Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain

Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchmarks: (a) are limited to English which makes it challenging to replicate many of the successes in English for other languages, or (b) focus on knowledge probing of LLMs and neglect to evaluate how LLMs apply these knowledge to perform on a wide range of bio-medical tasks, or (c) have become a publicly available corpus and are leaked to LLMs during pre-training. To facilitate the research in medical LLMs, we re-build the Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark into a large scale prompt-tuning benchmark, PromptCBLUE. Our benchmark is a suitable test-bed and an online platform for evaluating Chinese LLMs' multi-task capabilities on a wide range bio-medical tasks including medical entity recognition, medical text classification, medical natural language inference, medical dialogue understanding and medical content/dialogue generation. To establish evaluation on these tasks, we have experimented and report the results with the current 9 Chinese LLMs fine-tuned with differtent fine-tuning techniques

A systematic review and critical interpretive synthesis of public perceptions of palliative care

IntroductionProfessional and public misperceptions about palliative care are common and may contribute to poor palliative care access in different settings globally. In this thesis, I aimed to better understand these public perceptions and the influences on them.MethodsA systematic literature review and critical interpretive synthesis was conducted. Non-medical subject headings for palliative care and perceptions were used to search for relevant quantitative, qualitative, and mixed-methods studies in MEDLINE, EMBASE, PsycINFO, CINAHL and, Web of Science Social Science Citations Index Expanded and Conference Proceedings Citation Index from 1 Jan 2002 to 31 May 2020. Search results were screened against a priori inclusion criteria, data extracted, and quality appraised by two independent researchers. Data were analysed by narrative quantitative synthesis, qualitative thematic synthesis, and then combined in a critical interpretive synthesis.Results48/33985 studies from Europe, North America, Asia and Australasia were included (32 quantitative, 9 qualitative, 7 mixed methods), representing 32585 members of the public (aged 18-101 years; 54% women). Knowledge of palliative care was poor (especially for men, younger people, and ethnic minorities) with considerable variation in public perceptions. A perception consistent around the world is “palliative care is death”. To some, this is euthanasia and giving up, to others it is comfort care allowing a natural death. Personal experience of palliative care improves understanding. In this context it is generally seen as good care offered by compassionate people – albeit still to be avoided until unavoidable.ConclusionPublic understanding of palliative care is poor. Perceptions of palliative care are influenced by a triad of culture, socioeconomic position, and health literacy. To improve integration of a country’s palliative care services and improve access to palliative care, an intervention to increase exposure to and education in palliative care that considers these factors is needed

Huatuo-26M, a Large-scale Chinese Medical QA Dataset

In this paper, we release a largest ever medical Question Answering (QA) dataset with 26 million QA pairs. We benchmark many existing approaches in our dataset in terms of both retrieval and generation. Experimental results show that the existing models perform far lower than expected and the released dataset is still challenging in the pre-trained language model era. Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner. We believe that this dataset will not only contribute to medical research but also facilitate both the patients and clinical doctors. See \url{https://github.com/FreedomIntelligence/Huatuo-26M}

Patient Dropout Prediction in Virtual Health: A Multimodal Dynamic Knowledge Graph and Text Mining Approach

Virtual health has been acclaimed as a transformative force in healthcare delivery. Yet, its dropout issue is critical that leads to poor health outcomes, increased health, societal, and economic costs. Timely prediction of patient dropout enables stakeholders to take proactive steps to address patients' concerns, potentially improving retention rates. In virtual health, the information asymmetries inherent in its delivery format, between different stakeholders, and across different healthcare delivery systems hinder the performance of existing predictive methods. To resolve those information asymmetries, we propose a Multimodal Dynamic Knowledge-driven Dropout Prediction (MDKDP) framework that learns implicit and explicit knowledge from doctor-patient dialogues and the dynamic and complex networks of various stakeholders in both online and offline healthcare delivery systems. We evaluate MDKDP by partnering with one of the largest virtual health platforms in China. MDKDP improves the F1-score by 3.26 percentage points relative to the best benchmark. Comprehensive robustness analyses show that integrating stakeholder attributes, knowledge dynamics, and compact bilinear pooling significantly improves the performance. Our work provides significant implications for healthcare IT by revealing the value of mining relations and knowledge across different service modalities. Practically, MDKDP offers a novel design artifact for virtual health platforms in patient dropout management

Swift trust and behavioral change: facilitating factors of crowdsourcing in chronic disease prevention

Behind Internet usage habits there is a common vocabulary: trust. In order to promote preventive medicine, Internet medical care has been trying to cultivate user habits and behavior change, but whoever increases trust can go further. The Internet has accelerated the pace of work and life and generalized the temporary involvement of individuals and teams. In many organizations, there is usually no time to develop trust among team members or between the team and customers in traditional ways such as mutual familiarity, experience sharing, mutual disclosure, and verification of commitments. These new situations have led to the study of a new form of trust: "swift trust". According to Hurd et al. (2017), "swift trust" focuses on expecting that a person has the necessary attributes to be relied upon. In the "swift trust" theory, a group or individual assumes the existence of trust initially, and later verifies and adjusts trust beliefs accordingly. Faced with the problem of the rapid spread of chronic diseases and the high proportion of medical expenses needed to combat them and that have posed challenges to the national finances in China, this thesis focuses on studying the factors that may facilitate the establishment of "swift trust" in the Internet based chronic disease crowdsourcing model. Grounded on the idea that trust affects behavior and speed affects efficiency, we have reviewed extant literature and, with the help of ROST Content Mining (ROST-CM) text mining software, we dug millions of Internet data and conducted in-depth research on the "swift trust" problem. Results, later verified through two ongoing healthcare projects showed that "profession" followed by "platform", "dissemination" and "propensity" are the most critical factors that affect the establishment of swift trust. These results may be of interest to professionals, organizations and government decision makers in need of establishing and winning trust, and particularly "swift trust", as an essential ingredient in the sharing economy.Existe uma palavra comum por detrás de todos os hábitos de utilização da Internet: confiança. Com o objetivo de promover a medicina preventiva, alguns cuidados médicos prestados através da Internet têm vindo a procurar motivar os utilizadores para uma mudança de hábitos e comportamentos, mas apenas quem conseguir ganhar a confiança poderá ir mais longe. A Internet acelerou o ritmo da vida e do trabalho e generalizou a participação temporária de indivíduos e grupos. Em muitas organizações, não há tempo suficiente para se criar confiança entre os membros de um grupo ou entre grupos e indivíduos através de formas tradicionais como a convivência e o conhecimento mútuos, a partilha de experiências ou a verificação do cumprimento de compromissos. Esta situação levou ao estudo de uma nova forma de confiança: "a confiança imediata". Hurd et al. (2017) afirmam que este conceito se refere à expetativa de que uma determinada pessoa reúna os atributos necessários para ser confiável. Segundo a teoria que estuda a "confiança imediata", um grupo ou indivíduo assume desde logo a presença de confiança e reserva para mais tarde a confirmação da sua existência. Considerando os desafios colocados pelo rápido desenvolvimento de doenças crónicas num país tão populoso como a China e a necessidade de as combater, esta tese estuda os fatores que poderão facilitar a construção de "confiança imediata" no modelo de colaboração aberta através da Internet com vista à prevenção destas doenças. Partindo do princípio de que a confiança afeta os comportamentos e de que a rapidez afeta a eficiência procedeu-se à revisão de literatura sobre o tema e, com a ajuda do "software" de mineração de texto ROST-CM (ROST Content Mining) foram recolhidos e tratados milhões de dados extraídos da Internet. Os resultados foram depois confrontados com a prática de dois projetos na área da saúde e revelaram que a "profissão" seguida da "plataforma", "disseminação" e "propensão" são os fatores que mais contribuem para a formação de "confiança imediata". Os resultados obtidos poderão ser de interesse para profissionais, organizações e decisores governamentais que necessitam de construir e manter confiança e, em particular "confiança imediata", enquanto ingrediente essencial na economia de partilha

CREATE: Concept Representation and Extraction from Heterogeneous Evidence

Traditional information retrieval methodology is guided by document retrieval paradigm, where relevant documents are returned in response to user queries. This paradigm faces serious drawback if the desired result is not explicitly present in a single document. The problem becomes more obvious when a user tries to obtain complete information about a real world entity, such as person, company, location etc. In such cases, various facts about the target entity or concept need to be gathered from multiple document sources. In this work, we present a method to extract information about a target entity based on the concept retrieval paradigm that focuses on extracting and blending information related to a concept from multiple sources if necessary. The paradigm is built around a generic notion of concept which is defined as any item that can be thought of as a topic of interest. Concepts may correspond to any real world entity such as restaurant, person, city, organization, etc, or any abstract item such as news topic, event, theory, etc. Web is a heterogeneous collection of data in different forms such as facts, news, opinions etc. We propose different models for different forms of data, all of which work towards the same goal of concept centric retrieval. We motivate our work based on studies about current trends and demands for information seeking. The framework helps in understanding the intent of content, i.e. opinion versus fact. Our work has been conducted on free text data in English. Nevertheless, our framework can be easily transferred to other languages

Social analytics for health integration, intelligence, and monitoring

Nowadays, patient-generated social health data are abundant and Healthcare is changing from the authoritative provider-centric model to collaborative and patient-oriented care. The aim of this dissertation is to provide a Social Health Analytics framework to utilize social data to solve the interdisciplinary research challenges of Big Data Science and Health Informatics. Specific research issues and objectives are described below. The first objective is semantic integration of heterogeneous health data sources, which can vary from structured to unstructured and include patient-generated social data as well as authoritative data. An information seeker has to spend time selecting information from many websites and integrating it into a coherent mental model. An integrated health data model is designed to allow accommodating data features from different sources. The model utilizes semantic linked data for lightweight integration and allows a set of analytics and inferences over data sources. A prototype analytical and reasoning tool called “Social InfoButtons” that can be linked from existing EHR systems is developed to allow doctors to understand and take into consideration the behaviors, patterns or trends of patients’ healthcare practices during a patient’s care. The tool can also shed insights for public health officials to make better-informed policy decisions. The second objective is near-real time monitoring of disease outbreaks using social media. The research for epidemics detection based on search query terms entered by millions of users is limited by the fact that query terms are not easily accessible by non-affiliated researchers. Publically available Twitter data is exploited to develop the Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS provides four visual analytics tools for monitoring epidemics, i.e., Instance Map, Distribution Map, Filter Map, and Sentiment Trend to investigate public health threats in space and time. The third objective is to capture, analyze and quantify public health concerns through sentiment classifications on Twitter data. For traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues, due to their expenses and significant time delays. A two-step sentiment classification model is built to measure the concern. In the first step, Personal tweets are distinguished from Non-Personal tweets. In the second step, Personal Negative tweets are further separated from Personal Non-Negative tweets. In the proposed classification, training data is labeled by an emotion-oriented, clue-based method, and three Machine Learning models are trained and tested. Measure of Concern (MOC) is computed based on the number of Personal Negative sentiment tweets. A timeline trend of the MOC is also generated to monitor public concern levels, which is important for health emergency resource allocations and policy making. The fourth objective is predicting medical condition incidence and progression trajectories by using patients’ self-reported data on PatientsLikeMe. Some medical conditions are correlated with each other to a measureable degree (“comorbidities”). A prediction model is provided to predict the comorbidities and rank future conditions by their likelihood and to predict the possible progression trajectories given an observed medical condition. The novel models for trajectory prediction of medical conditions are validated to cover the comorbidities reported in the medical literature

Prevalence of occult hepatitis B infection in a highly endemic area for chronic hepatitis B: A study of a large blood donor population

Background and aims: The aim of the present study was to determine the population prevalence of occult hepatitis B (OHB) infection and its clinical profile in a highly endemic area of chronic hepatitis B virus disease. Methods: OHB was first identified by individual sample testing for hepatitis B surface antigen (HBsAg) followed by nucleic acid testing (NAT) and vice versa for 3044 (cohort 1, stored sera from donation within 1 year) and 9990 (cohort 2, prospective study) blood donors, respectively. OHB was confirmed meticulously by ≥2 out of 3 tests with detectable hepatitis B virus (HBV) DNA using a sensitive standardised assay. Detailed serology and viral load in the serum and liver were studied. Results: The prevalence of OHB was 0.13% (4/3044) and 0.11% (11/9967) for cohort 1 and 2, respectively. In cohort 2, 10 out of 11 OHB samples were positive for anti-HBc (hepatitis B core antigen) antibody (all were immunoglobulin G). Seven had detectable anti-HBs. The serum HBV DNA levels were extremely low (highest 14.1 IU/ml). Of the six donors who underwent liver biopsies, all had normal liver biochemistry, extremely low liver HBV DNA (highest 6.21 copies/cell) and nearly normal liver histology. For those with viral sequence generation, none had the common HBsAg mutant G145R. Conclusions: The prevalence of OHB in a highly endemic area of chronic HBV was very low, thus implying a low impact on transfusion services. To implement universal screening, the high cost of NAT should be taken into account. OHB blood donors had very low HBV replication, and normal liver biochemistry and histology, conferring a favourable prognosis.published_or_final_versio

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe