Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 3947-3951
Author(s):  
R. Vineeth ◽  
R. Rithish ◽  
D. V. S. N. Sai Varma ◽  
B. V. Ajay Prakash

In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.

2019 ◽  
Vol 11 (2) ◽  
pp. 125-35
Author(s):  
Anna Meiliana ◽  
Nurrani Mustika Dewi ◽  
Andi Wijaya

BACKGROUND: Giant transformations are going on currently in health care, and the greatest force behind this phenomenon is data.CONTENT: Big data has arrived into medicine field, lead to potential enhancement in accountability, quality, efficiency, and innovation. Most updated, artificial intelligence (AI) and machine-learning (ML) techniques rapidly developed, bring forth the big data analysis into more useful applications, from resource allocation to complex disease diagnosis. To realize this, a very large set of health-care data is needed for algorithms training and evaluation, including patients’ treatment data, patients respond to treatment, and personal patient information, such as genetic data, family history, health behavior, and vital signs.SUMMARY: Precision Health involving preventive, predictive, personalized and precise. The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes, thus the patient–doctor bond is expected to be reformed and not be eroded.KEYWORDS: artificial intelligence, machine learning, deep learning, electronic health records, big data


2021 ◽  
Vol 24 (3) ◽  
Author(s):  
Jonas Almeida Rodrigues ◽  
Henrique Dias Pereira dos Santos

Everyone who uses any digital platform in the daily routine has already been surprised by some sudden ad or product advertisement about which some information has been sought on the Internet. Coincidence? Of course not! This is just one example of how artificial intelligence is inserted into our daily lives. It is in the platforms for music streaming, movies, shopping for any product, in traffic applications, in the stock market. Each "like", each share, each post shows a pattern of consumer preference, a characteristic that can be used to direct advertisements in order to advertise or market a product to a specific target. This is already happening, it is not part of the future. Artificial intelligence is already part of our present.   But how do these platforms manage to "guess" our preferences or tastes and hit exactly what we were looking for? In reality nothing is guessed, it is learned. Through computer modeling, these systems learn from the examples that we ourselves give them. We feed these systems on a daily basis. Just like children, who learn many things by example (languages, for instance) before they even go to school, these systems are also capable of learning. A child learns that a dog is different from a cat when it sees examples of several dogs and several cats. So a child can learn the differences between both animals. Algorithms learn the same way, through examples. This is what we call "machine learning," a sub-area of artificial intelligence (AI). It is an advance for society, but it must be applied with ethics and transparency (see the Netflix documentary Coded Bias).   Moving away from the market sphere and thinking about health care, machine learning has also been widely employed, because these systems have the ability to learn using endless amount of patient and hospital data (Big Data). In this sense, AI-based systems have been developed aiming at improving patient care, from the organization of triage systems at clinics and hospitals, patient scheduling, organization of test result delivery, preventing errors in drug prescriptions, as well as predicting and assisting in disease diagnosis. The artificial intelligence literature in the medical field is already vast. In dentistry, research has focused on the use of convolutional neural networks (CNN) in dental radiology. Tools are produced for researchers and system developers that aim at assisting clinicians in imaging diagnosis, for example, of dental caries, periapical lesions, bone resorption, among other important outcomes.   Some companies, in Brazil and worldwide, have already seen a potential market in the application of these neural networks, and are providing software to assist in the analysis of radiographic images. Far from being able to replace health professionals, this technology should be used to improve the work of dentists and bring more security in diagnosis. Trying to replace a health professional with artificial intelligence, especially in dentistry, is impossible and not productive at all (see Eric Topol's book Deep Medicine).   Information technology as an ally will bring many benefits to dentistry, not only in radiology. The analysis of digital cohorts (electronic patient records) with machine learning algorithms can bring new insights to Science. Such algorithms are able to cross-reference thousands of predictive attributes with various endpoints to define which information is most relevant for qualitative analyses. It is the new advanced statistics.   For this reason, it is especially important to emphasize the need to build a large-scale public dental dataset to make the clinical application of AI possible. The challenge now is to improve the quality of the datasets to build really accurate machine learning algorithms. Finally, it would be very useful for dentists if these developed machine learning systems become applications that could be widely available and spread to the dental community.   The spectrum of AI is huge! Try doing a search today on some topic and wait for the algorithm to work! It will offer you all the information, based on the search example you yourself have offered! This is AI in our lives, no future, but a present!   Keywords Artificial intelligence; Health care.


Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.


2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.


2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.


2020 ◽  
pp. 57-63
Author(s):  
admin admin ◽  
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...  

The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.


Author(s):  
Namik Delilovic

Searching for contents in present digital libraries is still very primitive; most websites provide a search field where users can enter information such as book title, author name, or terms they expect to be found in the book. Some platforms provide advanced search options, which allow the users to narrow the search results by specific parameters such as year, author name, publisher, and similar. Currently, when users find a book which might be of interest to them, this search process ends; only a full-text search or references at the end of the book may provide some additional pointers. In this chapter, the author is going to give an example of how a user could permanently get recommendations for additional contents even while reading the article, using present machine learning and artificial intelligence techniques.


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