Artificial intelligence (AI) is imposed to impersonate human cognitive functions. AI Techniques are most popular across healthcare. The motive behind implementing an AI system is to make the system more fast and efficient. Now, AI can assist medical physician for fast and accurate diagnosis of diseases. When the time of deployment of the AI system will come then, systems need to be ‘trained’ for a huge amount of data will be generated from different clinical performance data. Now a day’s data is available in a structured, unstructured and, semi-structured format. For supporting, retrieving results and knowledge from this data, its analysis using different AI techniques are available. This includes machine learning methods for structured data and unsupervised learning for unstructured data which is useful for retrieving features when the outcome for some subjects is missing. In this paper different conventional machine learning techniques used in healthcare, domains is analyzed using different data types. Also, a comparison of different methods used in Artificial intelligence fiction in the healthcare domain is explored. A flow from clinical data creation, through NLP data enhancement and Machine learning data analysis for making clinical diagnosis decisions and its predictions are discussed and implementation using a support vector machine (SVM) on the healthcare dataset consisting of the patient questionnaire isdone.
In this technologically advancing world, a huge interest has been seen from the Automobile industry toward IoT applications, due to factors such as concern about the safety of the passengers, to satisfy customers' usability, and ultimately to offer cheaper but effective products which should maximize profit. The healthcare industry is more interested in how the IoT technology can lead to speedy and accurate treatment and how fast they could reach the neediest. This paper initially describes the practicability of equipping a vehicle with modern technology that can help patients to locate the nearest appropriate hospital by uploading their diagnosis into that system. Secondly, to reach the destination (i.e., Hospital) as soon as possible with the help of traffic police. For critical health issues such as Cardiac Arrest, Pregnancy (Labor pain), rapidly bleeding, snakebite, respiratory issues, gunshot fire and badly wounded, etc., where patient needs immediate medical assistance but unfortunately many of the times they could not make it due to traffic jams. While rushing to the hospital in case of medical emergency, it is risky to both communicate and locate the hospital at the same time while driving, so we have developed a system to establish a private network which will communicate via Cloud Thingspeak to the Hospital about our arrival. Also, the system will send patients' basic details, GPS location to them so that they'll get ready with their medical support. We will send vehicle information and GPS coordinates to the Traffic control room to get help in clearing the way for the patient's vehicle till the Hospital.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.