheimer’s disease got a
boost. Numerous old models have been improved while some
new models were also developed to increase prevention of this
disease by predicting its numerous stages. In 2010, a systematic
review identified that there are more than 50 models developed
for the prediction of dementia and these models exhibited a
difference in terms of risk score calculation, model accuracy
level, follow up time, and disease outcome. This study
identified a novel and effective linear model which is invented
to predict the disease status in a future perspective. To check
the effectiveness of their model, the authors used available data
on Alzheimer’s disease. This study also addressed the issue of
data missing through matrix factorization (Nie et al., 2017).
This study has analyzed various articles in order to find that
whether there is any model that could perfectly make the
prediction regarding dementia. By analyzing various articles,
this article contributed in this field by providing information
about various studies and models that has been used for the
prediction of dementia and Alzheimer’s disease. Although, there
are various models developed by many researchers, however,
still there is no such model about which it can be said that it is
a perfect model for dementia/Alzheimer’s risk prediction among
people due to the absence of risk score validation researches.
There is a need to have lots of work to improve the existing
models as well as to develop new models considering the
weaknesses of existing models (Tang et al., 2015).
This study has investigated the relationship between
Alzheimer’s disease and aging. Researchers has found a strong
connection between aging population and Alzheimer’s disease
and it is assumed that innovation could help in the partial
resolution of the aging related societal problems. This study has
contributed in this field by identifying a biomedical model of
Alzheimer’s disease to reduce the social-culture aspects of the
aging and Alzheimer’s disease. This model proposed a cure to
Alzheimer’s disease; however, this model was abandoned due to
the emergence of care models in 1990s and onward. The

17. majority of models were discovered to make an early diagnostic
of Alzheimer’s disease, however, due to the lack of any proper
treatment the early diagnostics of this disease were remained
useless as no cure was offered. The only way with which the
early diagnostic or predictive technologies could help in the
treatment of Alzheimer’s disease is that these early diagnostic
models should eb a part of Alzheimer’s disease management in
its early stages. This study ahs also discussed various aspects of
human life as well as the use of technologies such as MRI, CT
scan and chemical testing to predict the early diagnosis of
Alzheimer’s disease, however, still there is a need for
developing new models due to flaws in existing models
(Cuijpers & van Lente, 2015). Alzheimer’s disease is
accountable for 70% of the dementia cases across the world
therefore, the therapeutic paradigm of this disease has been
moved to secondary prevention. Various social implications and
ethical concerns has been identified in preclinical predictive
approaches, therefore, there is a need of a proper predictive
modeling is required to address the challenges brought be
prevention approaches. The majority of predictive models have
raised some social implications and ethical concerns that the
majority of the studies on this matter has overlooked. The
predictive models like Statistical algorithms method or the
methods of source data such as imaging data, family data,
genetic data, cerebrospinal fluid examination, neurocognitive
assessment, electronic/medical health record, demographics and
family history have raised some sort of ethical and social
concerns (Angehrn et al., 2019). This study has investigated the
direction of the therapeutic paradigm with respect to
Alzheimer’s disease. It is also concluded by the study that there
is a need to identify predictive models that could predict the
occurrence of this disease years before. This is the first study in
this field which has paid attention to the social and ethical
concerns raised by the technological development for the
prediction of Alzheimer’s disease.
The study of Tang et al. (2015) investigated 1,234 articles to

18. investigate the new developments in risk prediction of dementia
and Alzheimer’s disease and concluded that new developments
like non-APOE genes testing, incorporation of diet, application
of non-traditional risk factor of dementia, ethnicity and physical
function, and inclusion of some development in subgroups with
diabetic individuals and those with different educational levels.
However, after making an investigation of various studies, it is
concluded that there is no single study that could be used for
the prediction of dementia. Moreover, it seems impossible to
have a predictive model that fits all. There is a high need to
make researches for the development of existing predictive
models as well as for developing new models, however, it is
important to consider various predictive models (Tang et al.,
2015).
According to the study of Haas et al. (2016) many predictive
models for Alzheimer’s disease have failed due to the absence
of reliable models. This is because the majority of models are
computerized and there is a need for a more humanized kind of
models that best suits the clinical situation. The biggest need is
to make sure that data is shared among all the healthcare
organizations because it allows researchers to address the
various scientific questions in different predictive models. The
availability of data will also enable the researchers to access
both real life data and clinical trials data to innovate a more
accurate and ethically sound predictive model. The diagnosis
and prediction of Alzheimer’s disease is very difficult because
of its different symptoms in different patients. The study of
Haas et al. (2016) contributed in this field by identifying the
various weaknesses of the existing predictive models. The
presence of an effective predictive model could change the
entire direction of the treatment of Alzheimer’s disease. The
article has identified few real-world examples of such
predictive models which contributed effectively in various CNS
(central nervous system) diseases. Also, it is suggested that for
the development of a predictive model, government and private
organizations should make a partnership. Moreover, the

19. progression of this disease from preclinical, mild and severe is
different among different individuals. Instead on investigating
appropriate predictive models, there is a need to invest in the
training of physicians to make them ready and competent
enough to manage Alzheimer’s disease at various stages. The
improvement in the understanding level of the physicians could
improve the overall cycle of treatment and management of the
Alzheimer’s disease (Margolis, 2017). This study has identified
a more realistic side of the Alzheimer’s disease that instead of
developing a new predictive model, it is better to invest in the
training of physicians and medical staff to deal with different
types of patients at different stages of Alzheimer’s.
Due to the lack of discovery of the pathogeneses of Alzheimer’s
disease there is no proper cure available for it to date. Since it
is widely believing that pathogenic changes began to occur
years before therefore, it is important to have an accurate and
effective predictive model to accurately screen, diagnose and
prevent this disorder. Unfortunately, due to the polygenic nature
of Alzheimer’s disease, it has not been possible to effectively
predict the level of Alzheimer’s disease (Nazarian & Kulminski,
2018). This study has stressed on the importance of discovering
the biological process involved in Alzheimer’s disease because
the Alzheimer’s disease pathogenesis in not fully discovered
due to which not predictive model is working effectively. Once
the pathological changes are fully discovered, it would become
possible to develop an effective and standard predictive model
to treat Alzheimer’s disease.
References
Angehrn, Z., Nordon, C., Turner, A., Gove, D., Karcher, H., &
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27337-w
Running head: Predictive modeling 1
Predictive modeling 7Predictive Modeling as a Preventive

21. Technology in the Health Sector
Topic area
The average human lifespan is increasing along with the world
population, which poses new challenges to today’s treatment
delivery methods. The health sector is capable of collecting
massive amounts of data and look for best strategies to use the
numbers. With the use of predictive modeling, the health sector
has the potential to reduce costs of treatment, predict outbreaks
of epidemics, avoid preventable diseases and improve the
quality of life.
Advances in predictive modeling as a preventive technology in
the health sector should be designed to help clinically integrated
networks manage large, complex patient populations. One of
the challenges facing providers today is that predictive
modeling requires a strong data infrastructure, user engagement,
staffing and other resources.
Preventive technology could help make diagnoses more accurate
and treatment regimens more precise, reduce labor costs,
capture data faster, sort through vast amounts of data to gain
insights needed to drive better care decisions and outcomes.
With the use of predictive modeling and preventive technology,
from a patient-facing perspective, they could improve health
literacy. These instruments combined with patient-contributed
data could also help predict and fill patients’ clinical knowledge
gaps. In that way, patients could more confidently manage their
own health and make healthier choices on their own.
For example, the use of the fit bit arm/wrist band could be
extended into an integrated system that could collect patients’
health data continuously and send this data to the cloud which
will allow doctors to monitor and compare this data and react
every time if the results will be disturbing. If a patient’s blood
pressure increases alarmingly, the system will send an alert to
the doctor who will then take action to reach the patient and
administer measures to lower the pressure.
Research question

22. What impact does predictive modelling on creating successful
treatment plan for patients with diagnosis of Alzheimer’s
disease?
Alzheimer’s disease is one mental problem that mostly affects
the elderly population. This disease is characterized by a
progressive memory loss leading to impaired cognitive
functions. The condition is usually chronic hence affect the
patient for a long time. Patients with, therefore, disease have a
reduced quality of life coupled with continuous treatments. Use
of predictive modeling technology in the management of this
patient can play a significant role in creating a successful
treatment plan for the condition. Predictive modeling
technology has several benefits for both the patient and the
provider of healthcare.
Predictive analysis or modeling in healthcare improves the
accuracy of diagnosis. For patients with Alzheimer's disease,
using the predictive modeling technology help the primary care
physicians to make an accurate diagnosis of the bug regarding
the staging and presence of other comorbid condition such as
dementia. To achieve this, the clinicians use the predictive
algorithm provided by this model to assess for the stage of the
disease and the presence of other conditions (Wang, Kung, &
Byrd, 2018). As a result, the patient is usually screened using
this model before being discharged home to promote patient
safety and quality of life.
Predictive modeling technology play a significant role in
preventive disease medicine and public health. In this case, this
analytic technology helps care providers to prevent diseases and
their complications from advancing by identifying populations
at risk (Ritchie et al., 2015). Alzheimer’s disease patients are at
risk of developing other mental condition such as dementia or
promoting to the severely chronic stage that will acutely reduce
the health status and life of patients. Therefore, primary care
physicians use this model to screen the Alzheimer’s disease
patients under their care for possible complications or
deterioration. Knowledge gained about the disease using this

23. analytic model is therefore used to implement safety
precautions such as lifestyle changes that promote disease
recovery. The model, thus, transforms the care of Alzheimer’s
disease from curative to preventive since prevention is better
than cure (Wang, Kung, & Byrd, 2018).
Predictive modeling technology in mental health provides
physicians and psychiatrists with information on how to provide
care for the individual patient. For a patient with Alzheimer's
disease that was diagnosed with the algorithm as per this model,
the exact treatment methods required are also identified. This is
different from the usual plan of care that is drawn from standard
symptoms and treatment. The patient will be given specific
drugs to meet their health needs. Also due to predictive analysis
on the type of effective medications, the hospital can use this
information to purchase only the required drugs from the
pharmaceutical stores to prevent wastage and shortages of
medicines (Bhagwat et al., 2018). Therefore, medication for
Alzheimer's disease will be readily available for use.
Predictive modeling in healthcare also has a direct impact on
patients with Alzheimer's disease. The use of the algorithm in
diagnosis and prescription of treatment results in patients
getting the drugs that are only effective or active against their
condition. Use of specific medications to treat this condition
increases patient knowledge on how to promote the
effectiveness of these medications (Bhagwat et al., 2018).
Patients will be aware of potential complications and learn how
to prevent such from occurring. This is, in turn, support
evidence-based practice where patient-centered care is
practiced. As a result, quality and safe care is practice hence
promoting better health outcomes for the patients.
The significance of the study
The purpose of healthcare predictive modeling is to help doctors
make data-driven decisions within seconds to improve a
patients’ treatment. By using data-driven findings to predict
and solve a problem before it is too late, also assess methods
and treatments faster, keep better track of inventory, involve

24. patients more in their own health and give them the tools to do
so.
This is very useful with patients who have a complex medical
history and suffering from multiple conditions. This tool would
be able to predict, for example, who is at risk of diabetes, and
thereby be advised to make use of additional screenings or
weight management. For year’s gathering huge amounts of data
for medical use was costly and time-consuming. With
technology improving on a daily basis, it is easier to not only
collect such data but also to convert it into a useable form to
provide better care.
Healthcare providers had no direct incentive to share patient
information with one another, which made it harder to utilize
the power of predictive modeling and preventive technology.
Now that more of the health sector are getting paid based on
patient outcomes, they have a financial incentive to share data
that can be used to improve the lives of patients while cutting
costs for insurance companies. Healthcare needs to catch up
with other industries that have moved from the standard
regression-based methods to a more future oriented like
predictive model, to improve patient outcomes while reducing
spending.
References
Bhagwat, N., Viviano, J. D., Voineskos, A. N., Chakravarty, M.
M., & Alzheimer’s Disease Neuroimaging Initiative. (2018).
Modeling and prediction of clinical symptom trajectories in
Alzheimer’s disease using longitudinal data. PLoS
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Breuker, D., Matzner, M., Delfmann, P., & Becker, J. (2016).
Comprehensible Predictive Models for Business Processes. MIS
Quarterly, 40(4), 1009-1034.
Ritchie, C. W., Molinuevo, J. L., Truyen, L., Satlin, A., Van der
Geyten, S., & Lovestone, S. (2016). Development of
interventions for the secondary prevention of Alzheimer's
dementia: the European Prevention of Alzheimer's Dementia

25. (EPAD) project. The Lancet Psychiatry, 3(2), 179-186.
Wagenen, J. (2017, November). Predicting-analytics-3-Big-Data
Trends in Healthcare. Healthtech, pp-1-6. Retrieved from
https://healthtechmagazine.net/article/2017/11/predicting-
analytics-3-big-data-trends-healthcare.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics:
Understanding its capabilities and
potential benefits for healthcare organizations. Technological
Forecasting and Social
Change, 126, 3-13.
Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M.,
& Poranki, S. (2015). Predictive modeling of hospital
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