Prediction of advertisement preference by fusing EEG response and sentiment analysis.

Neural Netw. 2017 Feb 16;:

Authors: Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B

Abstract
This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data.

PMID: 28254237 [PubMed - as supplied by publisher]

Natural Language Processing in Oncology: A Review.

JAMA Oncol. 2016 Jun 01;2(6):797-804

Authors: Yim WW, Yetisgen M, Harris WP, Kwan SW

Abstract
IMPORTANCE: Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records.
OBSERVATIONS: Natural language processing can be used as a tool for oncological evidence-based research and quality improvement. Oncologists interested in applying NLP for clinical research can play pivotal roles in building NLP systems and, in doing so, contribute to both oncological and clinical NLP research. Herein, we provide an introduction to NLP and its potential applications in oncology, a description of specific tools available, and a review on the state of the current technology with respect to cancer case identification, staging, and outcomes quantification.
CONCLUSIONS AND RELEVANCE: More automated means of leveraging unstructured data from daily clinical practice is crucial as therapeutic options and access to individual-level health information increase. Research-minded oncologists may push the avenues of evidence-based research by taking advantage of the new technologies available with clinical NLP. As continued progress is made with applying NLP toward oncological research, incremental gains will lead to large impacts, building a cost-effective infrastructure for advancing cancer care.

PMID: 27124593 [PubMed - indexed for MEDLINE]

Automated discovery of safety and efficacy concerns for joint & muscle pain relief treatments from online reviews.

Int J Med Inform. 2017 Apr;100:108-120

Authors: Adams DZ, Gruss R, Abrahams AS

Abstract
OBJECTIVES: Product issues can cost companies millions in lawsuits and have devastating effects on a firm's sales, image and goodwill, especially in the era of social media. The ability for a system to detect the presence of safety and efficacy (S&E) concerns early on could not only protect consumers from injuries due to safety hazards, but could also mitigate financial damage to the manufacturer. Prior studies in the field of automated defect discovery have found industry-specific techniques appropriate to the automotive, consumer electronics, home appliance, and toy industries, but have not investigated pain relief medicines and medical devices. In this study, we focus specifically on automated discovery of S&E concerns in over-the-counter (OTC) joint and muscle pain relief remedies and devices.
METHODS: We select a dataset of over 32,000 records for three categories of Joint & Muscle Pain Relief treatments from Amazon's online product reviews, and train "smoke word" dictionaries which we use to score holdout reviews, for the presence of safety and efficacy issues. We also score using conventional sentiment analysis techniques.
RESULTS: Compared to traditional sentiment analysis techniques, we found that smoke term dictionaries were better suited to detect product concerns from online consumer reviews, and significantly outperformed the sentiment analysis techniques in uncovering both efficacy and safety concerns, across all product subcategories.
CONCLUSION: Our research can be applied to the healthcare and pharmaceutical industry in order to detect safety and efficacy concerns, reducing risks that consumers face using these products. These findings can be highly beneficial to improving quality assurance and management in joint and muscle pain relief.

PMID: 28241932 [PubMed - in process]

Text mining approach to predict hospital admissions using early medical records from the emergency department.

Int J Med Inform. 2017 Apr;100:1-8

Authors: Lucini FR, S Fogliatto F, C da Silveira GJ, L Neyeloff J, Anzanello MJ, de S Kuchenbecker R, D Schaan B

Abstract
OBJECTIVE: Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges.
DESIGN: We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ(2) and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear).
MEASUREMENTS: Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested.
RESULTS: Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%.
CONCLUSIONS: The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams.

PMID: 28241931 [PubMed - in process]

Early recognition of multiple sclerosis using natural language processing of the electronic health record.

BMC Med Inform Decis Mak. 2017 Feb 28;17(1):24

Authors: Chase HS, Mitrani LR, Lu GG, Fulgieri DJ

Abstract
BACKGROUND: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers.
METHODS: An MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients' notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients' notes than controls'. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification.
RESULTS: Classification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23-59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS.
CONCLUSIONS: Diagnostic accuracy might be improved by mining patients' clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

PMID: 28241760 [PubMed - in process]

Nematode neuropeptides as transgenic nematicides.

PLoS Pathog. 2017 Feb 27;13(2):e1006237

Authors: Warnock ND, Wilson L, Patten C, Fleming CC, Maule AG, Dalzell JJ

Abstract
Plant parasitic nematodes (PPNs) seriously threaten global food security. Conventionally an integrated approach to PPN management has relied heavily on carbamate, organophosphate and fumigant nematicides which are now being withdrawn over environmental health and safety concerns. This progressive withdrawal has left a significant shortcoming in our ability to manage these economically important parasites, and highlights the need for novel and robust control methods. Nematodes can assimilate exogenous peptides through retrograde transport along the chemosensory amphid neurons. Peptides can accumulate within cells of the central nerve ring and can elicit physiological effects when released to interact with receptors on adjoining cells. We have profiled bioactive neuropeptides from the neuropeptide-like protein (NLP) family of PPNs as novel nematicides, and have identified numerous discrete NLPs that negatively impact chemosensation, host invasion and stylet thrusting of the root knot nematode Meloidogyne incognita and the potato cyst nematode Globodera pallida. Transgenic secretion of these peptides from the rhizobacterium, Bacillus subtilis, and the terrestrial microalgae Chlamydomonas reinhardtii reduce tomato infection levels by up to 90% when compared with controls. These data pave the way for the exploitation of nematode neuropeptides as a novel class of plant protective nematicide, using novel non-food transgenic delivery systems which could be deployed on farmer-preferred cultivars.

PMID: 28241060 [PubMed - as supplied by publisher]

Text mining a self-report back-translation.

Psychol Assess. 2016 06;28(6):750-64

Authors: Blanch A, Aluja A

Abstract
There are several recommendations about the routine to undertake when back translating self-report instruments in cross-cultural research. However, text mining methods have been generally ignored within this field. This work describes a text mining innovative application useful to adapt a personality questionnaire to 12 different languages. The method is divided in 3 different stages, a descriptive analysis of the available back-translated instrument versions, a dissimilarity assessment between the source language instrument and the 12 back-translations, and an item assessment of item meaning equivalence. The suggested method contributes to improve the back-translation process of self-report instruments for cross-cultural research in 2 significant intertwined ways. First, it defines a systematic approach to the back translation issue, allowing for a more orderly and informed evaluation concerning the equivalence of different versions of the same instrument in different languages. Second, it provides more accurate instrument back-translations, which has direct implications for the reliability and validity of the instrument's test scores when used in different cultures/languages. In addition, this procedure can be extended to the back-translation of self-reports measuring psychological constructs in clinical assessment. Future research works could refine the suggested methodology and use additional available text mining tools. (PsycINFO Database Record

PMID: 26302100 [PubMed - indexed for MEDLINE]

Analysis of Patient Narratives in Disease Blogs on the Internet: An Exploratory Study of Social Pharmacovigilance.

JMIR Public Health Surveill. 2017 Feb 24;3(1):e10

Authors: Matsuda S, Aoki K, Tomizawa S, Sone M, Tanaka R, Kuriki H, Takahashi Y

Abstract
BACKGROUND: Although several reports have suggested that patient-generated data from Internet sources could be used to improve drug safety and pharmacovigilance, few studies have identified such data sources in Japan. We introduce a unique Japanese data source: tōbyōki, which translates literally as "an account of a struggle with disease."
OBJECTIVE: The objective of this study was to evaluate the basic characteristics of the TOBYO database, a collection of tōbyōki blogs on the Internet, and discuss potential applications for pharmacovigilance.
METHODS: We analyzed the overall gender and age distribution of the patient-generated TOBYO database and compared this with other external databases generated by health care professionals. For detailed analysis, we prepared separate datasets for blogs written by patients with depression and blogs written by patients with rheumatoid arthritis (RA), because these conditions were expected to entail subjective patient symptoms such as discomfort, insomnia, and pain. Frequently appearing medical terms were counted, and their variations were compared with those in an external adverse drug reaction (ADR) reporting database. Frequently appearing words regarding patients with depression and patients with RA were visualized using word clouds and word cooccurrence networks.
RESULTS: As of June 4, 2016, the TOBYO database comprised 54,010 blogs representing 1405 disorders. Overall, more entries were written by female bloggers (68.8%) than by male bloggers (30.8%). The most frequently observed disorders were breast cancer (4983 blogs), depression (3556), infertility (2430), RA (1118), and panic disorder (1090). Comparison of medical terms observed in tōbyōki blogs with those in an external ADR reporting database showed that subjective and symptomatic events and general terms tended to be frequently observed in tōbyōki blogs (eg, anxiety, headache, and pain), whereas events using more technical medical terms (eg, syndrome and abnormal laboratory test result) tended to be observed frequently in the ADR database. We also confirmed the feasibility of using visualization techniques to obtain insights from unstructured text-based tōbyōki blog data. Word clouds described the characteristics of each disorder, such as "sleeping" and "anxiety" in depression and "pain" and "painful" in RA.
CONCLUSIONS: Pharmacovigilance should maintain a strong focus on patients' actual experiences, concerns, and outcomes, and this approach can be expected to uncover hidden adverse event signals earlier and to help us understand adverse events in a patient-centered way. Patient-generated tōbyōki blogs in the TOBYO database showed unique characteristics that were different from the data in existing sources generated by health care professionals. Analysis of tōbyōki blogs would add value to the assessment of disorders with a high prevalence in women, psychiatric disorders in which subjective symptoms have important clinical meaning, refractory disorders, and other chronic disorders.

PMID: 28235749 [PubMed - in process]

Representing Documents via Latent Keyphrase Inference.

Proc Int World Wide Web Conf. 2016 Apr;2016:1057-1067

Authors: Liu J, Ren X, Shang J, Cassidy T, Voss CR, Han J

Abstract
Many text mining approaches adopt bag-of-words or n-grams models to represent documents. Looking beyond just the words, i.e., the explicit surface forms, in a document can improve a computer's understanding of text. Being aware of this, researchers have proposed concept-based models that rely on a human-curated knowledge base to incorporate other related concepts in the document representation. But these methods are not desirable when applied to vertical domains (e.g., literature, enterprise, etc.) due to low coverage of in-domain concepts in the general knowledge base and interference from out-of-domain concepts. In this paper, we propose a data-driven model named Latent Keyphrase Inference (LAKI) that represents documents with a vector of closely related domain keyphrases instead of single words or existing concepts in the knowledge base. We show that given a corpus of in-domain documents, topical content units can be learned for each domain keyphrase, which enables a computer to do smart inference to discover latent document keyphrases, going beyond just explicit mentions. Compared with the state-of-art document representation approaches, LAKI fills the gap between bag-of-words and concept-based models by using domain keyphrases as the basic representation unit. It removes dependency on a knowledge base while providing, with keyphrases, readily interpretable representations. When evaluated against 8 other methods on two text mining tasks over two corpora, LAKI outperformed all.

PMID: 28229132 [PubMed - in process]

Optic Disc and Macular Imaging in Blind Eyes from Non-glaucomatous Optic Neuropathy: A Study with Spectral-domain Optical Coherence Tomography.

Neuroophthalmology. 2017 Feb;41(1):1-6

Authors: Hansapinyo L, Cheng AC, Chan NC, Chan CK

Abstract
The purpose of this study was to determine and compare the optic disc and macular thickness measurements using two spectral-domain optical coherence tomography (SD-OCT) instruments in long-standing blind eyes diagnosed with non-glaucomatous optic neuropathies (NGON). A prospective observational case-series design was used. Twelve eyes from 12 NGON patients with no light perception for at least 6 months underwent optic disc and macular imaging with Cirrus HD-OCT and Spectralis OCT. The correlation between the peripapillary retinal nerve fibre layer (PRNFL) and macular ganglion cell layer and inner plexiform layer (GCL+IPL) thicknesses, and between the duration of no light perception (NLP) and PRNFL/GCL+IPL thicknesses were determined using Spearman's correlation analysis. The mean average PRNFL thickness was 55.9 ± 4.8 µm for Cirrus HD-OCT, which was significantly thicker than that measured by Spectralis OCT (31.9 ± 7.4 µm; p < 0.001). The mean central macular thickness on Cirrus HD-OCT was normal, but there was global thinning at the other macular areas. The mean average GCL+IPL thickness on Cirrus HD-OCT was 51.8 ± 5.8 µm. There was a good correlation between average PRNFL thickness and GCL+IPL thickness (r = 0.830, p = 0.002); however, there was no significant correlation between the duration of NLP to the average PRNFL thickness (on either instruments) or GCL+IPL thickness on Cirrus HD-OCT (p > 0.7). These results show that there was residual PRNFL thickness in NGON eyes with NLP, which varied significantly between SD-OCT instruments. The values of the residual PRNFL and GCL+IPL thicknesses in blind eyes (the "floor" effect) may be useful for prognostic purposes for patients with partial optic atrophy.

PMID: 28228830 [PubMed - in process]

Visualizing patient journals by combining vital signs monitoring and natural language processing.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2529-2532

Authors: Vilic A, Petersen JA, Hoppe K, Sorensen HB, Vilic A, Petersen JA, Hoppe K, Sorensen HB, Petersen JA, Vilic A, Hoppe K, Sorensen HB

Abstract
This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.

PMID: 28227035 [PubMed - in process]

S2NI: a mobile platform for nutrition monitoring from spoken data.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1991-1994

Authors: Hezarjaribi N, Reynolds CA, Miller DT, Chaytor N, Ghasemzadeh H, Hezarjaribi N, Reynolds CA, Miller DT, Chaytor N, Ghasemzadeh H, Ghasemzadeh H, Reynolds CA, Chaytor N, Hezarjaribi N, Miller DT

Abstract
Diet and physical activity are important lifestyle and behavioral factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used in the past to objectively measure physical activity or detect eating time. Diet monitoring, however, still relies on self-recorded data by end users where individuals use mobile devices for recording nutrition intake by either entering text or taking images. Such approaches have shown low adherence in technology adoption and achieve only moderate accuracy. In this paper, we propose development and validation of Speech-to-Nutrient-Information (S2NI), a comprehensive nutrition monitoring system that combines speech processing, natural language processing, and text mining in a unified platform to extract nutrient information such as calorie intake from spoken data. After converting the voice data to text, we identify food name and portion size information within the text. We then develop a tiered matching algorithm to search the food name in our nutrition database and to accurately compute calorie intake. Due to its pervasive nature and ease of use, S2NI enables users to report their diet routine more frequently and at anytime through their smartphone. We evaluate S2NI using real data collected with 10 participants. Our experimental results show that S2NI achieves 80.6% accuracy in computing calorie intake.

PMID: 28226908 [PubMed - in process]

compMS2Miner: an automatable metabolite identification, visualization and data-sharing R package for high-resolution LC-MS datasets.

Anal Chem. 2017 Feb 22;:

Authors: Edmands WM, Petrick LM, Barupal DK, Scalbert A, Wilson M, Wickliffe J, Rappaport SM

Abstract
A long-standing challenge of untargeted metabolomic profiling by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation, therefore a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also a means to overview the MS2 data with a web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: i) matching unknown MS1 features to precursor MS2 scans, ii) filtration of spectral noise (dynamic noise filter), iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal iv) interpretation of possible fragment ion sub-structure using an internal database, v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization and vi) network graph visualizations, data curation and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a dataset of blood serum samples from 7 diet induced obese (DIO) and 7 non-obese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the supporting information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes (https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS&https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG). The workflow provided rapid annotation of a diversity of endogenous and gut microbially-derived metabolites affected by both diet and antibiotic treatment which conformed to previously published reports. Composite spectra (n=173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 commonly endogenous compound classes (e.g. 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette (https://github.com/WMBEdmands/compMS2Miner) and a version of the published application is available on the shinyapps.io site (https://wmbedmands.shinyapps.io/compMS2Example).

PMID: 28225587 [PubMed - as supplied by publisher]

Documenting research with transgender and gender diverse people: protocol for an evidence map and thematic analysis.

Syst Rev. 2017 Feb 20;6(1):35

Authors: Marshall Z, Welch V, Thomas J, Brunger F, Swab M, Shemilt I, Kaposy C

Abstract
BACKGROUND: There is limited information about how transgender, gender diverse, and Two-Spirit (trans) people have been represented and studied by researchers. The objectives of this study are to (1) map and describe trans research in the social sciences, sciences, humanities, health, education, and business, (2) identify evidence gaps and opportunities for more responsible research with trans people, (3) assess the use of text mining for study identification, and (4) increase access to trans research for key stakeholders through the creation of a web-based evidence map.
METHODS: Study design was informed by community consultations and pilot searches. Eligibility criteria were established to include all original research of any design, including trans people or their health information, and published in English in peer-reviewed journals. A complex electronic search strategy based on relevant concepts in 15 databases was developed to obtain a broad range of results linked to transgender, gender diverse, and Two-Spirit individuals and communities. Searches conducted in early 2015 resulted in 25,242 references after removal of duplicates. Based on the number of references, resources, and an objective to capture upwards of 90% of the existing literature, this study is a good candidate for text mining using Latent Dirichlet Allocation to improve efficiency of the screening process. The following information will be collected for evidence mapping: study topic, study design, methods and data sources, recruitment strategies, sample size, sample demographics, researcher name and affiliation, country where research was conducted, funding source, and year of publication.
DISCUSSION: The proposed research incorporates an extensive search strategy, text mining, and evidence map; it therefore has the potential to build on knowledge in several fields. Review results will increase awareness of existing trans research, identify evidence gaps, and inform strategic research prioritization. Publishing the map online will improve access to research for key stakeholders including community members, policy makers, and healthcare providers. This study will also contribute to knowledge in the area of text mining for study identification by providing an example of how semi-automation performs for screening on title and abstract and on full text.

PMID: 28219417 [PubMed - in process]

Development of a Definition for the Alcohol Hangover: Consumer Descriptions and Expert Consensus.

Curr Drug Abuse Rev. 2017 Feb 16;:

Authors: Verster JC, Lantman MV, van de Loo AJ, Mackus M

Abstract
Up to now there is no adequate definition of the alcohol hangover. The purpose of the current study was to develop a useful definition, and consensus among those who will use it in scientific publications. A survey was conducted among N=1099 social drinkers who recently had a hangover. They were asked to provide their definition of the alcohol hangover. Text mining and content analysis revealed 3 potential definitions. These were submitted to members of the Alcohol Hangover Research Group, who were asked to give their expert opinion on the proposed definitions. Taking into account their comments and suggestions, the following definition for the alcohol hangover was formulated: "The alcohol hangover refers to the combination of cognitive and physical symptoms, experienced the day after a single episode of heavy drinking, starting when blood alcohol concentration approaches zero."

PMID: 28215179 [PubMed - as supplied by publisher]

A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports.

PLoS One. 2016;11(4):e0153749

Authors: Nath C, Albaghdadi MS, Jonnalagadda SR

Abstract
Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to explore the feasibility and reliability of using natural language processing (NLP) for large-scale and targeted extraction of multiple data elements from echocardiography reports. An NLP tool, EchoInfer, was developed to automatically extract data pertaining to cardiovascular structure and function from heterogeneously formatted echocardiographic data sources. EchoInfer was applied to echocardiography reports (2004 to 2013) available from 3 different on-going clinical research projects. EchoInfer analyzed 15,116 echocardiography reports from 1684 patients, and extracted 59 quantitative and 21 qualitative data elements per report. EchoInfer achieved a precision of 94.06%, a recall of 92.21%, and an F1-score of 93.12% across all 80 data elements in 50 reports. Physician review of 400 reports demonstrated that EchoInfer achieved a recall of 92-99.9% and a precision of >97% in four data elements, including three quantitative and one qualitative data element. Failure of EchoInfer to correctly identify or reject reported parameters was primarily related to non-standardized reporting of echocardiography data. EchoInfer provides a powerful and reliable NLP-based approach for the large-scale, targeted extraction of information from heterogeneous data sources. The use of EchoInfer may have implications for the clinical management and research analysis of patients undergoing echocardiographic evaluation.

PMID: 27124000 [PubMed - indexed for MEDLINE]

Enhancement of antigen-specific CD4(+) and CD8(+) T cell responses using a self-assembled biologic nanolipoprotein particle vaccine.

Vaccine. 2017 Feb 14;:

Authors: Weilhammer D, Dunkle AD, Blanchette CD, Fischer NO, Corzett M, Lehmann D, Boone T, Hoeprich P, Driks A, Rasley A

Abstract
To address the need for vaccine platforms that induce robust cell-mediated immunity, we investigated the potential of utilizing self-assembling biologic nanolipoprotein particles (NLPs) as an antigen and adjuvant delivery system to induce antigen-specific murine T cell responses. We utilized OT-I and OT-II TCR-transgenic mice to investigate the effects of NLP-mediated delivery of the model antigen ovalbumin (OVA) on T cell activation. Delivery of OVA with the TLR4 agonist monophosphoryl lipid A (MPLA) in the context of NLPs significantly enhanced the activation of both CD4(+) and CD8(+) T cells in vitro compared to co-administration of free OVA and MPLA. Upon intranasal immunization of mice harboring TCR-transgenic cells, NLPs enhanced the adjuvant effects of MPLA and the in vivo delivery of OVA, leading to significantly increased expansion of CD4(+) and CD8(+) T cells in lung-draining lymph nodes. Therefore, NLPs are a promising vaccine platform for inducing T cell responses following intranasal administration.

PMID: 28214044 [PubMed - as supplied by publisher]

KIWI: A technology for public health event monitoring and early warning signal detection.

Online J Public Health Inform. 2016;8(3):e208

Authors: Mukhi SN

Abstract
OBJECTIVES: To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats.
METHODS: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper.
RESULTS: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%).
DISCUSSION: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities.
CONCLUSIONS: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

PMID: 28210429 [PubMed - in process]

Closantel; a veterinary drug with potential severe morbidity in humans.

BMC Ophthalmol. 2016 Nov 29;16(1):207

Authors: Tabatabaei SA, Soleimani M, Mansouri MR, Mirshahi A, Inanlou B, Abrishami M, Pakrah AR, Masarat H

Abstract
BACKGROUND: Closantel is a halogenated salicylanilide with a potent anti parasitic activity. It is widely used in management of parasitic infestation in animals, but is contraindicated in humans.
CASE PRESENTATION: A 34-year-old man with depression was referred to our center with progressive loss of vision in both eyes 10 days after unintentional ingestion of three 500 mg tablets of Closantel. On fundus examination, left optic disc margin was blurred. His bilateral visual acuity was no light perception (NLP) despite prescribed IV erythropoietin injections 20,000 units daily for 3 days and 1gr intravenous methylprednisolone acetate for 3 days followed by 1 mg/kg oral prednisolone. On macular optical coherence tomography (OCT), a disruption in outer retina was observed. Electroretinogram and visual evoked potential tests showed visual pathway involvement.
CONCLUSIONS: Destruction of neurosensory retina and visual pathways after accidental Closantel use is related to severe visual loss. This case alerts us about the destructive effect of this drug on humans even in low dosage which necessitates preventive efforts to reduce the chance of this morbid side effect.

PMID: 27899086 [PubMed - indexed for MEDLINE]

Assessment of tissue damage due to percutaneous nephrolithotomy using serum concentrations of inflammatory mediators.

Actas Urol Esp. 2015 Jun;39(5):283-90

Authors: Pérez-Fentes D, Gude F, Blanco-Parra M, Morón E, Ulloa B, García C

Abstract
OBJECTIVES: To determine the percutaneous nephrolithotomy (PCNL) effects on the tissues using the quantification of inflammatory mediators, and to assess their impact on the development of postoperative complications.
PATIENTS AND METHODS: Prospective observational non-randomized study on 40 patients underwent to PCNL. 50 patients with kidney stone who were treated by extracorporeal shock wave lithotripsy (ESWL) were used as control group. Interleukin-1beta (IL-1β), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and C-reactive protein (CRP) were determined at baseline (T0: before treatment), and at 2, 6 and 24hours after (T1, T2 and T3).
RESULTS: No relevant changes on IL-1β and TNF-α were found. IL-6 showed two peaks at 2 and 6hours post-PCNL (median 17.8 and 15.8 pg/mL, respectively). At 24hours CRP had reached its peak value (3.4mg/L). The group treated with ESWL no showed significant changes in any of the markers. The serum concentration of IL-6 and CRP at 24hours post-NLP is different depending on the occurrence of complications (P=.001 and P=.039, respectively). IL-6 showed a good predictive power for the development of complications (AUC .801).
CONCLUSIONS: Tissue damage caused by the PCNL is low. This damage increases significantly in those cases showing postoperative complications. IL-6 at 24hours has been shown to be a good predictive tool for the development of complications.

PMID: 25667173 [PubMed - indexed for MEDLINE]