A Hybrid Approach to Develop and Integrate Chatbot in Health
Informatics Systems
Abhijat Chaturvedi, Siddharth Srivastava, Astha Rai and A. S. Cheema
Centre for Development of Advanced Computing, Noida, India
Keywords:
Chatbot, Health Informatics Systems.
Abstract:
In this paper, we develop a chatbot that seeks free-form natural language queries by its users for blood and
related services such as list of blood banks, live blood stock, blood donation camps etc. with one or more
parameters as search criteria. The queries can be both Frequently Asked Questions (FAQs) and data driven
including location based services. The uniqueness of this chatbot lies in the fact that its architecture provides it
flexibility to evolve to encompass more domains and services without having any impact on existing services.
Moreover, with approximate keyword initialization, the proposed chatbot can smartly infer from incomplete or
incorrect queries by the users as well as has the ability to learn abbreviations. The bot achieves this by leverag-
ing state-of-the-art deep learning and natural language understanding algorithms at the back-end. Specifically,
this bot uses a hierarchical approach for parsing queries. At first level, it parses the query into intents i.e. FAQ
or data driven. If the classified intent is a FAQ, chatbot to respond while, if it is amongst many of the citizen
centric queries, it drills down through the query to identify the entities such as city etc. along with the type of
the service and returns the users with the required details.
1 INTRODUCTION
As the reachability of various health informatics so-
lutions are increasing, the usability and effectiveness
of such systems have become important. While user
interfaces and workflow play an important role in ac-
ceptance of such systems, but letting the users query
the system with natural text as input and giving them
the appropriate output such as data, help text, ex-
cerpts from user manuals, filtering their query for
more inputs, is becoming the next level of expertise
required in health informatics solutions. The chat-
bots are a natural solution to this problem (Chung
and Park, 2019). Chatbots brings the ease of use
along with 24X7 service at a fraction of what a hu-
man employee would require. This enhances the op-
erational efficiency at a reduced cost. As argued by
Petter Bae Brandtzaeg in his study(Brandtzaeg and
Følstad, 2017) people prefer chatbots because of pro-
ductivity. Chatbots in health care provides a variety
of solutions from symptoms checking (Divya et al.,
2018) to getting basic health information (James and
Vales, 2009). The AI provides the edge to the chatbots
over any other technology, because they can learn and
grow over time. This saves the time and money of
both patients and health care provider. Thereby, cre-
ating a smooth health care experience.
However, most of the chatbot solutions work on
static data or pre-defined workflows which may cater
to specific requirements. Therefore, in this work,
we propose a modular chatbot framework for re-
quirements suiting to health informatics solution. To
demonstrate the effectiveness of the framework, we
take a ”Blood Bank Management System” as a use
case. Specifically, the considered blood bank man-
agement system has two modes of usage. The first
is via a citizen centric portal where details on blood
banks, blood stock, information etc. are given. The
second is the set of users using the application for
day to day use such as blood bank staff. Therefore,
we implement the proposed framework for address-
ing queries from citizens via a nationwide public web
portal as well as the users of a blood bank manage-
ment system.
Figure 1 shows the workflow of queries from a cit-
izen centric portal for a blood bank within the pro-
posed architecture. It is important to note that, the
queries from both sets of users (citizens, application
users) are different. While we can limit the scope of
queries from internal users of the health informatics
solutions such as blood bank staff in the considered
use case, it is extremely difficult and impractical to
control or limit the queries from citizens on a public
774
Chaturvedi, A., Srivastava, S., Rai, A. and Cheema, A.
A Hybrid Approach to Develop and Integrate Chatbot in Health Informatics Systems.
DOI: 10.5220/0009188007740781
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 774-781
ISBN: 978-989-758-398-8; ISSN: 2184-4305
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
portal.
In view of the above, the contributions of this pa-
per are:
1. A generic chatbot framework for health informat-
ics solutions and implementation in a nationwide
blood bank management system.
2. Formalizing common problems and their solu-
tions while implementing chatbots.
The rest of the paper is organized as follows, in
Section 2 we discuss and compare various types of
chatbots. In Section 3, we formally define the prob-
lem, the challenges and relevant works. Section 4 de-
scribes the proposed architecture while Section 5 dis-
cusses the results. In Section 6 we discuss possible
extensions of the proposed architecture followed by
conclusions in Section 7.
2 TYPES OF CHATBOTS
On the basis of workflow, chatbots can be classified
into three categories as follows
Menu based Chatbots: Menu based chatbots
work on the principle of decision trees. The hier-
archies are used to get the next stage of input from
the user and thereby classify the next best action
based on the input received from the user. These
chatbots full fill the basic search of the queries
but the advance search with multiple variables can
not be handled from such type of chatbots. Also,
the menu based chatbots fall short on the number
of clicks required for getting the desired informa-
tion from the bot. It requires maximum number
of flows to get the desired information among the
three types of chatbots discussed in this paper.
Keyword based Chatbots: These types of chat-
bots works on the identification of keywords.
Once the input is received from the user, it tries
to map with the existing set of keywords and once
a match is found the corresponding logic is exe-
cuted. This type of chatbots gives an illusion of
understanding the user. However, it just identifies
the keywords in the user’s statement. Keyword
based chatbots fails when there is multiplicity in
the keywords and one keyword falls under differ-
ent intents.
Like in the case of blood stock search and blood
donation camp search. In both the intents, key-
words are same such as blood and search. Sim-
ilarly, the FAQ’s about blood donation also have
many common keywords.
Contextual based Chatbots: Contextual chat-
bots understands the context of the conversation
and therefore are the most advanced form of chat-
bots. These bots utilize Machine Learning (ML)
and Artificial Intelligence (AI) to remember con-
versations with specific users to learn and grow
over time(Xiaojiang, 2014). These chatbots fills
the slots once the information is available and then
uses the same slots to understands the context of
the conversation. This helps the user because the
bot understands the history and flow of the con-
versation and remembers the information it has
received from the user.
A common example of flow of the contextual
chatbot is when user asks the availability of B pos-
itive blood in Delhi. The bot replies the stock list
of blood banks. Then the user inputs and in mum-
bai?. The bot understands that the user is asking
for availability of B positive blood.
3 BACKGROUND
3.1 Problem
While the paper aims at developing chatbots for
health informatics solutions and the proposed archi-
tecture is applicable to development of generic archi-
tectures, for clarity of the readers, we limit the scope
of the discussion for next two sections w.r.t. a blood
bank management system. Blood search, traditionally
was done over the web in the drop down select type
method, which is convenient but not enough as typing
a random query over the app. The same goes with the
frequently asked questions. Users do not want to read
all the question, if they want to know the answer of
just one. Therefore, to reduce the time, effort and ease
the complexity for users, a system is needed which
can be interactive and provide relevant information by
understanding the language of the user, who may or
may not be accustomed to the system. Therefore, we
propose to use chatbot for interacting with the users
and solving their queries for getting information or
providing them help while using the system.
3.2 Challenges
One of the biggest challenge in the process of cre-
ating the bot was the understanding of the user’s in-
tention in the different sounding statements. For in-
stance, the statements, ”Look for blood in Delhi” and
”I am looking blood banks in Delhi, India”, may have
different selection of words, but the intention of the
A Hybrid Approach to Develop and Integrate Chatbot in Health Informatics Systems
775
Figure 1: Workflow of the proposed framework.
user in both these sentences is the same. In both the
cases the user wants to search the blood for donation
in Delhi.
The correct mapping of the questions in the FAQ
section was another major challenge. In practice, in
any application, initially the FAQs are limited and
they grow as the application is developed over the
years and also with the number of users. Therefore,
with limited amount of questions in the beginning, it
is difficult to correctly relate the user’s query to the
nearest question available in the dataset. For exam-
ple, when the questions in the dataset can be sim-
ilar (”Why should I donate blood?”, ”When to do-
nate blood?”) and the input from users can be lim-
ited (”Why donate blood?”), the algorithms usually
provide incorrect output as most of the sentences in
the dataset contain the word blood and when, why
(”When to donate blood instead of plasma?”).
3.3 Relevant Works
There has been no attempt at making a blood donation
search chatbot. Although there are many FAQ chat-
bots as well as many chatbots are tailored for health-
care industry, most of them are for disease prediction
like Mandy (Ni et al., 2017) and Quro (Ghosh et al.,
2018) or act as a counselling service (Lee et al., 2017).
The majority of these FAQ chatbots deploy a bag-of-
words model to predict the answer of the question en-
tered by the user. This technique, is however, not al-
ways effective especially in the cases where the length
of the questions is comparatively large.
3.4 Motivation
Ability to get information on availability of is a task
of utmost urgency. We wanted to provide a way that
is fast, easy to use and reliable when it comes to the
question of life and death. Hence we choose to create
a chatbot because that is the most easy way for a non
technical person to search for blood when required.
Also, every business has its own challenges and
requirements, it is not possible to fit one solution to
every problem. Therefore, we wanted to provide a so-
lution for the better engagement of the users on the
business, through chatbots. Considering the major-
ity of the people who do not understand the complex
of higher machine learning, artificial intelligence and
natural language processing, we created a modular
approach to create a chatbot, which can be used as-
is or extended by adding, removing or modifying the
existing modules.
4 METHODOLOGY
The chatbot can is made up of two parts, one being the
FAQ section which handles the FAQs and the other be-
ing the custom section for all the other queries of the
user. In all total this bot handles 13 different intents.
The architecture of the bot is displayed in Figure 2.
HEALTHINF 2020 - 13th International Conference on Health Informatics
776
Figure 2: Architecture.
4.1 Intent Parsing
The first step that is made in the process of chat-
bot providing an answer to the user’s query, once the
user’s input is captured is the intent classification or
intent identification. For this, Snips NLU is deployed.
Snips reads the text and based on it’s Deterministic In-
tent Parser(DIP) and Probabilistic Intent Parser(PIP)
classifies the intent and the corresponding entities as
explained by Coucke et al. in 2018 (Coucke et al.,
2018). The step is vital since the output of this step is
the intent or the expectation of the user from the bot.
This output is the input to the next step in the process.
The Snips algorithm reads the text entered and
tries to map the statement with the statements pro-
vided in the training set. This is done by the com-
ponent called Deterministic Intent Parser(DIP). This
is swift and accurate segment of the Snips algorithm.
However, in most cases it is not possible to get the ex-
act statement as provided during the training phase.
Therefore, the second segment Probabilistic Intent
Parser(PIP) is put into place. If the DIP fails to pre-
dict the intent and entities PIP is initiated and on the
basis of probability model, it identifies the intent and
entities of the user’s statement. See this structure in
fig 2.
Once the intent is classified, next comes the task
of generating the response corresponding to the user’s
query. This is done through a series of custom pro-
cessing, business logic and api calls.
4.2 FAQ Section
To overcome the limitation of scarcity of data avail-
able to train, we used two fold mechanism. One is
to generate similar sounding questions from all the
questions in the dataset. This helped a lot in identi-
fying the correct question and thereby correct corre-
sponding answer that the user wants. Second was to
put the bot to use by actual users. We therefore pre-
sented some different users who used and queried the
bot just like an actual users. Each of them used the bot
in all intents and all use cases. Further processing the
FAQ’s multiple options were available, including ex-
ternal libraries and internal machine learning model.
In order to keep it simple and easy to use, we prefer
pre-build libraries rather than creating a ML model
from scratch. For this, many options were available
(Janarthanam, 2017), including DialogFlow and MS
Azure.
As studied by Canonico et al. in 2018 (Canonico
and De Russis, 2018), DialogFlow is the best avail-
able cloud based chatbot engine. It has high usability,
pre-build entities and pre-build intents. Also, it has
Default Fallback intent, which comes handy when an
unexpected statement is encountered.
The setup of DialogFlow started with the creation
of a Google account and creating an agent that will
process the information and produce the result. Once
done, the FAQ’s has to be uploaded in the csv format.
The DialogFlow, automatically learns the intents and
entities and trains the agent.
4.3 Custom Processing
For the search of blood along with the quantity and
camp search, the custom processing section is pro-
vided. This section basically handles the queries of
the blood search. Once the intent is classified the cor-
responding code gets invoked and the API calls are
made to the e-Rakt Kosh Web Services.
The user’s query contains the intent and the enti-
A Hybrid Approach to Develop and Integrate Chatbot in Health Informatics Systems
777
Figure 3: Chatbot framework based on Snips NLU architecture.
ties for the blood search. These intent and entities are
identified by the Snips algorithm and upon identifica-
tion the API of the e-Rakt Kosh web services are in-
voked, the API returns the data in json format, which
is again parsed by the custom processing segment in
order to present the desired data in the desired format.
Intents like mobile apps for download and help are
also included for the better user aid. Quantity bound
blood search is an important part where most user en-
gagement is expected. Intent to contact the service
provider is also put into place so as to give the user
a smooth experience while chatting. This way users
need to put a single statement for getting the quantity
of blood available in a particular blood bank.
4.4 Analytics
MD Mulvenna et al. presented a way for analysing
the chat logs (Mulvenna et al., ). For the sake of fu-
ture development and to understand the usage pattern
of the users, we have deployed the analytic mecha-
nism for the bot. Through this we want to capture the
insights such as the most searched question or which
geographical area is using the bot most. This will help
us in creating a better user experiences by enhancing
the search and service accordingly.
The chat logs are captured in the SQL database,
from where it can be accessed for the analysis and
usage pattern understanding. Actionable analytics are
of immense help in expanding the services and user
experience in any business. The bot usage insights
will result in the better and smooth user experience,
which in turn enhance the user engagement.
4.5 Workflow
For instance the user enter the query, ”Look for ab+ve
blood in delhi”. The very first step in the process will
be the parsing of the sentence in the Snips algorithm
which will classify the intent of the user, which in this
case is, Stock Search. The entities will be identified
as ”A+ve” and ”Delhi”. As they both are necessary
for the fetching of the list of blood banks.
Once the intent is identified, it will lead to the cor-
responding segment to be initiated to generate the cor-
responding answer. Here, it is important to note that
chatbot handles only one intent at a time. This allows
the bot to be simple and light weight.
Since the intent here is Stock Search, therefore
the bot will try to get the answer for that. The blood
group is identified as ”A+ve” and the city as ”Delhi”.
Now, the bot will get the geocodes of the said loca-
tion from Google Maps API. The geocodes are then
sent to the e-Rakt Kosh web service, which returns
the blood banks in the area.
Similarly, in case of fetching the list of blood
banks along with the minimum required quantity of
blood. The work-flow remains the same except after
fetching the list, the bot filters out the blood banks
which have less than required quantity of blood units.
See workflow in fig 3.
On the similar lines, if the statement is like ”how
much o+ve blood is avaiable in fortis hospital ,
noida”, the entities that will be extracted will be ”For-
tis hospital” as the name of the blood bank, ”O+ve” as
blood group and ”noida” as city. However the intent
will be count search instead of simply stock search.
Some other less used intents are also put in places
for one stop solution for the users. These includes,
a help intent which guides the user to use the bot in
HEALTHINF 2020 - 13th International Conference on Health Informatics
778
Figure 4: Workflow.
easy steps. Notification intent is also present to fetch
any latest notification about the blood bank portal.
4.6 Chat Logs
Every interaction with the bot is logged in the e-Rakt
Kosh database. This data is used for analysis of the
user engagement and enhancing the bot’s quality. The
questions asked by the user are directly put into ta-
bles. Corresponding to which intent as classified by
the bot is put in the table along with the response from
the bot. Once the chat is started the logging process
begins.
4.7 Integration
Once developed the bot needs to be integrated with
the erkatkosh website. This was the biggest challenge
of the project. The website is developed in java and
the bot is developed in python. Integration of the both
causes technical glitches and inefficiency.
Two clear options were available for the integra-
tion of the code. One was to compile the python code
in java and generate a java byte code. This is however
not a suitable mechanism because of its resource in-
tensiveness. The other method was to use api’s. This
way it will be easy to maintain and less resource in-
tensive. The most common to use python server is
flask(Vogel et al., 2017)(Lokhande et al., 2015) which
hosts the application. Therefore we had to create an
instance of flask and use it’s api’s to get the response
to the main page in e-Rakt Kosh website.
At the front end, there is the html, javascript and
css page for e-Rakt Kosh website with a popup of the
chatbot. This captures the users’s statement in the
textarea. Following it is the api call that sends this
statement to the flask application running on another
port on the same server through post method. The
flask application is the bot application itself. It re-
ceives the input processes it through business logic
and machine learning algorithm it has and sends back
Figure 5: Flask API Flow.
the generated response. This structure is shown in fig-
ure 4. Flask API structure is shown in figure 5.
The simple and easy to use features are the key
components of the bot. This is to ensure that users
with all technical literacy are able to use the bot. Min-
imum button layout is preferred so as to clear the clut-
ter and give the user a smooth experience.
5 RESULTS
In 1950, Alan Turing proposes the Turing test for
the intelligent machines (Turing, 1950). Much later
in 2016, Zhou Yu et al. proposes the crowd source
method of evaluating the chatbot (Yu et al., 2016).
They propose a method of taking user feedback on the
chatbot usage. They argued that with modern chat-
bots, which are much more capable and there is no
end to the chat(in general chatbots) the Turing test
is not enough for performance evaluation. Marking
the chat as satisfactory or not, and whether the user
is willing to use the service again, provides a mecha-
nism to evaluate the chatbot.
We have used the first method in our result fixa-
tion. A dataset of random 206 questions from all in-
tents is used along with some questions that does not
fall in any intent. Such a dataset is put to test the in-
tent classification of the bot. Out of the 206 random
questions, the bot correctly answered 179 questions,
while 26 questions were not identified by the bot. This
actually provides a near 87 percent accuracy.
However merely intent classification of the user’s
statement is not enough for the bot. Generating the
A Hybrid Approach to Develop and Integrate Chatbot in Health Informatics Systems
779
correct statement for response is vitally important. In
order to check the correct response generation, the
same dataset is used to test the accuracy of the bot.
Out of 206 questions, the bot accurately answered 175
question working at an approx accuracy of 85 percent.
The dataset is entered in the database and a script
is used to fetch the question from this testing dataset
and putting it into the bat and finally saving the bot’s
response back in the database. This created a table
with a set of random question and answers on the ba-
sis of the chat session.
The second technique proposed by Zhou Yu et al.
is based on the capturing of the user feedback. This
evaluation technique is however, more subjective and
user centric, therefore the results vary to the great ex-
tent. They propose a system where the users are able
to rate the bot after a set of interaction with the chat-
bot.
Table 1: Intent Classification and Response generation re-
sults.
Results
Measure Correctly
Identified
Incorrectly
Identified
Accuracy
Intent Classi-
fication
179 27 86.89
Response
Generation
175 31 84.95
6 GUIDELINES AND FUTURE
DIRECTIONS
In this section, we provide a few suggestions for repli-
cating and extending our work which will be of im-
portance for complete integration of chatbots in health
informatics systems.
Analysis: The formal analysis of chatbot solu-
tions in an indistrial setting is important but is
given little attention. We recommend to setup an
R based analytic tool for the proper analysis of
the chat logs for both exploratory data analysis
and explanatory data analysis. This will provide
the business intelligence tools and graphs that are
easy to present and read information from. The
analysis also reveals the usage patterns and most
queried statements. This type of data gathering is
important for the better customization of the prod-
uct. It will also be helpful in identifying the gap
in the supply and demand of the blood in various
parts of the country.
Voice based Chat: The chatbots can be extended
with TTS(Text to Speech) and STT(Speech to
Text), making it further easy for anyone to use
it. The voice capturing can be of immense help
when being used in mobile devices, thereby tap-
ping a huge market. There has been some of the
works in this area as well. Works of Tsiao et
al(Tsiao et al., 2007) and Emerick et al(Emerick
et al., 2015) notable in this area. The voice based
search combining with the regional language sup-
port will open this product to virtually everyone
holding a smartphone. In the context of this work,
this will let users search for blood in emergency
situations, irrespective of their technical literacy
and proficiency.
Language: Efforts should be made to include re-
gional languages along with English, so as to en-
able every person to use this service, thereby re-
moving the language barrier. V
¯
ıra et al (V
¯
ıra et al.,
2014) demonstrated this with Spanish, French,
and Russian apart from English.
Conversational Responses: The chatbot re-
sponds to the user messages if it is well trained on
the possible flow of the conversation. This gives
the user an illusion that the bot is actually talk-
ing to him. However, the bot is just following the
pre-trained story line with different branches as
defined by the developer team.
Other libraries such as Rasa NLU and Rasa Core
provides good enough structure to create a con-
versational chatbot(Bocklisch et al., 2017), that
keeps the track of the conversation between the
user and the bot and provides feature rich expe-
rience to the user. While we found that Rasa is
more flexible, but it is slightly complicated (de-
pendencies and required data) for quickly getting
a system up and running as compared to Snips.
7 CONCLUSION
We have elaborated our proposed system in detail.
The proposed system will bring the flexibility and
ease in the blood search at the national level. With the
use of Natural Language Processing, users can look
for blood in query format rather than looking up on
the web page. This saves time and effort which is
vital in cases of emergencies. The future plans also
includes the search in multiple languages which will
make the system more accessible for other languages
as well. The system will be the first point of contact
between a potential donor and the e-Rakt Kosh appli-
cation. It will cater the FAQ’s to the donor and clear
the doubts regarding blood donation.
We have explained its integration with the exist-
HEALTHINF 2020 - 13th International Conference on Health Informatics
780
ing e-Rakt Kosh application and the challenges that
were faced including the different language and envi-
ronment
REFERENCES
Bocklisch, T., Faulkner, J., Pawlowski, N., and Nichol,
A. (2017). Rasa: Open source language under-
standing and dialogue management. arXiv preprint
arXiv:1712.05181.
Brandtzaeg, P. B. and Følstad, A. (2017). Why people use
chatbots. In International Conference on Internet Sci-
ence, pages 377–392. Springer.
Canonico, M. and De Russis, L. (2018). A comparison
and critique of natural language understanding tools.
CLOUD COMPUTING 2018, page 120.
Chung, K. and Park, R. C. (2019). Chatbot-based heathcare
service with a knowledge base for cloud computing.
Cluster Computing, 22(1):1925–1937.
Coucke, A., Saade, A., Ball, A., Bluche, T., Caulier, A.,
Leroy, D., Doumouro, C., Gisselbrecht, T., Calta-
girone, F., Lavril, T., et al. (2018). Snips voice plat-
form: an embedded spoken language understanding
system for private-by-design voice interfaces. arXiv
preprint arXiv:1805.10190.
Divya, S., Indumathi, V., Ishwarya, S., Priyasankari, M.,
and Devi, K. (2018). A self-diagnosis medical chatbot
using artificial intelligence. Journal of Web Develop-
ment and Web Designing, 3(1).
Emerick, C. T., Logan, J. R., Bates, R. A., and Wahl, J.
(2015). Voice assistant system. US Patent 9,171,543.
Ghosh, S., Bhatia, S., and Bhatia, A. (2018). Quro: Fa-
cilitating user symptom check using a personalised
chatbot-oriented dialogue system. Studies in health
technology and informatics, 252:51–56.
James, S. and Vales, I. (2009). Personal healthcare assis-
tant/companion in virtual world. In AAAI Fall Sympo-
sium Series.
Janarthanam, S. (2017). Hands-on chatbots and conver-
sational ui development: Build chatbots and voice
user interfaces with chatfuel, dialogflow, microsoft
bot framework, twilio, and alexa skills.
Lee, D., Oh, K.-J., and Choi, H.-J. (2017). The chat-
bot feels you-a counseling service using emotional re-
sponse generation. In Big Data and Smart Computing
(BigComp), 2017 IEEE International Conference on,
pages 437–440. IEEE.
Lokhande, P., Aslam, F., Hawa, N., Munir, J., and Gulam-
gaus, M. (2015). Efficient way of web development
using python and flask.
Mulvenna, M. D., Bond, R. R., Grigorash, A., O’Neill, S.,
and Ryan, A. Hilda-a health interaction log data anal-
ysis workflow to aid understanding of usage patterns
and behaviours.
Ni, L., Lu, C., Liu, N., and Liu, J. (2017). Mandy: To-
wards a smart primary care chatbot application. In
International Symposium on Knowledge and Systems
Sciences, pages 38–52. Springer.
Tsiao, J. C.-s., Chao, D. Y., and Tong, P. P. (2007). Natural-
language voice-activated personal assistant. US Patent
7,216,080.
Turing, A. (1950). Mind. Mind, 59(236):433–460.
V
¯
ıra, I., Tesel¸skis, J., and Skadin¸a, I. (2014). Towards the
development of the multilingual multimodal virtual
agent. In International Conference on Natural Lan-
guage Processing, pages 470–477. Springer.
Vogel, P., Klooster, T., Andrikopoulos, V., and Lungu, M.
(2017). A low-effort analytics platform for visualizing
evolving flask-based python web services. In 2017
IEEE Working Conference on Software Visualization
(VISSOFT), pages 109–113. IEEE.
Xiaojiang, D. (2014). Chatbot system and method with con-
textual input and output messages. US Patent App.
13/661,040.
Yu, Z., Xu, Z., Black, A. W., and Rudnicky, A. (2016).
Chatbot evaluation and database expansion via crowd-
sourcing. In Proceedings of the chatbot workshop of
LREC, volume 63, page 102.
A Hybrid Approach to Develop and Integrate Chatbot in Health Informatics Systems
781