List of Titles

Machine learning in mental health: a scoping review of methods and applications

- Shatte, Adrian, Hutchinson, Delyse, Teague, Samantha

Authors: Shatte, Adrian , Hutchinson, Delyse , Teague, Samantha
Date: 2019
Type: Text , Journal article
Relation: Psychological Medicine Vol. 49, no. 9 (2019), p. 1426-1448
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Description: This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health, and (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Key design considerations using a cohort stepped-wedge cluster randomised trial in evaluating community-based interventions : lessons learnt from an Australian domiciliary aged care intervention evaluation

- Mohebbi,Mohammadreza, Sanagou, Masoumeh, Ottmann, Goetz

Authors: Mohebbi,Mohammadreza , Sanagou, Masoumeh , Ottmann, Goetz
Date: 2017
Type: Text , Journal article
Relation: International journal of statistics in medical research Vol. 6, no. 3 (2017), p. 123-133
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Description: The ‘stepped-wedge cluster randomised trial’ (SW-CRT) harbours promise when for ethical or practical reasons the recruitment of a control group is not possible or when a staggered implementation of an intervention is required. Yet SW-CRT designs can create considerable challenges in terms of methodological integration, implementation, and analysis. While cross-sectional methods in participants recruitment of the SW-CRT have been discussed in the literature the cohort method is a novel feature that has not been considered yet. This paper provides a succinct overview of the methodological, analytical, and practical aspects of cohort SW-CRTs.We discuss five issues that are of special relevance to SW-CRTs. First, issues relating to the design, secondly size of clusters and sample size; thirdly, dealing with missing data in the fourth place analysis; and finally, the advantages and disadvantages of SW-CRTs are considered. An Australian study employing a cohort SW-CRT to evaluate a domiciliary aged care intervention is used as case study. The paper concludes that the main advantage of the cohort SW-CRT is that the intervention rolls out to all participants. There are concerns about missing a whole cluster, and difficulty of completing clusters in a given time frame due to involvement frail older people. Cohort SW-CRT designs can be successfully used within public health and health promotion context. However, careful planning is required to accommodate methodological, analytical, and practical challenges.

Insider research, validity issues, and the OHS professional: one person's journey

- Teusner, Annabel

Authors: Teusner, Annabel
Date: 2016
Type: Text , Journal article
Relation: International journal of social research methodology Vol. 19, no. 1 (2016), p. 85-96
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Description: Validity in the context of qualitative research may take on many different definitions depending on the philosophical perspective. When a researcher conducts research within their place of employment, this will also influence the approach and interpretation. Insider research has been under scrutiny for the very fact that the insider is an actor within the setting. This paper explores my journey, conducting insider research and working as an occupational health and safety practitioner, by presenting an example of the methods used, including reflectivity and reflexivity, to address validity issues which may arise prior, during and post data collection.

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The Children’s Picture Books Lexicon (CPB-Lex) : a large-scale lexical database from children’s picture books

- Green, Clarence, Keogh, Kathleen, Sun, He, O’Brien, Beth

Authors: Green, Clarence , Keogh, Kathleen , Sun, He , O’Brien, Beth
Date: 2024
Type: Text , Journal article
Relation: Behavior Research Methods Vol. 56, no. 5 (2024), p. 4504-4521
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Description: This article presents cpb-lex, a large-scale database of lexical statistics derived from children’s picture books (age range 0–8 years). Such a database is essential for research in psychology, education and computational modelling, where rich details on the vocabulary of early print exposure are required. Cpb-lex was built through an innovative method of computationally extracting lexical information from automatic speech-to-text captions and subtitle tracks generated from social media channels dedicated to reading picture books aloud. It consists of approximately 25,585 types (wordforms) and their frequency norms (raw and Zipf-transformed), a lexicon of bigrams (two-word sequences and their transitional probabilities) and a document-term matrix (which shows the importance of each word in the corpus in each book). Several immediate contributions of cpb-lex to behavioural science research are reported, including that the new cpb-lex frequency norms strongly predict age of acquisition and outperform comparable child-input lexical databases. The database allows researchers and practitioners to extract lexical statistics for high-frequency words which can be used to develop word lists. The paper concludes with an investigation of how cpb-lex can be used to extend recent modelling research on the lexical diversity children receive from picture books in addition to child-directed speech. Our model shows that the vocabulary input from a relatively small number of picture books can dramatically enrich vocabulary exposure from child-directed speech and potentially assist children with vocabulary input deficits. The database is freely available from the Open Science Framework repository: https://tinyurl.com/4este73c. © Crown 2023.

A comprehensive survey of anomaly detection algorithms

- Samariya, Durgesh, Thakkar, Amit

Authors: Samariya, Durgesh , Thakkar, Amit
Date: 2023
Type: Text , Journal article
Relation: Annals of data science Vol. 10, no. 3 (2023), p. 829-850
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Description: Anomaly or outlier detection is consider as one of the vital application of data mining, which deals with anomalies or outliers. Anomalies are considered as data points that are dramatically different from the rest of the data points. In this survey, we comprehensively present anomaly detection algorithms in an organized manner. We begin this survey with the definition of anomaly, then provide essential elements of anomaly detection, such as different types of anomaly, different application domains, and evaluation measures. Such anomaly detection algorithms are categorized in seven categories based on their working mechanisms, which includes total of 52 algorithms. The categories are anomaly detection algorithms based on statistics, density, distance, clustering, isolation, ensemble and subspace. For each category, we provide the time complexity of each algorithm and their general advantages and disadvantages. In the end, we compared all discussed anomaly detection algorithms in detail.

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