An Intelligent Virtual Medical Assistant for Healthcare Prediction

An Intelligent Virtual Medical Assistant for Healthcare Prediction

Jeya Mala D. (Vellore Institute of Technology, Chennai, India) and Pradeep Reynold A. (InduTch Composites Pvt. Ltc., India)
Copyright: © 2023 |Pages: 17
DOI: 10.4018/978-1-7998-9220-5.ch050
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Developing an intelligent virtual medical assistant device would be a better solution for people who can't spend their time and/or who have movement and transportation issues for physical diagnosis and checkup, especially the old-age people and those who have other movement related diseases. Such virtual medical assistants will be a boon to both patients and their relatives. A simple IoT-enabled virtual medical assistant can be an IoT device with sensors to monitor the health status on some basic parameters such as temperature, blood pressure, oxygen level, etc. However, for providing smart healthcare, intelligence needs to be embedded in these kinds of virtual assistants. This article discusses the application of machine intelligence (ML) algorithms in an intelligent virtual medical assistant to provide improved solutions by tracking the patients' historical data along with the current data, which can then provide suggestions, notifications, and medical prescriptions for self-improvement.
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In city life, healthcare is an essential and significant part of every citizen to lead a healthy life. A typical healthcare system has to include the needs of patients, physicians, lab technicians, specialists etc. It needs to keep track of various stages of healthcare including monitoring, diagnostics, treatments, reports etc. Due to the increase of population density with steady increase of old age population along with the rise of pandemic diseases, challenges such as high demand of hospitals; personal medical care and administration of medical resources have been posed on traditional healthcare systems.

Traditional healthcare applications can no longer be trusted or completely relying upon due to the rapid spread of dangerous diseases worldwide. Hence, the medical practitioners, healthcare industries etc., are now focusing on developing smart healthcare solutions to tackle the challenges in delivering smart solutions especially during this pandemic era (Sumayya, 2020).

Smart healthcare is generally the combination of healthcare practices with information technology such as IoT, cloud computing, mobile communications, big data analytics and Artificial Intelligence (AI) / Machine Learning (ML) techniques. They act as an alternative of conventional medical service systems and manual or human based health management systems.

The IoT enabled healthcare systems are playing a crucial role in smart city applications. These kinds of applications can be used as assistants to detect the transmissible diseases, monitoring of treatments and further healthcare management activities. Apart from these, the application of AI in the area of smart healthcare has gained huge impact on providing more efficient, cost effective and personalized solutions.

Nowadays, machine learning techniques embedded in automated healthcare solutions have impact on providing robust and reliable solutions in smart healthcare. Also, management activities related to customer healthcare information management, customer insurance management, Laboratory equipments management, Doctors’ prescriptions management etc. have been equipped with intelligent methods to improve the efficiency in storing, retrieving and processing of data from the repositories and to take intelligent decision making.

The current research works explore the application of AI and ML in smart healthcare systems and their allied areas. It is essential to trace the patients’ historical data in crucial life saving situations and to prescribe immediate medications to rescue the patient’s health are accomplished nowadays by intelligent solutions.

In this connection, several research works are carried out to apply Machine Learning (ML) and Artificial Intelligence (AI) to get greater impact on healthcare domain and its administration activities. The healthcare industries are now in need of a better independent solution to help the people by applying AI integrated medical solutions. Many industries and individual researchers are nowadays developing several applications using current programming languages such as Python (Admin, 2020).

It is predicted that, by 2025, there shall be at most 50% of increase in the automated, smart healthcare devices shall occupy the healthcare industry. The information technology organizations help achieve these kinds of personalized, automated healthcare services by significantly reducing the need for human assistants by means of AI based solutions (Sundaravadivel, Kougianos, Mohanty &Ganapathiraju, 2018).

Some of the significant advantages of AI based healthcare systems include (i) cost reduction and improved quality; (ii) more efficient healthcare products (iii) robust solution to the patients and their caretakers (iv) enhanced internal functionality.

In this connection, several smart applications such as AI powered Chatbots, Robots, Virtual nurse with AR etc. are developed to assist the patients, old-age people etc (Dodhia, Jha, Anudeep & Sarmah P, 2017).

This chapter discusses on the related work in this area of research, virtual medical assistant development, application of ML in virtual assistants’ decision making process etc.

Key Terms in this Chapter

Artificial Intelligence (AI): As per the father of AI, McCarthy, “AI is the science and engineering of making the Machine to be intelligent”. It helps to make a computer system, a robot or a device to be programmed in such a way that, it mimics the activities of a human by providing a set of rules in the form of a knowledge base and an inference engine to take appropriate decisions automatically.

Wearable Computing: It is the study and application of sensory and computational devices as wearable in order to get the person’s current body condition attributes such as temperature, pressure, oxygen level etc. Typical examples are: Smart Watches, Smart Clothing, etc.

Chatbots: They are software applications that acts as an agent to perform a particular activity. It uses AI and Natural Language Processing (NLP) to interact with the users based on their needs and it guides the users to get the desired outcome. Example: Siri, Sales Assistants in E-Commerce Sites, etc.

Automated Image Diagnostics: It applies computer assisted mechanism to get the results of medical images taken for a patient to derive results by applying systematic procedures which are provided in the form of software.

Data Analytics: It is the science of applying algorithms to analyze the unprocessed raw data to get some fruitful conclusions. Several techniques are applied to the data analytics process. Statistical analytics is one of the ways of getting insight on the data that is derived from an application domain such as Finance, Share Markets, Manufacturing departments, etc.

Virtual Medical Assistant: Generally this assistant can be a human or a robot or a device that acts as a medical professional and sits with the patient to help the patient, medical practitioners and relatives of the patients by performing daily recurrent activities. It is generally a remote employee that works in a remote location to give the regular updates of the patient and taking care of the patients based on the current diagnostics results.

Smart Healthcare: It is a semi/fully automated service that apply computer technology such as IoT, Internet Notifications, Wearable Devices, Mobile networks, Cloud services with Artificial Intelligence and Machine Learning to provide an efficient healthcare solution.

AutoML: Automated Machine Learning (AutoML) helps non-machine learning experts to apply the different ML algorithms to get the efficiency level in applying them for a particular domain.

Predictive Analytics: It is the branch of data analytics to get predictions on the future by using historical data with current conditions by means of applying statistical algorithms, machine learning algorithms etc. Example: Sales forecasting, Share market price forecasting, Weather forecasting, etc.

Machine Learning (ML): It is the subset of Artificial Intelligence which helps the system to learn from the dataset without having any specific programs.

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