Herb-Drug Interactions: A Holistic Decision Support System in Healthcare

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events

Identifying Phytochemicals from Biomedical Literature Utilizing Semantic Knowledge Sources

Chemicals derived from plants (phytochemicals) are major concepts of interest in the study of medicinal plants. To date, efforts to catalogue and organize phytochemical knowledge have resorted to manual approaches. This study explored the potential to leverage publicly accessible semantic knowledge sources for identifying possible phytochemicals. Within the context of this feasibility study, putative phytochemicals were identified for more than 4,000 plants from the Medical Subject Headings Supplementary Concept Records and the Semantic MEDLINE Database. An examination of phytochemicals identified for five selected plant species using the method developed here reveals that there is a disparity in electronically catalogued phytochemical knowledge compared to information from Dr. Duke’s Phytochemical and Ethnobotanical Databases maintained by the United States Department of Agriculture. The results therefore suggest that semantic knowledge sources for biomedicine can be utilized as a source for identifying potential phytochemicals and thus contribute to the overall curation of plant phytochemical knowledge

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Chinese medicine usage in respiratory disorders: a health service research of teaching clinic patients

This research project aimed to: (a) determine the demographic profile and common conditions of patients in a Teaching Clinic; (b) describe the adverse events from Chinese medicine treatment; (c) examine the overall treatment outcomes and treatment interventions of Chinese medicine in patients with respiratory disorders; and (d) assess the level of knowledge and compliance with Chinese medicine in patients with respiratory disorders. The first component of this research is a systematic literature review on studies that assessed the quality of care based on medical records within outpatient practice. The second component is examining the medical records in the Chinese Medicine Teaching Clinic at RMIT University. The last component is a postal survey focusing on a subgroup of the patients (with respiratory disorders) attending the clinic. The RMIT University’s Human Research Ethics Committee reviewed and approved the project. For the medical record study, medical records in the Teaching Clinic dated 1 January 2010 to 31 December 2011 were reviewed and extracted. During the study period, 1,677 patients had made 11,529 visits to the Teaching Clinic. Patients mean age was 42.1±18.1 years and majority were female (65.7%) and Australian-born (66.2%). Patients generally had Chinese medicine for musculoskeletal and pain disorders, emotional disorders, obstetrics and gynaecological disorders, respiratory disorders and gastrointestinal disorders. The most common respiratory disorders were coryza, cough, allergic rhinitis, sinus problems and asthma. Acupuncture was given at almost all visits (96.7%). 153 adverse events were documented; most were gastrointestinal effects such as diarrhoea, nausea, heartburn. For the survey study, a questionnaire was developed and mailed to all (n=299) eligible potential respondents identified from the medical record study described above. In brief, the survey asked about the respondents’ use of health services, adverse events, compliance with Chinese medicine, knowledge of Chinese medicine and their demographic. A total of 63 surveys were returned, a 16.7% response rate. The survey provided additional demographic data compared with the medical records, including: 26.7% of health insured respondents were covered for Chinese medicine; and over half of the respondents earned more than $60,000 annually and had tertiary education. Survey respondents largely reported positive overall treatment outcomes. More respondents disclosed their other treatment to Chinese medicine practitioners (88.5%) than disclosing their Chinese medicine use to general practitioners (62.9%). Almost half of the respondents had moderately complied (44.3%) with Chinese medicine. In terms of knowledge, over a third of the respondents had good knowledge (34.9%) of Chinese medicine. Of 63 respondents, only five reported adverse events; Chinese medicine practitioners verified two events and were informed about three events. In summary, this study was the first to provide insight on quality of care and treatment practice in the Chinese Medicine Teaching Clinic, RMIT University. Additionally, this thesis reported treatment outcomes and communication on health service from the patients’ point of view. Several study limitations were identified, and accordingly, strategies for improving documentation, study design and clinic practices were recommended. Implementing these suggestions can improve health service and quality of care in the Teaching Clinic

Medication Safety in Municipal Health and Care Services

Medicines constitute an essential part of healthcare delivery and help to prevent or treat illness, influence quality of life, and generally increase life expectancy. However, medications can also cause harm if prescribed irrationally, dispensed or used incorrectly, and monitored or followed up insufficiently. In this anthology, we showcase the challenges of medication management and the rational use of medicines in municipal health and care services, and present various strategies and measures related to medication safety. The contributors are researchers representing a wide range of disciplines, with experience from different levels of healthcare services and different areas within the research and education sectors. We hope to raise awareness, engage and enable discussion of initiatives and strategies to improve patient safety related to medications in municipal health and care services, and create a basis for further research to promote safe medication management and rational use of medicines. This anthology will be of interest to anyone involved in or concerned with medication safety, primarily healthcare professionals, academic staff, researchers, policymakers, and managers in healthcare services

Medication Safety in Municipal Health and Care Services

Medicines constitute an essential part of healthcare delivery and help to prevent or treat illness, influence quality of life, and generally increase life expectancy. However, medications can also cause harm if prescribed irrationally, dispensed or used incorrectly, and monitored or followed up insufficiently. In this anthology, we showcase the challenges of medication management and the rational use of medicines in municipal health and care services, and present various strategies and measures related to medication safety. The contributors are researchers representing a wide range of disciplines, with experience from different levels of healthcare services and different areas within the research and education sectors. We hope to raise awareness, engage and enable discussion of initiatives and strategies to improve patient safety related to medications in municipal health and care services, and create a basis for further research to promote safe medication management and rational use of medicines. This anthology will be of interest to anyone involved in or concerned with medication safety, primarily healthcare professionals, academic staff, researchers, policymakers, and managers in healthcare services

Suggested approach for establishing a rehabilitation engineering information service for the state of California

An ever expanding body of rehabilitation engineering technology is developing in this country, but it rarely reaches the people for whom it is intended. The increasing concern of state and federal departments of rehabilitation for this technology lag was the stimulus for a series of problem-solving workshops held in California during 1977. As a result of the workshops, the recommendation emerged that the California Department of Rehabilitation take the lead in the development of a coordinated delivery system that would eventually serve the entire state and be a model for similar systems across the nation

Scalable and Declarative Information Extraction in a Parallel Data Analytics System

Informationsextraktions (IE) auf sehr großen Datenmengen erfordert hochkomplexe, skalierbare und anpassungsfähige Systeme. Obwohl zahlreiche IE-Algorithmen existieren, ist die nahtlose und erweiterbare Kombination dieser Werkzeuge in einem skalierbaren System immer noch eine große Herausforderung. In dieser Arbeit wird ein anfragebasiertes IE-System für eine parallelen Datenanalyseplattform vorgestellt, das für konkrete Anwendungsdomänen konfigurierbar ist und für Textsammlungen im Terabyte-Bereich skaliert. Zunächst werden konfigurierbare Operatoren für grundlegende IE- und Web-Analytics-Aufgaben definiert, mit denen komplexe IE-Aufgaben in Form von deklarativen Anfragen ausgedrückt werden können. Alle Operatoren werden hinsichtlich ihrer Eigenschaften charakterisiert um das Potenzial und die Bedeutung der Optimierung nicht-relationaler, benutzerdefinierter Operatoren (UDFs) für Data Flows hervorzuheben. Anschließend wird der Stand der Technik in der Optimierung nicht-relationaler Data Flows untersucht und herausgearbeitet, dass eine umfassende Optimierung von UDFs immer noch eine Herausforderung ist. Darauf aufbauend wird ein erweiterbarer, logischer Optimierer (SOFA) vorgestellt, der die Semantik von UDFs mit in die Optimierung mit einbezieht. SOFA analysiert eine kompakte Menge von Operator-Eigenschaften und kombiniert eine automatisierte Analyse mit manuellen UDF-Annotationen, um die umfassende Optimierung von Data Flows zu ermöglichen. SOFA ist in der Lage, beliebige Data Flows aus unterschiedlichen Anwendungsbereichen logisch zu optimieren, was zu erheblichen Laufzeitverbesserungen im Vergleich mit anderen Techniken führt. Als Viertes wird die Anwendbarkeit des vorgestellten Systems auf Korpora im Terabyte-Bereich untersucht und systematisch die Skalierbarkeit und Robustheit der eingesetzten Methoden und Werkzeuge beurteilt um schließlich die kritischsten Herausforderungen beim Aufbau eines IE-Systems für sehr große Datenmenge zu charakterisieren.Information extraction (IE) on very large data sets requires highly complex, scalable, and adaptive systems. Although numerous IE algorithms exist, their seamless and extensible combination in a scalable system still is a major challenge. This work presents a query-based IE system for a parallel data analysis platform, which is configurable for specific application domains and scales for terabyte-sized text collections. First, configurable operators are defined for basic IE and Web Analytics tasks, which can be used to express complex IE tasks in the form of declarative queries. All operators are characterized in terms of their properties to highlight the potential and importance of optimizing non-relational, user-defined operators (UDFs) for dataflows. Subsequently, we survey the state of the art in optimizing non-relational dataflows and highlight that a comprehensive optimization of UDFs is still a challenge. Based on this observation, an extensible, logical optimizer (SOFA) is introduced, which incorporates the semantics of UDFs into the optimization process. SOFA analyzes a compact set of operator properties and combines automated analysis with manual UDF annotations to enable a comprehensive optimization of data flows. SOFA is able to logically optimize arbitrary data flows from different application areas, resulting in significant runtime improvements compared to other techniques. Finally, the applicability of the presented system to terabyte-sized corpora is investigated. Hereby, we systematically evaluate scalability and robustness of the employed methods and tools in order to pinpoint the most critical challenges in building an IE system for very large data sets

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

Combining Heterogeneous Databases to Detect Adverse Drug Reactions

Adverse drug reactions (ADRs) cause a global and substantial burden accounting for considerable mortality, morbidity and extra costs. In the United States, over 770,000 ADR related injures or deaths occur each year in hospitals, which may cost up to $5.6 million each year per hospital. Unanticipated ADRs may occur after a drug has been approved due to its use or prolonged use on large, diverse populations. Therefore, the post-marketing surveillance of drugs is essential for generating more complete drug safety profiles and for providing a decision making tool to help governmental drug administration agencies take an action on the marketed drugs. Analysis of spontaneous reports of suspected ADRs has traditionally served as a valuable tool in pharmacovigilance. However, because of well-known limitations of spontaneous reports, observational healthcare data, such as electronic health records (EHRs) and administrative claims data, are starting to be used to complement the spontaneous reporting system. Synthesizing ADR evidence from multiple data sources has been conducted by human experts on an at hoc basis. However, the amount of data from both spontaneous reporting systems (SRSs) and observational healthcare databases is growing exponentially. The revolution in the ability of machines to access, process, and mine databases, making it advantageous to develop an automatic system to obtain integrated evidence by combining them. Towards this goal, this dissertation proposes a framework consisting of three components that generates signal scores based on data an EHR system and of an SRS system, and then integrates two signal scores into a composite one. The first component is a data-driven and regression- based method that aims to alleviate confounding effect and detect ADR based on EHRs. The results demonstrate that this component achieves comparable or slightly higher accuracy than those trained with experts and existing automatic methods. The second component is also a data- driven and regression-based method that aims to reduce the effect of confounding by co- medication and confounding by indication using primary suspected, secondary suspected, concomitant medications and indications on the basis of a SRS. This study demonstrates that it could accomplish comparable or slightly better accuracy than the cutting edge algorithm Gamma Poisson Shrinkage (GPS), which uses primary suspected medications only. The third component is a computational integration method that normalizes signal scores from each data source and integrates them into a composite signal score. The results achieved by the method demonstrate that the combined ADR evidence achieve better accuracy of drug-ADR detection than individual systems based on either an SRS or an EHR. Furthermore, component three is explored as a tool to assist clinical assessors in pharmacovigilance practice. The research presented in this dissertation has produced several novel insights and provided new solutions towards the challenging problem of pharmacovigilance. The method of reducing confounding effect can be generalizable to other EHR systems and the method for integrating ADR evidence can be generalizable to include other data sources. In conclusion, this dissertation develops a method to reduce confounding effect in both EHRs and SRSs, and a combined system to synthesize evidence, which could potentially unveil drug safety profiles and novel adverse events in a timely fashion