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A research area of NLP is known as keyphrases extraction, which aims to identify words and expressions in a text that comprehensively represent the content of the text itself. In this study, we introduce a new approach called KRAKEN (Keyphrease extRAction maKing use of EmbeddiNgs). Our method takes advantage of widely used NLP techniques to extract keyphrases from a text in an unsupervised manner and we compare the results with well-known benchmark datasets in the literature. The main contribution of this work is developing a novel approach for keyphrase extraction. Both natural language text preprocessing techniques and distributional semantics techniques, such as word embeddings, are used to obtain a vector representation of the texts that maintains their semantic meaning. Through KRAKEN, we propose and design a new method that exploits word embedding for identifying keyphrases, considering the relationship among words in the text. To evaluate KRAKEN, we employ benchmark datasets and compare our approach with state-of-the-art methods. Another contribution of this work is the introduction of a metric to rank the identified keyphrases, considering the relatedness of both the words within the phrases and all the extracted phrases from the same text.

It is currently difficult to successfully choose the correct type of antidepressant for individual patients. To discover patterns in patient characteristics, treatment choices and outcomes, we performed retrospective Bayesian network analysis combined with natural language processing (NLP). This study was conducted at two mental healthcare facilities in the Netherlands. Adult patients admitted and treated with antidepressants between 2014 and 2020 were included. Outcome measures were antidepressant continuation, prescription duration and four treatment outcome topics: core complaints, social functioning, general well-being and patient experience, extracted through NLP of clinical notes. Combined with patient and treatment characteristics, Bayesian networks were constructed at both facilities and compared. Antidepressant choices were continued in 66% and 89% of antidepressant trajectories. Score-based network analysis revealed 28 dependencies between treatment choices, patient characteristics and outcomes. Treatment outcomes and prescription duration were tightly intertwined and interacted with antipsychotics and benzodiazepine co-medication. Tricyclic antidepressant prescription and depressive disorder were important predictors for antidepressant continuation. We show a feasible way of pattern discovery in psychiatry data, through combining network analysis with NLP. Further research should explore the found patterns in patient characteristics, treatment choices and outcomes prospectively, and the possibility of translating these into a tool for clinical decision support.

Background: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders (“transdiagnostic”). Methods: A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. Results: Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62—0.79) and

The need for improved energy security in Nigeria cannot be over-emphasized. Currently, energy security is rather poor, while access to energy is fundamental to socio-economic development and poverty alleviation. Renewable energy could potentially contribute to resolving this because renewable sources such as solar radiation are more available and sustainable, and can be set up in small generation units, meaning that it is suitable for community management and ownership. In theory, a community energy approach could well apply. In this paper, the main research question is: In what ways can community energy initiatives contribute to increasing the use of renewable energy sources and improving energy security in Nigeria according to selected stakeholders and households? A mixed methods research approach was used to answer this question, with stakeholder interviews and survey data from 124 residents in two case studies of selected housing estates in Lagos. The results show that 58% of the households and most of the stakeholders express support for community renewable energy as a viable approach for increasing energy access and greening energy supply. The present study shows that there is a need to raise awareness and support projects for effective and supportive renewable energy policy to encourage local renewable energy community formation., Organisation & Governance

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Adaptation is an essential part of climate change policy. In areas where impacts are likely to be severe, migration is considered to be an adaptation option. In Bangladesh coastal areas migration due to climate change is contingent on people’s freedom of choice at individual and household level. Following Amartya Sen’s capability approach, we argue that there should be a line drawn between migrations by free choice versus forced migration. Sen’s capability approach focuses on the importance of people’s freedom of choice to act, and the ability to achieve what they consider valuable in their life. In this paper, we use an extensive empirical work engaging 22 focus groups discussions (8–12 individuals in each group) and 14 Key Informants Interviews in South-West Bangladesh to elicit how freedom of choice changes with the economic class and social status of an individual. Using these data we apply Sen’s capability approach to understand the role of the freedom of choice when considering migration as an adaptation option. We argue that the capability approach is essential in revealing a thin border between migration as a (planned) adaptation option and forced migration.

Evidence from academic studies that analyze social and institutional factors that influence success with community energy projects is scarce. To address this, we pose the question: What are the social, organizational, and governance factors that explain success with ‘local low-carbon energy initiatives’ (LLCEIs)? To answer this question, claims were first established pertaining to three groups of social factors: i.e. (i) those related to the LLCEI itself; (ii) factors related to the interaction between an LLCEI and the local community; and (iii) the presence of supportive governance settings and linkages with local government and intermediaries. These were analyzed using a variable-oriented cross-case design involving fourteen LLCEIs in the Dutch Province of Fryslân. Results show that there is a difference in the sets of factors that positively correlate with various measures of LLCEI success. Factors related to the LLCEI (i.e. internal organizational factors) correlate with collective energy projects and to a lesser extent with individual projects realized. Items related to interactions of the LLCEI with the local community primarily correlate with the customer base and to a lesser extent with individual projects realized. Finally, items related to the governance setting correlate most strongly with individual projects realized., Organisation and Governance

Design: This is a study to evaluate machine learning feature extraction approaches on an established public dataset of retinal images in the diagnosis of diabetic retinopathy (DR). Purpose: Convolutional Neural Networks (CNNs) have been demonstrated to achieve state-of-the-art results on many complex computer vision tasks including the automated diagnosis of diseases such as DR. CNNs are a powerful development of machine learning, which not only produce excellent classification results based on image features but also determine the relevant features automatically. However, the current inability to demonstrate what these features are has led to CNN-based approaches being considered “black box” methods, which are difficult to accept. In this work, we demonstrate two methods for identifying the learned features, applying this to the diagnosing DR from fundus images. Methods: Our aim is to demonstrate the successful identification of the relevant features determined during the classification process. We build two identification methods into the established DenseNet architecture and evaluate for DR diagnosis. We use a large dataset of over 88k retinal fundus images (http://www.eyepacs.com/), which are classified in terms of the presence and severity of DR, i.e. [no, mild, moderate, severe, proliferative]. We train on a random subset of 78,076 (88%) images, reserving 10,626 for testing, extracting feature maps for each test image, which identify the details that have led most strongly to the CNN prediction. Results: Using this approach, we have been able to determine on an image-basis the regions identified within the CNN as being relevant. E.g. for severe DR, haemorrhages, microaneurysms and cotton wool spots have been identified as contributing to the diagnosis; while laser spots, neovascularisation and venous reduplication have been automatically identified as corresponding to proliferative DR. Conclusions: This feature extraction has great potential for providing a me