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Natural Language Processing Systems for Capturing and Standardizing Unstructured Clinical Information: a systematic review.
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Natural Language Processing Systems for Capturing and Standardizing Unstructured Clinical Information: a systematic review.

J Biomed Inform. 2017 Jul 17;:

Authors: Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, Forshee R, Walderhaug M, Botsis T

Abstract
We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7,149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

PMID: 28729030 [PubMed - as supplied by publisher]




Quality Assurance of Chemical Ingredient Classification for the National Drug File - Reference Terminology.
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Quality Assurance of Chemical Ingredient Classification for the National Drug File - Reference Terminology.

J Biomed Inform. 2017 Jul 16;:

Authors: Zheng L, Yumak H, Chen L, Ochs C, Geller J, Kapusnik-Uner J, Perl Y

Abstract
The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies. However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT's Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors.

PMID: 28723580 [PubMed - as supplied by publisher]




Elucidating high-dimensional cancer hallmark annotation via enriched ontology.
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Elucidating high-dimensional cancer hallmark annotation via enriched ontology.

J Biomed Inform. 2017 Jul 16;:

Authors: Yan S, Wong KC

Abstract
MOTIVATION: Cancer hallmark annotation is a promising technique that could discover novel knowledge about cancer from the biomedical literature. The automated annotation of cancer hallmarks could reveal relevant cancer transformation processes in the literature or extract the articles that correspond to the cancer hallmark of interest. It acts as a complementary approach that can retrieve knowledge from massive text information, advancing numerous focused studies in cancer research. Nonetheless, the high-dimensional nature of cancer hallmark annotation imposes a unique challenge.
RESULTS: To address the curse of dimensionality, we compared multiple cancer hallmark annotation methods on 1580 PubMed abstracts. Based on the insights, a novel approach, UDT-RF, which makes use of ontological features is proposed. It expands the feature space via the Medical Subject Headings (MeSH) ontology graph and utilizes novel feature selections for elucidating the high-dimensional cancer hallmark annotation space. To demonstrate its effectiveness, state-of-the-art methods are compared and evaluated by a multitude of performance metrics, revealing the full performance spectrum on the full set of cancer hallmarks. Several case studies are conducted, demonstrating how the proposed approach could reveal novel insights into cancers.
AVAILABILITY: https://github.com/cskyan/chmannot.

PMID: 28723579 [PubMed - as supplied by publisher]




On the utility of 3D Hand Cursors to Explore Medical Volume Datasets with a Touchless Interface.
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On the utility of 3D Hand Cursors to Explore Medical Volume Datasets with a Touchless Interface.

J Biomed Inform. 2017 Jul 15;:

Authors: Simões Lopes D, Duarte de Figueiredo Parreira P, Figueiredo Paulo S, Nunes V, Amaral Rego P, Cassiano Neves M, Silva Rodrigues P, Armando Jorge J

Abstract
Analyzing medical volume datasets requires interactive visualization so that users can extract anatomo-physiological information in real-time. Conventional volume rendering systems rely on 2D input devices, such as mice and keyboards, which are known to hamper 3D analysis as users often struggle to obtain the desired orientation that is only achieved after several attempts. In this paper, we address which 3D analysis tools are better performed with 3D hand cursors operating on a touchless interface comparatively to a 2D input devices running on a conventional WIMP interface. The main goals of this paper are to explore the capabilities of (simple) hand gestures to facilitate sterile manipulation of 3D medical data on a touchless interface, without resorting on wearables, and to evaluate the surgical feasibility of the proposed interface next to senior surgeons (N = 5) and interns (N = 2). To this end, we developed a touchless interface controlled via hand gestures and body postures to rapidly rotate and position medical volume images in three-dimensions, where each hand acts as an interactive 3D cursor. User studies were conducted with laypeople, while informal evaluation sessions were carried with senior surgeons, radiologists and professional biomedical engineers. Results demonstrate its usability as the proposed touchless interface improves spatial awareness and a more fluent interaction with the 3D volume than with traditional 2D input devices, as it requires lesser number of attempts to achieve the desired orientation by avoiding the composition of several cumulative rotations, which is typically necessary in WIMP interfaces. However, tasks requiring precision such as clipping plane visualization and tagging are best performed with mouse-based systems due to noise, incorrect gestures detection and problems in skeleton tracking that need to be addressed before tests in real medical environments might be performed.

PMID: 28720438 [PubMed - as supplied by publisher]




CHI: A Contemporaneous Health Index for Degenerative Disease Monitoring using Longitudinal Measurements.
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CHI: A Contemporaneous Health Index for Degenerative Disease Monitoring using Longitudinal Measurements.

J Biomed Inform. 2017 Jul 13;:

Authors: Huang Y, Meng Q, Evans H, Lober W, Cheng Y, Qian X, Liu J, Huang S

Abstract
In this paper, we develop a novel formulation for contemporaneous patient risk monitoring by exploiting the emerging data-rich environment in many healthcare applications, where an abundance of longitudinal data that reflect the degeneration of the health condition can be continuously collected. Our objective, and the developed formulation, is fundamentally different from many existing risk score models for different healthcare applications, which mostly focus on predicting the likelihood of a certain outcome at a pre-specified time. Rather, our formulation translates multivariate longitudinal measurements into a contemporaneous health index (CHI) that captures patient condition changes over the course of progression. Another significant feature of our formulation is that, CHI can be estimated with or without label information, different from other risk score models strictly based on supervised learning. To develop this formulation, we focus on the degenerative disease conditions, for which we could utilize the monotonic progression characteristic (either towards disease or recovery) to learn CHI. Such a domain knowledge leads us to a novel learning formulation, and on top of that, we further generalize this formulation with a capacity to incorporate label information if available. We further develop algorithms to mitigate the challenges associated with the nonsmooth convex optimization problem by first identifying its dual reformulation as a constrained smooth optimization problem, and then, using the block coordinate descent algorithm to iteratively solve the optimization with a derived efficient projection at each iteration. Extensive numerical studies are performed on both synthetic datasets and real-world applications on Alzheimer's disease and Surgical Site Infection, which demonstrate the utility and efficacy of the proposed method on degenerative conditions that include a wide range of applications.

PMID: 28712748 [PubMed - as supplied by publisher]




Reproducibility of Studies on Text Mining for Citation Screening in Systematic Reviews: Evaluation and Checklist.
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Reproducibility of Studies on Text Mining for Citation Screening in Systematic Reviews: Evaluation and Checklist.

J Biomed Inform. 2017 Jul 12;:

Authors: Olorisade BK, Brereton P, Andras P

Abstract
CONTEXT: Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields. However, there is little work so far on reproduction studies in the field.
OBJECTIVE: In this paper, we investigate the reproducibility of studies in this area based on information contained in published articles and we propose reporting guidelines that could improve reproducibility.
METHODS: The study was approached in two ways. Initially we attempted to reproduce results from six studies, which were based on the same raw dataset. Then, based on this experience, we identified steps considered essential to successful reproduction of text mining experiments and characterized them to measure how reproducible is a study given the information provided on these steps. 33 articles were systematically assessed for reproducibility using this approach.
RESULTS: Our work revealed that it is currently difficult if not impossible to independently reproduce the results published in any of the studies investigated. The lack of information about the datasets used limits reproducibility of about 80% of the studies assessed. Also, information about the machine learning algorithms is inadequate in about 27% of the papers. On the plus side, the third party software tools used are mostly free and available.
CONCLUSIONS: The reproducibility potential of most of the studies can be significantly improved if more attention is paid to information provided on the datasets used, how they were partitioned and utilized, and how any randomization was controlled. We introduce a checklist of information that needs to be provided in order to ensure that a published study can be reproduced.

PMID: 28711679 [PubMed - as supplied by publisher]




Developing Smartphone Apps for Behavioural Studies: The AlcoRisk App Case Study.
Related Articles Developing Smartphone Apps for Behavioural Studies: The AlcoRisk App Case Study. J Biomed Inform. 2017 Jul 11;: Authors: Smith A, de Salas K, Lewis I, Schüz B Abstract Advances in mobile technology and significantly increasing utilization of mobile devices such as smartphones and tablets have resulted in a paradigm shift from PC-centric computing to mobile computing. The results of careful analysis conducted of this mobile landscape indicate that there is a growing demand for smart, user-centric, situation-aware mobile software. Invariably, concomitant with this demand is the need for methodologies that can provide support the development of this type of software. In this paper, we propose a semantic framework, called the mobile situation-aware framework, which supports efficient modeling, construction, processing, management, and inference of mobile situation information. The frame- work comprises two phases, situation modeling and situation construction, and can serve as a guide and a template in the development of situation-aware applications for the mobile environment. A case study in which mobile situation-aware framework is utilized in the development of a semantic media player verifies the efficacy of the proposed framework. Advances in mobile technology and significantly increasing utilization of mobile devices such as smartphones and tablets have resulted in a paradigm shift from PC-centric computing to mobile computing. The results of careful analysis conducted of this mobile landscape indicate that there is a growing demand for smart, user-centric, situation-aware mobile software. Invariably, concomitant with this demand is the need for methodologies that can provide support the development of this type of software. In this paper, we propose a semantic framework, called the mobile situation-aware framework, which supports efficient modeling, construction, processing, management, and inference of mobile situation information. The frame- work comprises two phases, situation modeling and situation construction, and can serve as a guide and a template in the development of situation-aware applications for the mobile environment. A case study in which mobile situation-aware framework is utilized in the development of a semantic media player verifies the efficacy of the proposed framework. Smartphone apps have emerged as valuable research tools to sample human behaviours at their time of occurrence within natural environments. Human behaviour sampling methods, such as Ecological Momentary Assessment (EMA), aim to facilitate research that is situated in ecologically valid real world environments rather than laboratory environments. Researchers have trialled a range of EMA smartphone apps to sample human behaviours such as dieting, physical activity and smoking. Software development processes for EMA smartphones apps, however, are not widely documented with little guidance provided for the integration of complex multidisciplinary behavioural and technical fields. In this paper, the AlcoRisk app for studying alcohol consumption and risk taking tendencies is presented alongside a software development process that integrates these multidisciplinary fields. The software development process consists of three stages including requirements analysis, feature and interface design followed by app implementation. Results from a preliminary feasibility study support the efficacy of the AlcoRisk app's software development process. PMID: 28709856 [PubMed - as supplied by publisher] [...]



Recurrent Neural Networks for Classifying Relations in Clinical Notes.
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Recurrent Neural Networks for Classifying Relations in Clinical Notes.

J Biomed Inform. 2017 Jul 07;:

Authors: Luo Y

Abstract
We proposed the first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes. We tested our models on the i2b2/VA relation classification challenge dataset. We showed that our segment LSTM model, with only word embedding feature and no manual feature engineering, achieved a micro-averaged f-measure of 0.661 for classifying medical problem-treatment relations, 0.800 for medical problem-test relations, and 0.683 for medical problem-medical problem relations. These results are comparable to those of the state-of-the-art systems on the i2b2/VA relation classification challenge. We compared the segment LSTM model with the sentence LSTM model, and demonstrated the benefits of exploring the difference between concept text and context text, and between different contextual parts in the sentence. We also evaluated the impact of word embedding on the performance of LSTM models and showed that medical domain word embedding help improve the relation classification. These results support the use of LSTM models for classifying relations between medical concepts, as they show comparable performance to previously published systems while requiring no manual feature engineering.

PMID: 28694119 [PubMed - as supplied by publisher]




Automatic classification of RDoC positive valence severity with a neural network.
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Automatic classification of RDoC positive valence severity with a neural network.

J Biomed Inform. 2017 Jul 08;:

Authors: Clark C, Wellner B, Davis R, Aberdeen J, Hirschman L

Abstract
OBJECTIVE: Our objective was to develop a machine learning-based system to determine the severity of Positive Valance symptoms for a patient, based on information included in their initial psychiatric evaluation. Severity was rated on an ordinal scale of 0-3 as follows: 0 (absent=no symptoms), 1 (mild=modest significance), 2 (moderate=requires treatment), 3 (severe=causes substantial impairment) by experts.
MATERIALS AND METHODS: We treated the task of assigning Positive Valence severity as a text classification problem. During development, we experimented with regularized multinomial logistic regression classifiers, gradient boosted trees, and feedforward, fully-connected neural networks. We found both regularization and feature selection via mutual information to be very important in preventing models from overfitting the data. Our best configuration was a neural network with three fully connected hidden layers with rectified linear unit activations.
RESULTS: Our best performing system achieved a score of 77.86%. The evaluation metric is an inverse normalization of the Mean Absolute Error presented as a percentage number between 0 and 100, where 100 means the highest performance. Error analysis showed that 90% of the system errors involved neighboring severity categories.
CONCLUSION: Machine learning text classification techniques with feature selection can be trained to recognize broad differences in Positive Valence symptom severity with a modest amount of training data (in this case 600 documents, 167 of which were unannotated). An increase in the amount of annotated data can increase accuracy of symptom severity classification by several percentage points. Additional features and/or a larger training corpus may further improve accuracy.

PMID: 28694118 [PubMed - as supplied by publisher]




Fuzzy Evidential Network and Its Application as Medical Prognosis and Diagnosis Models.
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Fuzzy Evidential Network and Its Application as Medical Prognosis and Diagnosis Models.

J Biomed Inform. 2017 Jul 06;:

Authors: Janghorbani A, Hassan Moradi M

Abstract
Uncertainty is one of the important facts of the medical knowledge. Medical prognosis and diagnosis, as the essential parts of medical knowledge, is affected by different aspects of uncertainty, which must be managed. In the previous studies, different theories such as Bayesian probability theory, evidence theory, and fuzzy set theory have been developed to represent and manage different aspects of uncertainty. Recently, hybrid frameworks are suggested to deal with various types of uncertainty in a single framework. Evidential networks are general frameworks for dealing explicitly with total and partial ignorance and offer powerful combination rule of contradictory evidence. In this framework, the fuzziness of linguistic variables is neglected while these variables commonly appear in the medical domain knowledge and different sources of medical information. In addition, the evidential network parameters are determined based on the experts' knowledge and no data-driven algorithm is provided to learn these parameters. In this study, a novel hybrid framework called fuzzy evidential network was suggested to manage the imprecision and epistemic uncertainty of medical prognosis and diagnosis. also, a data-driven algorithm based on the fuzzy set theory and the fuzzy maximum likelihood is provided to learn the network parameters from clinical databases. The performance of the proposed framework as various prognosis and diagnosis models, compared with well-known machine learning algorithms and the results showed its superiority. Also, an evidential method is suggested to handle the missing values and its results were compared with KNN imputation method.

PMID: 28690054 [PubMed - as supplied by publisher]