Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation

Dedicated Energy Crop Supply Chain and Associated Feedstock Transportation Emissions: A Case Study of Tennessee

and Bradly Wilson This study minimizes total cost for single-feedstock supply chains of two dedicated energy crops, perennial switchgrass and biomass sorghum, in Tennessee using a spatial optimization model. Greenhouse gas emissions from the transport of feedstock to the conversion facility were estimated for respective feedstock supply chains. Results show that different demand for land types from two feedstocks and the geographically diverse landscape across the state affect the economics of bioenergy crops supply chains and feedstock transportation emissions. Switchgrass is more suitable than biomass sorghum for biofuel production in Tennessee based on the supply chains cost and feedstock hauling emissions

Glycemic Index and Pregnancy: A Systematic Literature Review

Background/Aim. Dietary glycemic index (GI) has received considerable research interest over the past 25 years although its application to pregnancy outcomes is more recent. This paper critically evaluates the current evidence regarding the effect of dietary GI on maternal and fetal nutrition. Methods. A systematic literature search using MEDLINE, EMBASE, CINAHL, Cochrane Library, SCOPUS, and ISI Web of Science, from 1980 through September 2010, was conducted. Results. Eight studies were included in the systematic review. Two interventional studies suggest that a low-GI diet can reduce the risk of large-for-gestational-age (LGA) infants in healthy pregnancies, but one epidemiological study reported an increase in small-for-gestational-age (SGA) infants. Evidence in pregnancies complicated by gestational diabetes mellitus (GDM), though limited (n = 3), consistently supports the advantages of a low-GI diet. Conclusion. There is insufficient evidence to recommend a low-GI diet during normal pregnancy. In pregnancy complicated by GDM, a low-GI diet may reduce the need for insulin without adverse effects on pregnancy outcomes. Until larger-scale intervention trials are completed, a low-GI diet should not replace the current recommended pregnancy diets from government and health agencies. Further research regarding the optimal time to start a low-GI diet for maximum protection against adverse pregnancy outcomes is warranted

Preparation and biomedical application of a non-polymer coated superparamagnetic nanoparticle

We report the preparation of a non-polymer coated superparamagnetic nanoparticle that is stable and biocompatible both in vitro and in vivo. The non-polymer, betaine, is a natural methylating agent in mammalian liver with active surface property. Upon systemic administration, the nanoparticle has preferential biodistribution in mammalian liver and exhibits good reduction of relaxivity time and negative enhancement for the detection of hepatoma nodules in rats using MRI. Our data demonstrate that the non-polymer coated superparamagnetic nanoparticle should have potential applications in biomedicine

MicroRNAs targeting oncogenes are down-regulated in pancreatic malignant transformation from benign tumors

BACKGROUND MicroRNA (miRNA) expression profiles have been described in pancreatic ductal adenocarcinoma (PDAC), but these have not been compared with pre-malignant pancreatic tumors. We wished to compare the miRNA expression signatures in pancreatic benign cystic tumors (BCT) of low and high malignant potential with PDAC, in order to identify miRNAs deregulated during PDAC development. The mechanistic consequences of miRNA dysregulation were further evaluated. METHODS Tissue samples were obtained at a tertiary pancreatic unit from individuals with BCT and PDAC. MiRNA profiling was performed using a custom microarray and results were validated using RT-qPCR prior to evaluation of miRNA targets. RESULTS Widespread miRNA down-regulation was observed in PDAC compared to low malignant potential BCT. We show that amongst those miRNAs down-regulated, miR-16, miR-126 and let-7d regulate known PDAC oncogenes (targeting BCL2, CRK and KRAS respectively). Notably, miR-126 also directly targets the KRAS transcript at a "seedless" binding site within its 3'UTR. In clinical specimens, miR-126 was strongly down-regulated in PDAC tissues, with an associated elevation in KRAS and CRK proteins. Furthermore, miR-21, a known oncogenic miRNA in pancreatic and other cancers, was not elevated in PDAC compared to serous microcystic adenoma (SMCA), but in both groups it was up-regulated compared to normal pancreas, implicating early up-regulation during malignant change. CONCLUSIONS Expression profiling revealed 21 miRNAs down-regulated in PDAC compared to SMCA, the most benign lesion that rarely progresses to invasive carcinoma. It appears that miR-21 up-regulation is an early event in the transformation from normal pancreatic tissue. MiRNA expression has the potential to distinguish PDAC from normal pancreas and BCT. Mechanistically the down-regulation of miR-16, miR-126 and let-7d promotes PDAC transformation by post-transcriptional up-regulation of crucial PDAC oncogenes. We show that miR-126 is able to directly target KRAS; re-expression has the potential as a therapeutic strategy against PDAC and other KRAS-driven cancers

Oncological outcome after free jejunal flap reconstruction for carcinoma of the hypopharynx

It has been a common practice among the oncologist to reduce the dosage of adjuvant radiotherapy for patients after free jejunal flap reconstruction. The current aims to study potential risk of radiation to the visceral flap and the subsequent oncological outcome. Between 1996 and 2010, consecutive patients with carcinoma of the hypopharynx requiring laryngectomy, circumferential pharyngectomy and post-operative irradiation were recruited. Ninety-six patients were recruited. TNM tumor staging at presentation was: stage II (40.6%), stage III (34.4%) and stage IV (25.0%). Median follow-up period after surgery was 68 months. After tumor ablation, reconstruction was performed using free jejunal flap (60.4%), pectoralis major myocutaneous (PM) flap (31.3%) and free anterolateral thigh (ALT) flap (8.3%). All patients underwent adjuvant radiotherapy within 6.4 weeks after surgery. The mean total dose of radiation given to those receiving cutaneous and jejunal flap reconstruction was 62.2 Gy and 54.8 Gy, respectively. There was no secondary ischaemia or necrosis of the flaps after radiotherapy. The 5-year actuarial loco-regional tumor control for the cutaneous flap and jejunal flap group was: stage II (61 vs. 69%, p = 0.9), stage III (36 vs. 46%, p = 0.2) and stage IV (32 vs. 14%, p = 0.04), respectively. Reduction of radiation dosage in free jejunal group adversely affects the oncological control in stage IV hypopharyngeal carcinoma. In such circumstances, tubed cutaneous flaps are the preferred reconstructive option, so that full-dose radiotherapy can be given

Is searching full text more effective than searching abstracts?

<p>Abstract</p> <p>Background</p> <p>With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE^®abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: <it>bm25 </it>and the ranking algorithm implemented in the open-source Lucene search engine.</p> <p>Results</p> <p>Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles.</p> <p>Conclusion</p> <p>Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.</p