Expert knowledge for computerized ECG interpretation

In this study, two main questions are addressed: (1) Can the time consuming and cumbersome development and refinement of (heuristic) ECG classifiers be alleviated, and (2) Is it possible to increase diagnostic performance of ECG computer programs by combining knowledge from multiple sources? Chapters 2 and 3 are of an introductory character. In Chapter 2, the measurement part of MEANS is described and evaluated. This research largely depends on the earlier work of Talman [11]. In Chapter 3, different methods of diagnostic ECG classification are described and their pros and cons discussed. The issue is raised whether or not the ECG should be classified using as much prior information as possible, and our position is made clear. The first question~ how to ease the transfer of cardiological knowledge into computer algorithms, is addressed in Chapters 4 and 5. The development and refinement of heuristic ECG classifiers is impeded by two problems: (1) It generally requires a computer expert to translate the cardiologist's reasoning into computer language without the average cardiologist being able to verify whether his diagnostic intentions were properly realized, and (2) The classifiers are often so complex as to obscure insight into their doings when a particular case is processed by the classification program. To circumvent these problems. we developed a dedicated language. DTL (Decision Tree Language), and an interpreter and compiler of that language. In Chapter 4, a comprehensive description of the DTL environment is given. In Chapter 5, the use of the environment to optimize MEANS, following a procedure of stepwise refmement, is described The second question, whether it is feasible to combine knowledge from multiple sources in order to increase diagnostic performance of an ECG computer program, is explored from several perspectives in Chapters 6 tlrrough

Minimum bandwidth requirements for recording of pediatric electrocardiograms

BACKGROUND: Previous studies that determined the frequency content of the pediatric ECG had their limitations: the study population was small or the sampling frequency used by the recording system was low. Therefore, current bandwidth recommendations for recording pediatric ECGs are not well founded. We wanted to establish minimum bandwidth requirements using a large set of pediatric ECGs recorded at a high sampling rate. METHODS AND RESULTS: For 2169 children aged 1 day to 16 years, a 12-lead ECG was recorded at a sampling rate of 1200 Hz. The averaged beats of each ECG were passed through digital filters with different cut off points (50 to 300 Hz in 25-Hz steps). We measured the absolute errors in maximum QRS amplitude for each simulated bandwidth and determined the percentage of records with an error >25 microV. We found that in any lead, a bandwidth of 250 Hz yields amplitude errors 95% of the children <1 year. For older children, a gradual decrease in ECG frequency content was demonstrated. CONCLUSIONS: We recommend a minimum bandwidth of 250 Hz to record pediatric ECGs. This bandwidth is considerably higher than the previous recommendation of 150 Hz from the American Heart Association

Training text chunkers on a silver standard corpus: Can silver replace gold?

Background: To train chunkers in recognizing noun phrases and verb phrases in biomedical text, an annotated corpus is required. The creation of gold standard corpora (GSCs), however, is expensive and time-consuming. GSCs therefore tend to be small and to focus on specific subdomains, which limits their usefulness. We investigated the use of a silver standard corpus (SSC) that is automatically generated by combining the outputs of multiple chunking systems. We explored two use scenarios: one in which chunkers are trained on an SSC in a new domain for which a GSC is not available, and one in which chunkers are trained on an available, although small GSC but supplemented with an SSC.Results: We have tested the two scenarios using three chunkers, Lingpipe, OpenNLP, and Yamcha, and two different corpora, GENIA and PennBioIE. For the first scenario, we showed that the systems trained for noun-phrase recognition on the SSC in one domain performed 2.7-3.1 percenta

QT dispersion as an attribute of T-loop morphology

BACKGROUND: The suggestion that increased QT dispersion (QTD) is due to increased differences in local action potential durations within the myocardium is wanting. An alternative explanation was sought by relating QTD to vectorcardiographic T-loop morphology. METHODS AND RESULTS: The T loop is characterized by its amplitude and width (defined as the spatial angle between the mean vectors of the first and second halves of the loop). We reasoned that small, wide ("pathological") T loops produce larger QTD than large, narrow ("normal") loops. To quantify the relationship between QTD and T-loop morphology, we used a program for automated analysis of ECGs and a database of 1220 standard simultaneous 12-lead ECGs. For each ECG, QT durations, QTD, and T-loop parameters were computed. T-loop amplitude and width were dichotomized, with 250 microV (small versus large amplitudes) and 30 degrees (narrow versus wide loops) taken as thresholds. Over all 1220 ECGs, QTDs were smallest for large, narrow T loops (54.2+/-27.1 ms) and largest for small, wide loops (69. 5+/-33.5 ms; P<0.001). CONCLUSIONS: QTD is an attribute of T-loop morphology, as expressed by T-loop amplitude and width

República: Año III Número 346 - (10/08/33)

BACKGROUND: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins. RESULTS: Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best. CONCLUSION: We conclude that both predicate and provenance information provide added value for drug efficacy screening

Discovering information from an integrated graph database

The information explosion in science has become a different problem, not the sheer amount per se, but the multiplicity and heterogeneity of massive sets of data sources. Relations mined from these heterogeneous sources, namely texts, database records, and ontologies have been mapped to Resource Description Framework (RDF) triples in an integrated database. The subject and object resources are expressed as references to concepts in a biomedical ontology consisting of the Unified Medical Language System (UMLS), UniProt and EntrezGene and for the predicate resource to a predicate thesaurus. All RDF triples have been stored in a graph database, including provenance. For evaluation we used an actual formal PRISMA literature study identifying 61 cerebral spinal fluid biomarkers and 200 blood biomarkers for migraine. These biomarkers sets could be retrieved with weighted mean average precision values of 0.32 and 0.59, respectively, and can be used as a first reference for further refinements

Drug-induced atrial fibrillation

Atrial fibrillation (AF) is the most common sustained rhythm disorder observed in clinical practice and predominantly associated with cardiovascular disorders such as coronary heart disease and hypertension. However, several classes of drugs may induce AF in patients without apparent heart disease or may precipitate the onset of AF in patients with preexisting heart disease. We reviewed the literature on drug-induced AF, using the PubMed/Medline and Micromedex databases and lateral references. Successively, we discuss the potential role in the onset of AF of cardiovascular drugs, respiratory system drugs, cytostatics, central nervous system drugs, genitourinary system drugs, and some miscellaneous agents. Drug-induced AF may play a role in only a minority of the patients presenting with AF. Nevertheless, it is important to recognize drugs or other agents as a potential cause, especially in the elderly, because increasing age is associated with multiple drug use and a high incidence of AF. This may contribute to timely diagnosis and management of drug-induced AF