Depression and anxiety detection through the closed-loop method using DASS-21

The change of information and communication technology has brought many changes in daily life. The way humans interacting is changing. It is possible to express each form of communication directly and instantly. Social media has contributed data in size, diversity and capacity and quality. Based on it, the idea was to see and measure the tendency of depression and anxiety through social media using the Closed-Loop method using Facebook text mining posts. Through the stages of pre-processing including text extraction using the Naïve Bayes machine learning model for text classification, the early signs of depression and anxiety are measured using DASS-21 parameter. In total, 22,934 Facebook posts were contributed as training and learning data collected from July 2017 until July 2018. As a results, analysis and mapping of social demographics of users that are usually as a trigger of depression, and anxiety, such as grief, illness, household affairs, children education and others are available

Disrupting the Narrative: Diving Deeper into Section 230 Political Discourse

Online spaces have undoubtedly played a significant role in facilitating discourse and the exchange of information. With this increased discourse, however, digital platforms have also seen a rise in harmful or problematic content shared online––including health misinformation, hate speech, and child sex abuse material, among others. Many commentators have put the blame for this trend on Section 230, arguing that Section 230 has enabled the spread of harmful content and suggesting that Section 230 ought to be amended or replaced. This Essay, by contrast, argues that the current narrative about Section 230 gets it wrong. In reality, Section 230 has allowed digital platforms to take crucial steps toward combating dangerous content online. Removing Section 230 protections would likely reduce the level of content moderation that is available on online platforms—the opposite of what the bills discussed seek to promote—and would in fact make the problems of content moderation worse

Algorithmisch basierte Entscheidungsunterstützungssysteme für die deutsche Kinder- und Jugendhilfe? Messages from Research

Der Artikel von Phillip Gillingham und Timo Ackermann thematisiert die Einführung von datenbasierten, elektronischen Entscheidungsunterstützungssystemen in der internationalen Kinder- und Jugendhilfelandschaft. Sie weisen auf gravierende konzeptionelle Probleme dieser Systeme hin, die dazu führen, dass sie weder zuverlässig Vorhersagen treffen, noch ethisch vertretbar sind. Vor diesem Hintergrund stellen sie die Frage, ob die zu erwartenden Kosten für die Entwicklung von entsprechenden Programmen für die deutsche Kinder- und Jugendhilfe nicht sinnvoller in eine weitere Professionalisierung der dort tätigen Fachkräfte und Organisationen investiert werden sollte

Towards AI-governance in psychosocial care: A systematic literature review analysis

With increased digitalization and e-government services, Artificial Intelligence (AI) gained momentum. This paper focuses on AI-governance in Child Social Care field, exploring how aspects of individual, family/community factors are embedded in organizational level, especially when dealing with children resilience and wellbeing. A three-level based review has been conducted. In the first part we explored the interlink between individual factors associated to either resilience or wellbeing are connected to community and governance level where a new conceptual model is provided. In the second phase, we conducted an in-depth systematic literature review using PRISMA review protocol where new categorizations of identified literature with respect to individual, family and community levels in child social care field were suggested, while in the third phase, a review of relevant AI-initiatives in Europe and USA was performed. Finally, a comprehensive discussion of the literature review outcomes was carried out and a new updated conceptual model was provided.© 2023 The Author(s). Published by Elsevier Ltd on behalf of Prof JinHyo Joseph Yun. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning

Background State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considerable resources are required to read and code these text data. Data science and text mining offer potentially efficient and cost-effective strategies for maximizing the value of these data. Objective The current study tests the feasibility of using text mining for extracting information from unstructured text to better understand substance-related problems among families investigated for abuse or neglect. Method A state child welfare agency provided written summaries from investigations of child abuse and neglect. Expert human reviewers coded 2956 investigation summaries based on whether the caseworker observed a substance-related problem. These coded documents were used to develop, train, and validate computer models that could perform the coding on an automated basis. Results A set of computer models achieved greater than 90% accuracy when judged against expert human reviewers. Fleiss kappa estimates among computer models and expert human reviewers exceeded .80, indicating that expert human reviewer ratings are exchangeable with the computer models. Conclusion These results provide compelling evidence that text mining procedures can be a cost-effective and efficient solution for extracting meaningful insights from unstructured text data. Additional research is necessary to understand how to extract the actionable insights from these under-utilized stores of data in child welfare

The strategic impacts of Intelligent Automation for knowledge and service work : An interdisciplinary review

We would like to thank Professor Jarvenpaa and the review team for all the constructive comments and suggestions that were most helpful in revising the paper and in offering a stronger contribution. We would also like to thank Professor Guy Fitzgerald for his constructive comments on earlier versions of the paper. This study was funded by the Chartered Institute of Professional Development (CIPD). The views expressed are those of the authors and not necessarily those of the CIPD.Peer reviewedPublisher PD

Mobile Solution for data mining and decision support: Weight monitoring and early prediction of cardiac arrest.

The daily accumulation of data through various means has led to the popularity of data mining in recent times. Through the use of the data mining techniques, data that are collected are used for problem-solving and other purposes. In data mining, patterns and trends of large datasets are studied using computer-based techniques. This thesis is using an Android mobile application as a data sampling tool for data mining purposes. Using this application, a predictive machine learning model, was developed to predict the probability of occurrence of cardiac of arrest in users of a mobile app over a ten- year span. The designed mobile application also functions as a support tool for weight management and fitness. The mobile application was connected to a real-time database and a machine learning tool using a Python program to perform prediction. The machine learning was based on Logistic Regression that is one of the predominant models used in the healthcare sector for predictions. The system used the user’s age, height, weight, activity level and diabetes status to predict the user’s chances of getting a Sudden Cardiac Arrest (SCA) over a ten-year period. A detailed account of the implementation processes and principles are discussed throughout this work.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Fragile, please handle with care:Understanding and supporting professionals' response to suspicions of child abuse and neglect

This research project focuses on: * Child healthcare professionals'​ adherence to national guidelines on child abuse and neglect and reasons for (non)adherence. * Child healthcare professional's adherence to consultation of an in-house expert on child abuse and neglect. * Professionals' preferences and experiences with regard to requesting information from other child-serving agencies in case of suspected child maltreatment. * The development and evaluation of a digital tool to support professionals responding according to the guidelines on child abuse and neglect in preventive child health care.

Identification of Factors Contributing to Traffic Crashes by Analysis of Text Narratives

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature of text data within narratives has challenged exploration of crash narratives. In response, this dissertation aims to develop an analysis framework and methods to enable the extraction of insights from crash narratives and thus improve our level of understanding of traffic crashes to a new level. The methodological development of this dissertation is split into three objectives. The first objective is to devise an approach for extraction of severity contributing insights from crash narratives by investigating interpretable machine learning and text mining techniques. The second objective is to enable an enhanced identification of crash severity contributors in the form of meaningful phrases by integrating recent advancements in Natural Language Processing (NLP). The third objective is to develop an approach for semantic search of information of interest in crash narratives. The obtained results indicate that the developed approaches enable the extraction of valuable insights from crash narratives to 1) uncover factors that quantitative may not reveal, 2) confirm results from classic statistical analysis on crash data, and 3) fix inconsistencies in quantitative data. The outcomes of this dissertation add substantial value to traffic safety, as the developed approaches allow analysts to exploit the rich information in crash narratives for a more comprehensive and accurate diagnosis of traffic crashes

Identifying child abuse through text mining and machine learning

In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on children is unstructured, found in the form of free text notes. We explore whether these consultation data contain meaningful patterns to determine abuse. Then we train machine learning models on cases of abuse as determined by over 500 child specialists from a municipality in The Netherlands. The resulting model achieves a high score in classifying cases of possible abuse. We methodologically evaluate and compare the performance of the classifiers. We then describe our implementation of the decision support API at a municipality in the Netherlands