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Towards Building a Chatbot-Based First Aid Service in Arabic Language
Abstract
In this paper, we introduce a modern standard Arabic (MSA) conversational AI in the field of first aid to assist anyone facing an emergency case. The proposed chatbot -based model not only provide adequate medical assistance, but also engage in empathetic interactions with users. Due to the lack of a reliable dataset that fits our purpose, we collect and construct a dataset of 374 records covering 55 first aid topic. Our chatbot architecture relies fine tuning pretrained language models in its intent classification and employs textual similarity to match user queries to records from the dataset. We evaluate the proposed model based using both a quantitative approach, through performance metrics of different modules, and a qualitative approach using a questionnaire.
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Risk communication is incorporated into an emergency response system. In Thailand, the Department of Disease Control (DDC) manages emergencies through the Emergency Operation Center (EOC). As a part of the EOC, the risk communication unit provides a hotline service that delivers information to and receives complaints and concerns from the general public. During the Coronavirus Disease 2019 (COVID-19) pandemic, a chatbot, which is a type of artificial intelligence (AI) was used to support the hotline service. This paper focuses on how to design an informative chatbot for the COVID-19 pandemic period that disseminates information to the general public. The chatbot, named “COVID-19 Preventable”, was created based on the Design Science Research Methodology (DSRM) under two cycles of design and development. At the early stage of development, information from reliable sources was transformed into a question and answer system and imported to natural language processing in the Dialogflow on Google Cloud. The chatbot was the first official chatbot to communicate on COVID-19 on behalf of public health authorities. It consists of seven prompt features, namely, a situation report, how to protect yourself from COVID-19, fake news, self-screening for COVID-19, a list of nearest hospitals, the hotline number to call, and report notification. The uniquely informative and dynamic chatbot is likely to be an alternative channel for disseminating timely information on COVID-19.
The huge cost of emergency situations could have fatal effects on humanity and society, and it could present a genuine threat to both of them. In fact, most people confronted with an emergency could feel psychological trauma, which will, for the most part, change over time as they can exhibit chaotic or even turbulent behaviours. The situation could worsen in the case of a pandemic as fear and anxiety invade and spread in addition to isolation and quarantine. In this paper, we propose to build a smart assistant, called SMAD, that could detect the symptoms of an emergency case as well as symptoms of a mental disorder while analysing the natural language speech of an ordinary citizen, during and after an emergency situation using natural language processing and deep learning sentiment analysis model to track the patient’s mental state during an ongoing conversation. Our proposed smart assistant is an online human-bot interaction that could handle a variety of physical and mental circumstances of any emergency situation. The proposed approach is a smart healthcare service that consists of four interconnected modules: The information understanding module, the data collector module, the action generator module, and the mental analysis module, which is based on the sentiment analysis model performed on a social media dataset using a pre-trained word-embedding model.
Aim:
To explore the experiences and perceptions of final-year nursing students on the acceptability and feasibility of using a chatbot for clinical decision-making and patient safety.
Background:
The effective and inclusive use of new technologies such as conversational agents or chatbots could support nurses in increasing evidence-based care and decreasing low-quality services.
Methods:
A descriptive qualitative study was used through focus group interviews. The data analysis was conducted using a thematic analysis.
Results:
This study included 114 participants. After our data analysis, two main themes emerged: (i) experiences in the use of a chatbot service for clinical decision-making; and (ii) integrating conversational agents into the organizational safety culture.
Conclusions:
The findings of our study provide preliminary support for the acceptability and feasibility of adopting SafeBot, a chatbot for clinical decision-making and patient safety. Our results revealed substantial recommendations for refining navigation, layout, and content, as well as useful insights to support its acceptance in real nursing practice.
Implications for nursing management:
Leaders and managers may well see artificial intelligence-based conversational agents like SafeBot as a potential solution in modern nursing practice for effective problem-solving resolution, innovative staffing and nursing care delivery models at the bedside, and criteria for measuring and ensure quality and patient safety.
This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant ( P < 0.01 ). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant ( P < 0.05 ). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion.
The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks. The pretrained AraBERT models are publicly available on this https URL hoping to encourage research and applications for Arabic NLP.
Conversational models have witnessed a significant research interest in the last few years with the advancements in sequence generation models. A challenging aspect in developing human-like conversational models is enabling the sense of empathy in bots, making them infer emotions from the person they are interacting with. By learning to develop empathy, chatbot models are able to provide human-like, empathetic responses, thus making the human-machine interaction close to human-human interaction. Recent advances in English use complex encoder-decoder language models that require large amounts of empathetic conversational data. However, research has not produced empathetic bots for Arabic. Furthermore, there is a lack of Arabic conversational data labeled with empathy. To address these challenges, we create an Arabic conversational dataset that comprises empathetic responses. However, the dataset is not large enough to develop very complex encoder-decoder models. To address the limitation of data scale, we propose a special encoder-decoder composed of a Long Short-Term Memory (LSTM) Sequence-to-Sequence (Seq2Seq) with Attention. The experiments showed success of our proposed empathy-driven Arabic chatbot in generating empathetic responses with a perplexity of 38.6, an empathy score of 3.7, and a fluency score of 3.92.
Background
The World Health Organization declared the novel coronavirus outbreak (COVID-19) to be a pandemic on March 11, 2020. Large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan would improve preparation for and prevention of a massive outbreak.
Methods
A chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was developed using the LINE app to evaluate the current Japanese epidemiological situation. LINE users could participate in the system either though a QR code page in the prefecture’s website, or a banner at the top of the LINE app screen. COOPERA asked participants questions regarding personal information, preventive actions, and non-specific symptoms related to COVID-19 and their duration. We calculated daily cross correlation functions between the reported number of infected cases confirmed by PCR and the symptom-positive group captured by COOPERA.
Results
We analyzed 206,218 participants from three prefectures reported between March 5 and 30, 2020. The mean (standard deviation) age of participants was 44.2 (13.2). No symptoms were reported by 96.93% of participants, but there was a significantly positive correlation between the reported number of COVID-19 cases and self-reported fevers, suggesting that massive monitoring of fever might help to estimate the scale of the COVID-19 epidemic in real time.
Conclusions
COOPERA is the first real-time system being used to monitor trends in COVID-19 in Japan, and provides useful insights to assist political decisions to tackle the epidemic.
The use of chatbots evolved rapidly in numerous fields in recent years, including Marketing, Supporting Systems, Education, Health Care, Cultural Heritage, and Entertainment. In this paper, we first present a historical overview of the evolution of the international community’s interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots’ usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chatbots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation. Our engagement with the subject so far, reassures us of the prospects of chatbots and encourages us to study them in greater extent and depth.
The terrible cost of injuries and sudden illnesses does have fatal consequences that exposes the limitations of the current prehospital processes in terms of time for emergency staff to arrive on scene and lack of first aid skills among the available incident witnesses. In this paper we aim at developing a smart pervasive chatbot for emergency case assistance based on cloud computing called SPeCECA that assists victims or incident witnesses to help avoiding deterioration of the subject’s condition and maintaining his/her physical integrity until the aid arrives, which could dramatically increase the victim’s survivability chances. Therefore, even a person with no first aid skills, can help the victim to survive by performing first aid support as suggested by the virtual assistant. Furthermore, thanks to its connectivity with the emergency medical service, trusted person(s), and the access to social media, SPeCECA has its own way of alarming the emergency case, in parallel, after having released the degree of the emergency situation’s severity. The proposed method is a mobile pervasive healthcare service in the form of a connected mobile application as a virtual assistant for the benefit of anyone facing an emergency case. The proposed chatbot allows an online human-bot interaction that supports different scenarios for every single emergency case. The design of the system is introduced by its six interdependent components: information pre-processing component (IPPC), natural language processing component (NLPC), context component (CC), information post-processing component (IPoPC), response generator component (RGC), and alert message constructor component (AMCC).
Objective:
Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.
Methods:
We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement.
Results:
The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies.
Conclusions:
The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting.
Protocol registration:
The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.
We describe SemEval2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016: (A) Question–Comment Similarity, (B) Question–Question Similarity, (C) Question– External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for sub-tasks A–D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the base-lines, especially for subtasks A–C.
Numerous empirical studies from various populations and settings link patient treatment adherence to physician-patient communication. Meta-analysis allows estimates of the overall effects both in correlational research and in experimental interventions involving the training of physicians' communication skills.
Calculation and analysis of "r effect sizes" and moderators of the relationship between physician's communication and patient adherence, and the effects of communication training on adherence to treatment regimens for varying medical conditions.
Thorough search of published literature (1949-August 2008) producing separate effects from 106 correlational studies and 21 experimental interventions. Determination of random effects model statistics and the detailed examination of study variability using moderator analyses.
Physician communication is significantly positively correlated with patient adherence; there is a 19% higher risk of non-adherence among patients whose physician communicates poorly than among patients whose physician communicates well. Training physicians in communication skills results in substantial and significant improvements in patient adherence such that with physician communication training, the odds of patient adherence are 1.62 times higher than when a physician receives no training.
Communication in medical care is highly correlated with better patient adherence, and training physicians to communicate better enhances their patients' adherence. Findings can contribute to medical education and to interventions to improve adherence, supporting arguments that communication is important and resources devoted to improving it are worth investing in. Communication is thus an important factor over which physicians have some control in helping their patients to adhere.
Many people face death everyday because of severe medical emergencies, however a basic knowledge of how to deal with such situations could save several human lives. To avoid deterioration of a victim’s condition and maintaining his/her physical integrity, this paper aim at developing a chatbot based smart pervasive healthcare to help victims or incident witnesses properly perform first-aid in a medical emergency situation until the aid arrives. Therefore, even a person with no first aid skills, could help a victim to survive by performing first aid support as suggested by the virtual assistant. This assistance make ordinary people more confident to become a rescuer.
Upon declaring COVID-19 a global pandemic, the World Health Organization (WHO) orchestrated a global risk-communication outreach. The WHO’s objective was to persuade the public to upend and alter their lives so as to contain the disease and minimize its spread and infection. The WHO found a simple and efficient medium to communicate glocally through the social media application WhatsApp, through which individuals could access information without gatekeeping by governments and local agencies.
Eliza is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible. Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text. Responses are generated by reassembly rules associated with selected decomposition rules. The fundamental technical problems with which Eliza is concerned are: (1) the identification of key words, (2) the discovery of minimal context, (3) the choice of appropriate transformations, (4) generation of responses in the absence of key words, and (5) the provision of an editing capability for Eliza scripts. A discussion of some psychological issues relevant to the Eliza approach as well as of future developments concludes the paper. 9 references.
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