A Large-Scale CNN Ensemble for Medication Safety Analysis

Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating such effects. In this paper, we propose an end-to-end CNN-based method for predicting drug safety on user comments from healthcare discussion forums. We present an architecture that is based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote. To evaluate the performance of the proposed solution, we present a large-scale dataset collected from a medical website that consists of over 50 thousand reviews for more than 4000 drugs. The results demonstrate that our model significantly outperforms conventional approaches and predicts medicine safety with an accuracy of 87.17% for binary and 62.88% for multi-classification tasks

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena

Full-Reference Video Quality Assessment using Structural Similarity (SSIM) Index

Video Quality Assessment is one of the key words in the field of Quality of Service (QoS) for mobile phones, today. The goal of video quality assessment is to evaluate if a distorted video is of a good quality by quantifying the difference between the original and distorted video. To assess the video quality of an arbitrary distorted or compressed video, the visual features of the distorted video are compared with those of the original video. Objective video quality measures play important roles in a variety of video processing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. In this paper, we follow a new algorithm Structural Similarity (SSIM) Index in designing video quality metrics, which uses structural distortion as an estimate of perceived visual distortion. This algorithm is simple, straight forward, makes real time implementation easy, very consistent relation with the subjective measures and delivers more accurate results compared to other objective video quality measures MSE and PSNR and computationally efficient for full-reference (FR) video quality assessment

Patent Retrieval in Chemistry based on semantically tagged Named Entities

Gurulingappa H, Müller B, Klinger R, et al. Patent Retrieval in Chemistry based on semantically tagged Named Entities. In: Voorhees EM, Buckland LP, eds. The Eighteenth Text RETrieval Conference (TREC 2009) Proceedings. Gaithersburg, Maryland, USA; 2009.This paper reports on the work that has been conducted by Fraunhofer SCAI for Trec Chemistry (Trec-Chem) track 2009. The team of Fraunhofer SCAI participated in two tasks, namely Technology Survey and Prior Art Search. The core of the framework is an index of 1.2 million chemical patents provided as a data set by Trec. For the technology survey, three runs were submitted based on semantic dictionaries and noun phrases. For the prior art search task, several elds were introduced into the index that contained normalized noun phrases, biomedical as well as chemical entities. Altogether, 36 runs were submitted for this task that were based on automatic querying with tokens, noun phrases and entities along with dierent search strategies

Beauveria bassiana and Metarhizium anisopliae endophytically colonize cassava roots following soil drench inoculation

AbstractWe investigated the fungal entomopathogens Beauveria bassiana and Metarhizium anisopliae to determine if endophytic colonization could be achieved in cassava. An inoculation method based on drenching the soil around cassava stem cuttings using conidial suspensions resulted in endophytic colonization of cassava roots by both entomopathogens, though neither was found in the leaves or stems of the treated cassava plants. Both fungal entomopathogens were detected more often in the proximal end of the root than in the distal end. Colonization levels of B. bassiana were higher when plants were sampled at 7–9days post-inoculation (84%) compared to 47–49days post-inoculation (40%). In contrast, the colonization levels of M. anisopliae remained constant from 7–9days post-inoculation (80%) to 47–49days post-inoculation (80%), which suggests M. anisopliae is better able to persist in the soil, or as an endophyte in cassava roots over time. Differences in colonization success and plant growth were found among the fungal entomopathogen treatments

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels