🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Healthcare is essential in daily life. Unfortunately, consultation with a doctor can be difficult to obtain, especially if we need advice on non-life threatening problems. The proposed idea is to create a system with Dialog Flow that can meet the patients requirements. Healthcare chatbot is built with medical applications having the potential to reduce healthcare cost and improves accessibility to medical knowledge by a simple chat through method. Chatbots are useful for patients and for those who want to learn more about health. The real benefit of the chatbot is to provide advice and information for an healthy life. A text-to-text diagnosis engages patients in conversation about their medical issues and provides a personalized diagnosis based on their symptoms and also sets appointment for the user. Hence, people will have an idea about their health and have the right protection and prevention.

To provide a free service of interaction with a machine, the objective of “Emotionally Aware Chatbot” is to provide mental healthcare to those who are mentally-ill anywhere and anytime. It raises the question of what role, if any, the chatbot should play in suicide prevention. With this chatbot, we expect to reach as many mentally-ill people as possible by hosting this on web domains of known hospitals or counselors. If it is easy to detect mental health issues at the correct time and provide suitable help, it might save precious lives. If integrated with the hospital systems, this would provide an effective way to automate the work.

A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives). According to Forrester (2015), AI will replace 16 percent of American jobs by the end of the decade.Chatbots have been used in applications such as customer service, sales and product education. However, a study conducted by Narrative Science in 2015 found that 80 percent of their respondents believe AI improves worker performance and creates jobs.[citation needed] is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities. Background

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The proposed idea is to create a health care chabot system using Artificial Intelligence (AI) that can diagnose the disease and provide basic details about the disease before consulting a doctor. The system provides text (or) voice assistance that means user can use own convenient language. Bot will provides which type of disease you have based on user symptoms and appeared doctor details respective to user disease. The Chabot will clarify the users symptoms with serious of questions and the symptom confirmation will be done. The disease will be categorized as minor and major disease. Chatbot will reply whether it is a major or minor disease. If it is a major disease user will be suggested with the doctor details and analagesics for further treatment and also provides food suggestion that means which type of food you have to take. The user can achieve the real benefit of a chatbot only when it can diagnose all kind of disease and provide necessary information. A text-to-text diagnosis bot engages patients in conversation about their medical issues and provides a personalized diagnosis based on their symptoms. Hence, people will have an idea about their health and have the right protection.

Through chatbots one can communicate with text or voice interface and get reply through Artificial intelligence. Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as ecommerce customer service, call centres and Internet gaming. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of Artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for.

This system combines the power of conversational chatbot interfaces with advanced disease prediction algorithms, enabling users to receive real-time medical guidance and identify potential health issues at an early stage

eShangazi is a knowledge-sharing system (ChatBot) that aims at informing, educating and advising youths about Sexual and Reproductive Health (SRH) via Facebook Messenger, Telegram, Skype, Slack, SMS and Google Hangouts.

This Git repository contains an AI-powered virtual mental consulting system. The system uses NLP and ML techniques to provide personalized mental health support through modules such as emotion recognition, personalized therapy recommendations, chatbot-based counseling, mental health prediction, and virtual peer support groups.

Artificial enabled healthcare chatbot systems are used recognize the diseases based on most comman symptoms by interaction with machine. Healthcare chatbot system will also provide the possible medicine prescription.Artificial healthcare chatbot system uses neural networks for calculating the output from the given input using the repeated iterations while training the data after it will use natural language processing for understanding the human language. Artificial healthcare Chabot system can understand the text based input as well as voice interface to communicate with the patients.This system will be very useful because it can take the decisions faster and it will also reduce the cost charged by the doctor. Although it can’t replace the doctor but it will help to provide the treatment to the most common diseases. Artificial enabled Chabot system will ask the questions in the series about symptoms to diagnose thee health condition of the patients. It will reduce the cost of the diagnosis as compared to doctors without appointments. Artificial intelligence healthcare Chabot systems are revolutionary technology that is leading the way in transforming the medical diagnosis systems.

This system combines the power of conversational chatbot interfaces with advanced disease prediction algorithms, enabling users to receive real-time medical guidance and identify potential health issues at an early stage.

Overview Virtual Assistant is an application program that understands natural language voice commands or text commands and completes the tasks for users. Virtual Assistants features a human interface system, they can understand the language and meaning of what the user is saying and have built in replies. Learn from different instances so that they can have a long term human interaction. It uses artificial intelligence to learn things from different situations. Using AI they can recognize, predict and classify based on analysis. Purpose Virtual Assistant provides various services. It is ready to help wherever you are and can be deployed in your devices. Wider scope and perform users to get answers to their questions and perform tasks using voice or text commands, all in an interactive form. Precise voice and text recognition with the ability to have conversation with the users. In case of Google assistant, they recognize the voice of the user and perform the specific task. Use case Customer support: Rather of customers waiting for a long to solve an issue, the can get instant support from chatbot, Banking Chatbots: Personalized banking with an aim to improve customer satisfaction and engagement. Project support: Can send notifications for various tasks. Reminder to follow up with an action. HR assistants: Can help employees register time off, retrieve company policies, and find answers to repetitive employment questions. Teaching: Can helps teachers to create more detailed learning plans and materials. Being full-blown health assistants: Virtual assistants can do so much more than giving tips, they can often help patients apply simple treatments, remind them to take medicine, and monitor their health. Automating FAQs and administrative tasks: If there's a scenario where the customers have dozens of repetitive questions, virtual assistant is there 24/7 to answer questions from people who may be anxious to get answers. Technical support: The customer has a product technical error, in this case, asks the customer to type the error they encounter, then it generates a dynamic link to search the customer input words in the technical knowledge repositories and guide the customer through his search. Efficient Processes: Make processes more streamlined and transparent by synchronizing between functions, roles, and departments. Booking: A virtual assistant can respond to a consumer through messages, web, SMS or email and update them on the status of their existing reservation, make changes to the reservation, process related payments or refunds, send proactive notifications and provide detailed information on their itinerary. Features a. NLP Text Search : Virtual assistant concentrates on NLP and NLU. Understands the slang that is used in everyday conversation and analyses the sentiments to enhance a better set of communication. b. FAQ voice assistant : FAQ voice assistant is a voice assistant that provides a list of questions and answers relating to a particular subject. c. Conversations voice assistant : Conversations voice assistant is a voice assistant that provides conversational services based on a subject. d. Speech conversations (STT,TTS) : It provides conversational services such as speech to text and text to speech. e. Integration with Enterprise Systems : It provides administrative service to clients. Such as scheduling appointments, making phone calls, making travel arrangements, managing email accounts etc. f. Rich Conversations : Rich conversation is a conversation that can use different features such as images, videos, buttons, forms etc. a) Images:Imagescanbesentorreceivedduringconversations. b) Buttons:Buttonscanprovidedifferentfunctionalitiesasperthefeatureofthebutton. c) Videos:Videoscanbesentorreceivedduringconversations d) Forms: Forms help to give visible shape or configuration of something. Technical Requirement g. HTML5 h. JavaScript i. Python (Flask API, NLP Packages) j. MySQL k. Docker l. Git

AI-Driven Public Health Chatbot for Disease Awareness A multilingual Streamlit-based chatbot that educates users about preventive healthcare, disease symptoms, vaccination schedules, and outbreak alerts. The system uses datasets hosted on GitHub and provides easy web access, designed to support rural and semi-urban communities.

COVID-19 Vaccination Passport System with AI chatbot support and scalable serverless architecture for secure health record management

A virtual assistant is an independent contractor who provides administrative services to clients while operating outside of the client's office. A virtual assistant typically operates from a home office but can access the necessary planning documents, such as shared calendars, remotely. People employed as virtual assistants often have several years of experience as an administrative assistant or office manager. New opportunities are opening up for virtual assistants who are skilled in social media, content management, blog post writing, graphic design, and Internet marketing. As working from home has become more accepted for both workers and employers, the demand for skilled virtual assistants is expected to grow. In today’s era almost all tasks are digitalized. We have Smartphone in hands and it is nothing less than having world at your fingertips. These days we aren’t even using fingers. We just speak of the task and it is done. There exist systems where we can say Text Friend, “I will Call you Letter.” And the text is sent or any one may make call by saying “Call To Mr. Roy” That is the task of a Virtual Assistant. It also supports specialized task such as booking a flight, or finding cheapest book online from various e-commerce sites and then providing an interface to book an order are helping automate search, discovery and online order operations. An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term "chatbot" is sometimes used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal (spoken?) commands. A similar concept, however with differences, lays under the dialogue systems. The project aims to develop a personal-assistant for Linux-based / Windows systems using Python, Jupitar Notebook, PyCharm. Voice Assistant using Python draws its inspiration from virtual assistants like Cortana for Windows, and Siri for iOS. It has been designed to provide a user-friendly interface for carrying out a variety of tasks by employing certain well-defined commands. Users can interact with the assistant either through voice commands or using sound or Voice input input. As a personal assistant, this system assists the end-user with day-to-day activities like general human conversation, searching queries in google, Bing or yahoo, searching for videos, retrieving images, live weather conditions, word meanings, searching for medicine details, health recommendations based on symptoms and reminding the user about the scheduled events and tasks. The user statements/commands are analyzed with the help of machine learning to give an optimal solution.

Abstract: Chronic diseases have affected many people globally and causing morbidity, and modality—the impact of chronic disease related to people’s lifestyles and their choices of habits like food, among others. Also, a model that immune and improves patients' lifestyle and habits by giving guidance and follow-up for future symptoms. As seen that there is high demand for specialists/experts in that field. Only a few studies have considered reducing the workload and exhaustion of physicians during the treatment phase. This article aims to provide a preliminary assessment of the Chatbot health system, a conversational agent-assisted health guiding framework to help doctors cope with burnout and provide continuous treatment to their patients. The approach is taken to create the chatbot dialog, and the guiding system is presented in this paper. As a result, talk about the first patient profiling technique for classifying patients based on their cholesterol levels. The machine then makes recommendations to the expert on tasks to include in the patient’s guidance in dialogue. This study makes three significant contributions to disease complications prevention (i.e., preserving a healthier lifestyle): (1) It provides the specialist/expert with a conversational agent to support patients; (2) It reduces experts’/specialists’ workload and safe their time to improves patient care by categorizing patients into groups, each group has its case scenario, that would help to follow up with patients until he visits a clinic/hospital for drugs or meeting doctors face-to-face; and (3) It gives the physician physical/nutrition activity guidelines when creating a general patient activity guiding choices based on his cholesterol level whether its normal, abnormal, or risk.

Healthcare is very important to lead a good life. However, it is very difficult to obtain a consultation with a doctor for every health problem. The idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbots. Chatbots are computer programs that use natural language to interact with users. The chatbot stores the data in the database to identify the sentence keywords and make a query decision and answer the question. Ranking and sentence similarity calculations are performed using n-gram, TFIDF and cosine similarity. The score will be obtained for each sentence from the given input sentence and more similar sentences will be obtained for the query given. The third-party, the expert program, handles the question presented to the bot that is not understood or is not present in the database.

The Medical Llama Chatbot is an AI-powered conversational agent providing medical information and guidance. Leveraging LLama 2 7B model in local system(offline), it offers an interactive user experience for health-related queries.

StressRelief-AI is an AI-powered mental health support system for students facing stress, anxiety, and burnout. It offers an AI chatbot for emotional support, stress level analysis, mindfulness exercises, and 24/7 availability.

Artificial Intelligence has core branches like, Machine Learning which takes in data, searches patterns, improves itself using the data, and displays the outcome. To lead healthy lifestyle healthcare is very much important. In few unsocialized areas, it is quite hard to find a consultation with a doctor that easily regarding health issues. The main idea here is to make a healthcare chatbot based on Artificial Intelligence using NLP that can diagnose the disease and provide required details about the specific disease before consulting or visiting a doctor. Reduces the healthcare costs and improves accessibility to this medical chatbot. Specific chatbots act as virtual medical assistance, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a chatbot only when it can diagnose all kinds of diseases and provide the necessary information. A text-to-text medical chatbot involves patients in online conversation considering their health problems which provides a set of personalized diagnoses based on their provided symptoms. These bots connect with the potential patients visiting the site, helping them discover specialists, booking appointments, and getting them access to correct treatment. This chatbot uses Natural language processing techniques to process and analyze the data and give the output inappropriate manner. It brings up the disease-related problems about whether the task mentioned above should be assigned to human staff. This healthcare chatbot system will provide patients healthcare support online at all times. It helps to generate health data and automatically delivers the information of reports to medical management. By asking the questions in series it helps the patient by guiding what exactly the user is looking for queries.

A chatbot that predicts and provide diagnosis to the disease based on the symptoms, age of patient, and duration of prevailing symptoms.

This project focuses on developing a health monitoring system integrated with an AI powered chatbot to predict human diseases. The system continuously gathers vital signs and health-related data from users through wearable sensors or manual inputs. It analyzes symptoms and patterns using machine learning algorithms trained on medical datasets.

A user-friendly app to help connect Consumers and Farmers directly by eliminating the need of middlemen. Enhanced with Gordon - our AI ChatBot trained to give recipe suggestions, nutritional facts and everything related to food. Plant Health Detection system helps farmers monitor their crop health using CNN.

MindCare: A Mental Health Intervention System, which makes use of Rasa Framework In-order to build a conversational chatbot.

MedMinder is a small-scale healthcare management system allowing users to interact with an AI chatbot to schedule appointments, summarize notes, review medications, and visualize health metrics.

An AI-powered multilingual public health chatbot designed to educate rural and semi-urban populations about preventive healthcare, disease symptoms, and vaccination schedules. The system also includes a Proactive Disease Monitoring Module that detects unusual disease query patterns to identify potential outbreaks early.

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This Team Project is regarding Mental Health Care Chatbot System to investigate how chatbots can help patients when required resources are not available. The goal is to ensure automatically providing online assistance on frequently asked questions, and companionship to the people in need.