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Objectives: As electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N-GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria., Methods: We designed and implemented 3 automatic methods: a knowledge-driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a machine learning method using a neural network; and a hybrid approach combining the first 2 methods with a neural network., Results: The results on an unseen evaluation set of 216 psychiatric evaluation records showed a performance of 80.1% for the rule-based method, 73.3% for the machine-learning approach, and 72.0% for the hybrid one., Conclusions: Although more work is needed to improve the accuracy, the results are encouraging and indicate that automated text mining methods can be used to classify mental health symptom severity from free text psychiatric notes to support epidemiological and clinical research., (© 2017 The Authors International Journal of Methods in Psychiatric Research Published by John Wiley & Sons Ltd.)

Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hypertension, hyperlipidemia, diabetes, smoking, and family history of premature CAD. This paper describes and evaluates a methodology to extract mentions of such risk factors from diabetic clinical notes, which was a task of the i2b2/UTHealth 2014 Challenge in Natural Language Processing for Clinical Data. The methodology is knowledge-driven and the system implements local lexicalized rules (based on syntactical patterns observed in notes) combined with manually constructed dictionaries that characterize the domain. A part of the task was also to detect the time interval in which the risk factors were present in a patient. The system was applied to an evaluation set of 514 unseen notes and achieved a micro-average F-score of 88% (with 86% precision and 90% recall). While the identification of CAD family history, medication and some of the related disease factors (e.g. hypertension, diabetes, hyperlipidemia) showed quite good results, the identification of CAD-specific indicators proved to be more challenging (F-score of 74%). Overall, the results are encouraging and suggested that automated text mining methods can be used to process clinical notes to identify risk factors and monitor progression of heart disease on a large-scale, providing necessary data for clinical and epidemiological studies., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research., Methods: A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar., Results: A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports., (Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)

The epidemiological question of concern here is “can young children at risk of obesity be identified from their early growth records?” Pilot work using logistic regression to predict overweight and obese children demonstrated relatively limited success. Hence we investigate the incorporation of non-linear interactions to help improve accuracy of prediction; by comparing the result of logistic regression with those of six mature data mining techniques. The contributions of this paper are as follows: a) a comparison of logistic regression with six data mining techniques: specifically, for the prediction of overweight and obese children at 3 years using data recorded at birth, 6 weeks, 8 months and 2 years respectively; b) improved accuracy of prediction: prediction at 8 months accuracy is improved very slightly, in this case by using neural networks, whereas for prediction at 2 years obtained accuracy is improved by over 10%, in this case by using Bayesian methods. It has also been shown that incorporation of non-linear interactions could be important in epidemiological prediction, and that data mining techniques are becoming sufficiently well established to offer the medical research community a valid alternative to logistic regression. [ABSTRACT FROM AUTHOR]

Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While manual literature curation is expensive and labour intensive, the development of semi-automated text mining support is hindered by unavailability of training data. There have been no studies on how existing data sources (e.g. TF-related data from the MeSH thesaurus and GO ontology) or potentially noisy example data (e.g. protein-protein interaction, PPI) could be used to provide training data for identification of TF-contexts in literature. Results: In this paper we describe a text-classification system designed to automatically recognise contexts related to transcription factors in literature. A learning model is based on a set of biological features (e.g. protein and gene names, interaction words, other biological terms) that are deemed relevant for the task. We have exploited background knowledge from existing biological resources (MeSH and GO) to engineer such features. Weak and noisy training datasets have been collected from descriptions of TF-related concepts in MeSH and GO, PPI data and data representing non-protein-function descriptions. Three machine-learning methods are investigated, along with a vote-based merging of individual approaches and/or different training datasets. The system achieved highly encouraging results, with most classifiers achieving an F-measure above 90%. Conclusions: The experimental results have shown that the proposed model can be used for identification of TF-related contexts (i.e. sentences) with high accuracy, with a significantly reduced set of features when compared to traditional bag-of-words approach. The results of considering existing PPI data suggest that there is not as high similarity between TF and PPI contexts as we have expected. We have also shown that existing knowledge sources are useful both for feature engineering and for obtaining noisy positive training data. [ABSTRACT FROM AUTHOR]

Modern hearing instruments contain logging technology to record data, such as the acoustic environments in which the device is being used and how the signal processing is consequently operating. Combined with patient data, such as the audiogram, this information gives a more comprehensive picture of the user and their relationship with the aid. Here, a relatively large, anonymized dataset (>300,000 devices, >150,000 wearers) from a hearing-aid manufacturer was data mined for connections between subsets of the logged varieties of data. Apart from replicating links that have previously been reported in labor-intensive studies, a link between device style (in-the-ear/behind-the-ear) and the sound levels of encountered environments was demonstrated, suggesting that some device types are more successful from a lifestyle perspective. Furthermore, the data also suggested links between the audiogram and the sound environments in which the aid was operated. Modeling the expected link between the environment and the microphone directionality settings revealed patterns of either abnormal fitting or where the aid was not operating correctly—factors that may indicate a failed fitting. Given the necessarily redacted nature of the dataset, the reported findings represent a proof-of-concept of the use of relatively large-scale data mining to guide and assess hearing-aid fitting procedures for possible benefits to the clinician, manufacturer, and patient. [ABSTRACT FROM AUTHOR]