Medication adherence monitoring with tracking automation and emergency assistance
| dc.contributor.author | Jamkhandikar, Irfan | |
| dc.contributor.author | Shaikh, Shadab Ali Murad Ali (17DCO74) | |
| dc.contributor.author | Kazi, Obaid Abdul Aziz (17DCO69) | |
| dc.contributor.author | Ansari, Mohd Adnan Azimuddin (17DCO63) | |
| dc.contributor.author | Shaikh, Romaan Usman (16DCO77) | |
| dc.date.accessioned | 2021-11-03T07:22:31Z | |
| dc.date.available | 2021-11-03T07:22:31Z | |
| dc.date.issued | 2020-05 | |
| dc.description.abstract | In today‘s busy world, people are not able to track/monitor the medication events of their dear ones who are suffering from diseases appropriately. There are times when a patient “forgets” to have or “doesn‘t take” the prescribed medicine at a given schedule. Such scenarios in medical term is known as Medication Non-adherence (MNA). The MNA to prescribed treatment is thought to cause at least 100,000 preventable deaths and $100 billion in preventable medical costs per year. The reason for Nonadherence shows 63% for forgetfulness.[5] A study kempegowda institute of medical sciences & research center, Bangalore, India depicts over 21% of MNA problem in hypertensive patients.[9] The technology that could help in improving the MNA related problem is over 28% combining phone call (10%), live chat (3%), SMS (9%), Mobile applications (5-8%).[11] Despite, The medical profession largely ignores MNA or sees it as a patient problem and not a physician or health system problem.[5] This is a great loss to society considering the health effects and causes being generated from this issue. This report highlights various mechanisms that can be thought to integrate in order to improve the adherence of a patient.[1] Unlike already existing applications, Our Application (MAMTE) will guide a patient through automated calls, maintenance & tracking of log of events, & will use state of the art technologies such as Google Cloud Vision A.I and Deep learning neural networks for text and handwriting detection-recognition and extracting medication events through images. Keywords: Medication Non-Adherence, handwriting recognition, google cloud vision A.I, deep learning neural networks, medication events tracker. | en_US |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3617 | |
| dc.language.iso | en | en_US |
| dc.publisher | AIKTC | en_US |
| dc.subject | Project Report - CO | en_US |
| dc.title | Medication adherence monitoring with tracking automation and emergency assistance | en_US |
| dc.type | Other | en_US |