Artificial Intelligence Platform on Mobile Devices to Assess Consumption of Pill in Subjects with Alzheimer

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Dr. Marcia B. Henry
Nischitha Sadananda
Jayesh Parsnani
Erick Jones

Abstract

There are many misconceptions in the
medical community concerning the
importance of drug monitoring. In the
In the United States alone, between 7,000 and
9,000 people die each year because of
medication errors. The total cost of
treating patients with medication errors is
over $40 billion (about $120 per person in
the United States) every year, with more
than 7 million patients (two times the
the population of Oklahoma) affected.
Patients incur psychological and physical
pain and suffering because of drug errors,
in addition to the financial costs. Finally,
pharmaceutical errors result in a
decrease in patient satisfaction and a
rising lack of trust in the healthcare
system. Failure to convey medicine
prescriptions, unreadable handwriting,
poor drug selection from a drop-down
option, confusion about drugs with
similar names, confusion about similar
packaging between products, and dosage
or weight errors are among the most
prevalent causes of errors. Human
mistakes can cause medication errors,
although they are more typically caused
by a failing system with insufficient
backup to detect the errors. In the case of
adverse medication responses, the patient
is harmed by taking a drug as prescribed,
does not have a necessary drug, or has
received it incorrectly, such as at an
excessively high or low dose. However, in
In recent years, medication surveillance has
not only gained support across the health
profession but has also been added to
regulatory guidelines from the US
Centers for Disease Control and
Prevention CDC (Centers for Disease
Control) and is accepted by various
medical councils. check whether the
Whether the patient has consumed the pill or not. In
our application to monitor Pill
consumption, we are using a Single Shot
a detector that takes a single exposure to
detect several objects in an image using a
multi-box. As a highlight, the resulting
The single Shot Detector model attains 92%
accuracy on MobileNet. The results of our
Single Shot Detector Model achieve 92%
accuracy on MobileNet. This application
will help to reduce the labor cost of
hospitals.

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