Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

How can Increased Electronic Health Record Interoperability be Achieved through the use of APIs?

This paper investigates how application programming interfaces can be used to improve the interoperability (or shareability) of health records. Electronic health records store health information that originates from various sources like prescription order systems, medical devices and even other EHRs. An API helps these disparate systems exchange information with one another. APIs can improve data sharing by using secure standards like FHIR. Having all off this integrated and usable data can aid in the clinical decision process. This would also allow patients to have a more comprehensive look at their health data in patient portals.Master of Science in Information Scienc

Incidence of Potential Drug–Drug Interactions in Cardiac Patients in a Tertiary Care Hospital

INTRODUCTION: Drug Interaction is desirable or undesirable pharmacological effect of drugs interacting with other drugs, with endogenous physiologic chemical agents, with components of the diet, and with chemicals used in diagnostic tests or the results of such tests.An interaction can either increase or decrease the effectiveness and/or the side effects of a drug, or it can create a new side effect not previously seen before. Drug interactions may make the drug less effective, cause unexpected side effects or increase the action of a particular drug. Some drug interactions can even be harmful. Therefore, reading the label every time before using a nonprescription or prescription drug and taking the time to learn about drug interactions may be useful. The probability of interactions increases with the number of drugs taken. The high rate of prescribed drugs in elderly patients (65-year-old patients take an average of 5 drugs) increases the likelihood of drug interactions and thus the risk that drugs itself can be the cause of hospitalization. AIM OF THE STUDY: To Assess the potential drug-drug interactions among hospitalized patients in cardiac departments in tertiary care hospitals. OBJECTIVES: 1. To identify prevalence of potential drug-drug interactions, in cardiology department. 2. To identify the types and severity of pDDIs. 3. To make list of most common pDDIs in the hospitalized cardiac patients and to determine the risk factors associated with pDDIs in cardiology department. METHODOLOGY: Study design: • It is a prospective observational study. Study site: • The research work was conducted at tertiary care hospital, Erode, Erode district, Tamil Nadu. Study period: • 6 Months. Inclusion criteria: • Hospitalized cardiac patients. • Age groups above 18 years. • Prescriptions with two or more drugs prescribed during the hospitalization were only selected for the study. Exclusion criteria: • Out patients. • Ayurveda, siddha, and other prescriptions involving alternative system of medicine. • Age group less than 18 years. • Prescription with less than 2 drugs prescribed . Source of data: The data were collected from case sheets of hospitalized patients and direct patient interview from cardiac department. CONCLUSION: Our study concluded that the overall incidence of pDDIs was very high in the Department of Cardiology. The pDDIs were found to be more in males compared to females, it was found that incidence of pDDIs was associated with old age, polypharmacy and increased lengths of hospital stay. The majority of interactions were pharmacodynamic in nature, having major severity. The most of the common pDDIs were betwwen aspirin and clopidogrelnd followed by sspirin and Enalapril. The development of such data base in hospitals may help for the surveillance of pDDIs in hospitalized cardiac patients. The physicians should be aware of interactions among those drugs while prescribing for patients and thorough monitoring should be required for the patient safety by the implementation of admonitory guidelines and computer-based screening, which might help to prevent potentially harmful drug interactions

Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients

Background: It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs. Methods: Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs. Results: MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding. Conclusions: An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs

Toward a complete dataset of drug-drug interaction information from publicly available sources

AbstractAlthough potential drug–drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes.An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data

The Implicitome: A Resource for Rationalizing Gene-Disease Associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.UB – Publicatie

Utilização de uma ferramenta informática no planeamento cirúrgico de implantes dentários : SAC Assessment Tool

A SAC Assessment Tool é uma ferramenta informática lançada pelo International Team for Implantology em 2007 com o objetivo de auxiliar os Médicos Dentistas no diagnóstico e no plano de tratamento de uma reabilitação com implantes dentários. Neste estudo, foi utilizada a vertente de avaliação cirúrgica desta ferramenta informática, em pacientes eventualmente candidatos a uma reabilitação com implantes dentários, com os seguintes objetivos: analisar e caraterizar a desdentação parcial e validar esta ferramenta informática enquanto sistema de suporte à decisão clínica. Numa primeira fase, efetuou-se uma análise descritiva das zonas desdentadas de pacientes desdentados parciais da Clínica Universitária, particularmente ao nível da classificação da desdentação parcial de Kennedy e do American College of Prosthodontics, utilizando a SAC Assessment Tool. Numa segunda fase, foram selecionados de forma aleatória 30 casos clínicos, dos quais foram recolhidos dados da anamnese, modelos de estudo, fotografias intra-orais e radiografias panorâmicas. Todos os dados foram analisados, com e sem acesso à SAC Assessment Tool por um Médico Dentista que se considerou como “Gold-standard” para efeitos de análise comparativa, e por um grupo de três Médicos Dentistas com experiência clínica e formação pós-graduada inferior. Os dados foram analisados através dos testes de concordância estatística – teste estatístico K (Fleiss Kappa), coeficiente de inter-relação de classes (ICC) e proporção de concordância. Obtiveram-se então os seguintes resultados: maior prevalência de indivíduos na faixa etária dos 56-83 anos (63,32%) e medicamente comprometidos (53,00%); 51,66% dos casos são Classes III de Kennedy na arcada superior e 40,00% são Classes I de Kennedy na arcada inferior; a Classe IV de desdentação parcial é a mais prevalente (65,00%); apenas 22,00% dos pacientes apresentam uma boa higiene oral; 50,66% dos casos são de grau de complexidade elevado, 30,66% são de grau intermédio e 18,66% de grau baixo. Há um aumento significativo de concordância quando os Avaliadores comparados com um perito, utilizam a SAC para realizar o diagnóstico de diversas áreas desdentadas, mostrando que a SAC é uma ferramenta bastante útil e eficaz para utilização na prática clínicaThe SAC Assessment Tool is an informatics software launched by the International Team for Implantology in 2007 aiming to help dentists in the diagnose and treatment plan in an oral rehabilitation with dental implants. The current study evaluated the surgical assessment of the SAC Assessment Tool with the main objective of analyse and characterize the partial edentulism and validate the informatics tool as a clinical decision support system. In the 1st phase, was performed a descriptive analysis of the partial edentulous patients at the University Clinic using the SAC Assessment Tool. In the 2nd phase, were random selected 30 clinical cases and clinical records, study models, intra and extra-oral photos and panoramic radiographies were obtained. All data were analysed with and without SAC Assessment Tool by a “Gold-standard” and compared with 3 reviewers with clinical experience and postgraduate qualification. All data were analysed using statically agreement tests (Fless Kappa), inter-class correlation (icc) and agreement rate. The following results were obtained: there was a major prevalence of individuals with higher age (56-83 years) – 63,32% and medical status compromised 53,00%; 51,66% of the clinical cases were superior Kennedy’s Class III and 40,00% were inferior Kennedy’s Class I; As regards the Classification of Partial Edentulism, Class IV were the most prevalent (65,00%); only 22,00% of the patients had a good oral hygiene; 50,66% were Complex cases, 30,66% were Advanced and 18,66% were Straightforward. There was a higher agreement when Reviewers and Gold standard use the SAC Assessment Tool and compared their evaluation. The results showed that the SAC Classification is a very useful informatics tool that helps in clinical practice