Search

The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies. [ABSTRACT FROM AUTHOR]

Statistical hypothesis testing is an essential component of biological and medical studies for making inferences and estimations from the collected data in the study; however, the misuse of statistical tests is widely common. In order to prevent possible errors in convenient statistical test selection, it is currently possible to consult available test selection algorithms developed for various purposes. However, the lack of an algorithm presenting the most common statistical tests used in biomedical research in a single flowchart causes several problems such as shifting users among the algorithms, poor decision support in test selection and lack of satisfaction of potential users. Herein, we demonstrated a unified flowchart; covers mostly used statistical tests in biomedical domain, to provide decision aid to non-statistician users while choosing the appropriate statistical test for testing their hypothesis. We also discuss some of the findings while we are integrating the flowcharts into each other to develop a single but more comprehensive decision algorithm. [ABSTRACT FROM AUTHOR]

With drastically decreasing costs of genetic sequencing, it has become feasible to use individual genetic markers to optimize treatment selection in cancer therapy. However, it is still difficult for medical practitioners to integrate these new kinds of data into clinical routine, since available information is growing rapidly. We demonstrate how a blend of manual curation and automated data extraction and evidence synthesis can be used to generate a 'living review', a summarization of current evidence on cancer classification, corresponding genetic markers, genetic tests and treatment options that can be used by clinicians to refine treatment choices. In contrast to a classical review, this automated 'living review' offers the opportunity of automatically updating core content when available data changes, making it easier to keep an overview of the best current evidence. We discuss some of the findings we made while creating a prototype of a 'living review' for colorectal cancer pharmacotherapy. [ABSTRACT FROM AUTHOR]