Conco-ERNIE: Complex User Intent Detect Model for Smart Healthcare Cognitive Bot

The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient's utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE, using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot. To provide knowledge support for MedicalBot, we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships). © 2023 Association for Computing Machinery.

A Knowledge-driven Domain Adaptive Approach to Early Misinformation Detection in an Emergent Health Domain on Social Media

This paper focuses on an important problem of early misinformation detection in an emergent health domain on social media. Current misinformation detection solutions often suffer from the lack of resources (e.g., labeled datasets, sufficient medical knowledge) in the emerging health domain to accurately identify online misinformation at an early stage. To address such a limitation, we develop a knowledge-driven domain adaptive approach that explores a good set of annotated data and reliable knowledge facts in a source domain (e.g., COVID-19) to learn the domain-invariant features that can be adapted to detect misinformation in the emergent target domain with little ground truth labels (e.g., Monkeypox). Two critical challenges exist in developing our solution: i) how to leverage the noisy knowledge facts in the source domain to obtain the medical knowledge related to the target domain? ii) How to adapt the domain discrepancy between the source and target domains to accurately assess the truthfulness of the social media posts in the target domain? To address the above challenges, we develop KAdapt, a knowledge-driven domain adaptive early misinformation detection framework that explicitly extracts rel-evant knowledge facts from the source domain and jointly learns the domain-invariant representation of the social media posts and their relevant knowledge facts to accurately identify misleading posts in the target domain. Evaluation results on five real-world datasets demonstrate that KAdapt significantly outperforms state-of-the-art baselines in terms of accurately detecting misleading Monkeypox posts on social media. © 2022 IEEE.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

The medical conversational system can relieve doctors' burden and improve healthcare effi-ciency, especially during the COVID-19 pan-demic. However, the existing medical dialogue systems have die problems of weak scalability, insufficient knowledge, and poor controlla-bility. Thus, we propose a medical conversa-tional question-answering (CQA) system based on the knowledge graph, namely MedConQA, which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures, including medi-cal triage, consultation, image-text drug recom-mendation, and record. Each module has been open-sourced as a tool, which can be used alone or in combination, with robust scalability. Besides, to conduct knowledge-grounded dia-logues with users, we first construct a Chinese Medical Knowledge Graph (CMKG) and col-lect a large-scale Chinese Medical CQA (CM-CQA) dataset, and we design a series of meth-ods for reasoning more intellectually. Finally, we use several state-of-the-art (SOTA) tech-niques to keep the final generated response more controllable, which is further assured by hospital and professional evaluations. We have open-sourced related code, datasets, web pages, and tools, hoping to advance future research. © 2022 Association for Computational Linguistics.

An agent-based model of COVID-19 pandemic and its variants using fuzzy subsets and real data applied in an island environment

In this paper, we present a model of the spread of the COVID-19 pandemic simulated by a multi-agent system (MAS) based on demographic data and medical knowledge. Demographic data are linked to the distribution of the population according to age and to an index of socioeconomic fragility with regard to the elderly. Medical knowledge are related to two risk factors: age and obesity. The contributions of this approach are as follows. Firstly, the two aggravating risk factors are introduced into the MAS using fuzzy sets. Secondly, the worsening of disease caused by these risk factors is modeled by fuzzy aggregation operators. The appearance of virus variants is also introduced into the simulation through a simplified modeling of their contagiousness. Using real data from inhabitants of an island in the Antilles (Guadeloupe, FWI), we model the rate of the population at risk which could be critical cases, if neither social distancing nor barrier gestures are respected by the entire population. The results show that hospital capacities are exceeded. The results show that hospital capacities are exceeded. The socioeconomic fragility index is used to assess mortality and also shows that the number of deaths can be significant. © The Author(s), 2023. Published by Cambridge University Press.

COVID-19 vaccination national radio advertising credibility assessment by rural consumers: The influence of indigenous medical knowledge systems and traditional beliefs

The rural consumer has been largely ignored in advertising research, yet they constitute more than 50% of the population in Africa. The study aimed to explore rural consumers' assessment of COVID-19 vaccination radio advertising credibility with a peculiar focus on the influence of indigenous medical knowledge systems and traditional beliefs in Zimbabwe. The interpretive paradigm was adopted in the embedded single case study. The rural population which was purposively sampled consisted of 6Focus Group Discussions (FGD) and 12 interviews of rural consumers. Thematic approach was used to analyze the data. The results show that credibility of radio adverts on COVID-19 is dependent on the source of information, political perceptions, religious and personal experiences on COVID-19. Rural consumers also relied on traditional medicines to treat COVID-19. The rural consumers have strong belief in spirituality and witchcraft which had a bearing on the acceptance/non acceptance of COVID-19 vaccination radio adverts. The study provides theoretical insights and also practical contribution to COVID-19 vaccination managers, advertisers and health marketing practitioners to enhance the acceptance and trust of national radio ads, so as to increase rural consumers' vaccination.Copyright © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

The long tail of COVID and the tale of long COVID: Diagnostic construction and the management of ignorance.

We bring together insights from the sociology of diagnosis and the sociology of ignorance to examine the early diagnostic unfolding of 'Long COVID' (LC). Originally described by patient activists, researchers set out to ponder its unwieldy clinical boundaries. Using a scoping review method in tandem with qualitative content analytic techniques, we analyse medicine's initial struggles to construct a LC diagnosis. Paying attention to the dynamics of ignorance, we highlight three consequential conceptual manoeuvres in the early classifications of LC: causal agnosticism concerning the relationship between COVID-19 and LC, evasion of lumping LC with similar conditions; and the predictable splitting off of medically explainable cases from the LC designation. These manoeuvres are not maleficent, inept or unreasonable. They are practical but impactful responses to the classificatory dilemmas present in the construction of diagnoses amidst ignorance. Although there are unique aspects to LC, we suggest that its early fate is nevertheless emblematic of medicine's diagnostic standardisation processes more generally. To varying degrees, diagnoses are ignorance management strategies; they create a pathway through the uncertainty at the core of disease realities. However, while diagnoses circumscribe some types of ignorance, they produce others through the creation of blind spots and paths not taken.

YouTube as a source of information about pulmonary rehabilitation during the COVID-19 pandemic

BACKGROUND AND AIM: To our best knowledge, there is no literature on the effectiveness of YouTube on pulmonary rehabilitation (PR) practice. In our study, we aimed to evaluate the characteristics and medical aspects of videos on YouTube about PR. METHODS: In the internet media website YouTube.com search engine, the Word PR was searched on August 3, 2021, without any filter. The first 100 videos listed were classified according to the number of likes, dislikes, origin of country, and content of PR. The materials were evaluated in terms of intelligibility using the suitability assessment of materials (SAM). User participation measurements were obtained for each video. RESULTS: The later years were shown to have a statistically significant relationship with respiratory techniques, PR contraindications, and videos with PR in COVID in our study (p<0.05). However, no significant relationship was identified between the later years and smoking in PR and videos with PR in the intensive care unit (p>0.05). The total SAM score was found to significantly correlate with the number of views, likes, dislikes, comments, and video durations (p<0.05). CONCLUSIONS: It was observed that COVID videos with PR content were uploaded with regard to the specific video issues and treatment needs during and after the COVID infection in the later years, especially after the pandemic. Moreover, videos with high comprehensibility are more interesting for users, reflected in views, likes, dislikes, comments, and video duration. Higher quality videos created by health professionals will be more useful for patient education in the future.

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.

Public Health and the Politics of Knowledge: The Role of Traditional Medicine in the Management of COVID-19 in Two South Indian States

Public health discourse about COVID-19 pandemic has mostly been framed around biomedical interventions, although there is evidence of the effective use of traditional medicine (TM) to manage the pandemic by some Asian countries such as China, Thailand, Vietnam and India. This article aims to place on record the policy of medical pluralism in the two South Indian states of Tamil Nadu and Kerala in their respective deployment of Siddha and Ayurveda in the management of COVID-19. Based on interviews with physicians of TM and health administrators, press reports, social media posts and published research, this article reconstructs the crucial yet undocumented process of incorporating TM in the biomedicine-based health bureaucracy in Tamil Nadu and Kerala to deal with infectious fevers such as dengue and chikungunya in the past and COVID-19 in the present. It is our argument that those methods of TM which are safe and in long recent use could provide low-cost and accessible means of prevention and early treatment of infectious fevers. They have to be identified and subjected to further investigation as innovations in social medicine brought forth by the state and its officials and are different from the highly expensive projects of the corporate pharmaceutical sector. © 2022 SAGE Publications.

Appealing AI in Appalling Covid-19 Crisis and the Impending

The outbreak of the deadly Covid-19 virus has snatched smiles from everyone’s face and now the entire world has been affected directly or indirectly by the effects of the virus, this virus keeps on mutating due to which there is no proper medicine or a final vaccine that assures it will curb the spread of the virus, major countries all over the world has lost more people than in a war and is still losing its people even after getting fully vaccinated. The horror is so much imbibed in each human it seems unrealistic to even think that the world will be normal ever again. This outbreak of the unknown virus is certainly a black-swan event that has annihilated people economically, emotionally, and socially and has made each individual realize the importance of one’s health and how to be a responsible person by taking care of whatever finances one has, as in unprecedented times savings are the only resort left with a person. It is a testing time and everyone is at war, we all are soldiers in this pandemic and our health care workers, administration, and government are trying their best to stop the spread of the disease as it has killed more than four lakh people in India only and in the world tally is more than forty lakhs with numbers increasing. In this appalling situation when everything has been shifted to online mode solutions must be looked at in more technologically driven methods, in today’s world due to rapid advancement in the IT and computer science sector there are ways to track the next rising hotspot of the virus and how it can be contained by taking swift actions if predicted within a particular time frame. Data collection, data analysis, and studying trends can help in assessing the upcoming threats, and in this manner, new job opportunities can also be created as it will involve people being prepared with limited medical knowledge to cure the people affected with the virus. In these times government and administration must adopt technologically backed solutions which will help the system to make accurate decisions based on real-time data-driven modeling capable of identifying the relevant information. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis and Prognosis of Covid-19 using Machine Learning Algorithms and Visualization using Tableau

This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.

MedSeq2Seq: A Medical Knowledge Enriched Sequence to Sequence Learning Model for COVID-19 Diagnosis

The COVID-19 pandemic has had a severe impact on humans' lives and and healthcare systems worldwide. How to early, fastly and accurately diagnose infected patients via multimodal learning is now a research focus. The central challenges in this task mainly lie on multi-modal data representation and multi-modal feature fusion. To solve such challenges, we propose a medical knowledge enriched multi-modal sequence to sequence learning model, termed MedSeq2Seq. The key components include two attention mechanisms, viz. intra-modal (Ia) and inter-model (Ie) attentions, and a medical knowledge augmentation mechanism. The former two mechanisms are to learn multi-modal refined representation, while the latter aims to incorporate external medical knowledge into the proposed model. The experimental results show the effectiveness of the proposed MedSeq2Seq framework over state-of-the-art baselines with a significant improvement of 1%-2%. © 2021 IEEE.

Multi-Agent modeling of the spread of diseases using the example of coronavirus disease COVID-19

Throughout the history of humanity, large-scale epidemics and pandemics have repeatedly erupted. Athenian ulcer, several plague and cholera pandemics, Spanish flu, Avian influenza, Swine influenza, HIV/AIDS-millions of people have died due to lack of medicines and medical knowledge. In the 21st century, it would seem that world medicine is ready and capable of preventing many diseases, but by the beginning of 2020, a new pandemic of the coronavirus disease COVID-19 caused by the SARS-CoV-2 virus broke out. The paper provided a brief systematic overview of modeling methods in epidemiology. A modified SEIRD simulation model of epidemic spread is presented. The proposed model was implemented in the AnyLogic system. © 2021 IEEE.

Interns Without Subinternships.

Surgical interns who have completed medical school in the era of Covid-19 will not have the same experience gained through the traditional multi-month fourth-year surgical subinternships. During subinternships, medical students learn relevant anatomic and radiographic features of surgical pathologies, hone technical skills, and gain exposure to surgical consults and procedures. This lack of intensive exposure will have this cohort starting at a lower comfort and knowledge level compared to years prior. Residency programs, especially subspeciality programs, should review and utilize national resources to facilitate the transition to intern year, such as the American College of Surgeons Entering Resident Readiness Assessment and American College of Surgeons/ Association of Program Directors in Surgery/Association for Surgical Education Resident Prep Curriculum. We recommend the use of a specialty-tailored intern boot-camp and longitudinal curriculum that focuses on learning procedural skills and surgical conditions, anatomy, pathology, clinical examination, radiographic findings, surgical approach, and postoperative complications. These steps will help address knowledge gaps and promote intern readiness in this cohort of individuals.

An Orthopaedic Virtual Clinical Clerkship for Visiting Medical Students: Early Successes and Future Implications.

OBJECTIVE: The visiting orthopaedic clerkship is viewed by both students and program directors as an important part of the orthopaedic surgery residency application process, despite being criticized as costly and inefficient. Restrictions due to the COVID-19 pandemic prevented students from participating in in-person clerkships at institutions other than at their home programs, necessitating a virtual replacement for the in-person orthopaedics clerkship experience. It remains unclear how the virtual clerkships will affect the application process this year, and moving forward. We describe and review our institution's initial experience with a virtual orthopaedic clerkship. We hypothesize that students would view the virtual clerkship as valuable, and that students would see a role for such clerkships going forward. DESIGN: A virtual orthopaedic surgery clerkship was created and students were invited to enroll. Thirty-one 4th-year medical students participated. Each clerkship included 8 two-hour sessions. Each session was moderated by a faculty member, and participants included only medical students. Students presented virtual cases, which provided the basis for the discussion and education. At the conclusion of each clerkship, students were given an anonymous survey assessing various aspects of the clerkship. RESULTS: Twenty-seven students responded to the survey. Overall, 15 students rated the experience as outstanding, 11 excellent, and 1 good. Twenty-two students saw a role for virtual clerkships moving forward, and five students did not see a role moving forward. Student reported strengths of the clerkship included direct faculty interaction, structured curriculum, and student-centered discussions. Lack of hands-on experience was cited as the biggest weakness. CONCLUSIONS: Students valued the opportunity for a virtual clerkship, and most could envision a role for such virtual clerkships moving forward. We suggest that virtual clerkships may be a cost-effective and useful tool in helping both students and programs navigate the residency selection process.

Where Do We Go From Here? Assessing Medical Students' Surgery Clerkship Preparedness During COVID-19.

INTRODUCTION: The impact of COVID-19 on surgical education has been profound, and clinical learning experiences transitioned to virtual formats. This study investigated the impact of virtual experiences created to facilitate learning during the pandemic for medical students. METHODS: We performed a cohort study to determine the perceived clinical preparedness for medical students enrolled in the preclinical surgery pilot course, surgical Extended Mastery Learning Rotation (EMLR), and longitudinal surgical clerkship (LC). The preclinical surgery pilot course took place before COVID-19 disruptions, and the EMLR and LC experiences took place virtually. Specialty choice was examined in the EMLR and LC cohorts. Performance on the NBME surgical assessments was analyzed among students enrolled in the traditional clerkship and pandemic-disrupted courses and compared to national data using a two-sample t-test. RESULTS: Compared to preclinical students, EMLR and LC students demonstrated improvements in their perceived surgical clerkship readiness. After the 3-week EMLR course, in the setting of completing only one-third of the clerkship year, students had an average NBME Surgical Self-Assessment Exam score of 72 (SD 12), comparable to the national average of 71 (SD 9) p = 0.33. The average shelf exam score for students (N = 24) enrolled in the traditional clerkship (block 1), prior to COVID-19, disruptions was 66 (SD 9) compared to an average score of 69 (SD 9) for the longitudinal clerkship students (N = 20) that took the shelf exam later in the year (p = 0.36). COVID-19 disruptions did not affect specialty choice. All LC students have decided on a specialty; 50% nonsurgical and 50% surgical. From the EMLR cohort, 36% and 38% plan to pursue surgical and nonsurgical specialties, respectively, with 26% still undecided. CONCLUSIONS: Courses were well-liked and will be implemented in future clerkships. Surgical educators demonstrated flexibility and creativity in the development of the EMLR. Despite COVID-19 disruptions, medical students made progress in their clinical skills and foundational science knowledge. COVID-19 disruptions did not appear to impact specialty choice.

"I Have a Cough": An Interactive Virtual Respiratory Case-Based Module.

Introduction: The COVID-19 pandemic has radically disrupted traditional models of medical education, forcing rapid evolution in the delivery of clinical training. As a result, clinical educators must quickly transition away from in-person sessions and develop effective virtual learning opportunities instead. This virtual resource was designed to replace a clinical simulation session for the physical examination course for medical students in the preclinical years. Methods: We designed an online interactive module in three sections for preclinical (first- or second-year) medical students who had not yet learned the respiratory physical exam. The first section incorporated demonstration and practice of the components of the respiratory physical exam that could be effectively taught via videoconferencing software. Following this, students conducted a telemedicine encounter with a standardized patient and received patient-centered feedback evaluating their communication skills. The final segment involved a case discussion and clinical reasoning component. Results: These sessions were implemented for 122 first-year medical students. The module was well received by the students. A majority felt that it helped improve their telemedicine communication skills (93%), interpretation of physical exam findings (84%), development of differential diagnosis (95%), and correlation of clinical and basic science content (93%). Discussion: Our pilot educational session demonstrates that this virtual instruction method is an effective tool for teaching basic clinical skills during medical school. Virtual learning resources allow remote instruction to take place and can be a supplement when face-to-face clinical teaching is not possible.

Multi-institutional Collaborative Surgery Education Didactics: Virtual Adaptations During a Global Pandemic.

OBJECTIVE: The COVID-19 pandemic has disrupted graduate medical education, impacting Accreditation Council for Graduate Medical Education (ACGME)-mandated didactics. We aimed to study the utility of 2 methods of virtual learning: the daily National Surgery Resident Lecture Series (NSRLS), and weekly "SCORE School" educational webinars designed around the Surgical Council on Resident Education (SCORE) curriculum. DESIGN AND SETTING: NSRLS: The National Surgery Resident Lecture Series was a daily virtual educational session initially led by faculty at an individual surgical residency program. Thirty-eight lectures were assessed for number of live viewings (March 23, 2020-May 15, 2020). SCORE SCHOOL: Attendance at eleven weekly SCORE educational webinars was characterized into live and asynchronous viewings (May 13, 2020-August 5, 2020). Each 1-hour live webinar was produced by SCORE on a Wednesday evening and featured nationally recognized surgeon educators using an online platform that allowed for audience interaction. RESULTS: NSRLS: There were a mean of 71 live viewers per NSRLS session (range 19-118). Participation began to decline in the final 2 weeks as elective case volumes increased, but sessions remained well-attended. SCORE SCHOOL: There were a range of 164-3889 live viewers per SCORE School session. Sessions have most commonly been viewed asynchronously (89.8% of viewings). Live viewership decreased as the academic year ended and then rebounded with the start of the new academic year (range 4.9%-27%). Overall, the eight webinars were viewed 11,135 times. Each webinar continues to be viewed a mean of 43 times a day (range 0-102). Overall, the eleven webinars have been viewed a total of 22,722 times. CONCLUSIONS: Virtual didactics aimed at surgical residents are feasible, well-attended (both live and recorded), and have high levels of viewer engagement. We have observed that careful coordination of timing and topics is ideal. The ability for asynchronous viewing is particularly important for attendance. As the COVID-19 pandemic continues to disrupt healthcare systems, training programs must continue to adapt to education via virtual platforms.

A 3-Dimensional-Printed Hand Model for Home-Based Acquisition of Fracture Fixation Skills Without Fluoroscopy.

OBJECTIVE: To design a low cost ($40), realistic and fluoroscopy-free percutaneous Kirschner wire hand fracture fixation training instrument kit for home-based skill acquisition during the COVID-19 pandemic. DESIGN: A 3D-printed hand was designed from a computed tomography scan of a healthy hand. These data were used to create replaceable hand and wrist bones and reusable silicone molds for a replica of the soft tissue envelope. The model is currently being integrated into the simulation curriculum at 2 integrated plastic surgery residency programs for training in percutaneous wire fixation of hand fractures. SETTING: Brown University, Warren Alpert Medical School of Brown University. Department of Surgery, Division of Plastic and Reconstructive Surgery. Large academic quaternary referral institution. Yale University, Yale School of Medicine. Department of Surgery, Division of Plastic and Reconstructive Surgery. Large academic quaternary referral institution. PARTICIPANTS: PGY 1-4 plastic surgery residents preparing to meet ACGME Accreditation for Graduate Medical Education hand surgery specific milestones. RESULTS: A realistic and durable 3D model with interchangeable bones allows trainees to practice the key motor skills necessary for successful fixation of hand and wrist fractures with K-wires in a home-based setting. CONCLUSIONS: A low cost, realistic and durable 3D hand model with interchangeable bones allows easy integration into any home-based hand surgery curriculum. With 3D printers and programming becoming more prevalent and affordable, such models offer a means of low-cost and safe instruction of residents in fracture fixation with no harm to patients.