Interoperability and the Future of Human Services

Identifying child abuse through text mining and machine learning

“…This is becoming a reality in today's environment, where a substantial amount of data from multiple sources is available on children and their families ( Gillingham, 2015 ). Building upon the view of Schoech (2010) , in an ideal scenario the gathered information should not be modified by the use of an expert system, while at the same time allowing potential modification of the existing patterns of information flow to be more efficient. Moreover, for child welfare workers this would mean good interoperability in a top-down model, where the expert system could assist with all manner of tasks, ranging from routine/inconsequential (e.g.…”

Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning

“…More recently, Schwartz, Kaufman and Schwartz (2004) used a ‘backpropagation’ algorithm with 1,767 cases from the USA's Third National Incidence Study of Child Abuse and Neglect to develop an artificial neural network that could predict, with 90 per cent accuracy, which children would meet the criteria set for a substantiation of abuse. Schoech (2010) describes how technological advances which connect databases from different agencies, allowing the easy exchange and collation of information about people, can ‘accumulate intelligence with use; for example, those using data mining, decision modelling, organizational intelligence strategies, wiki knowledge repositories, etc.’ (p. 8). In England, in response to media reports about the failure of a child protection service, it has been claimed that ‘understanding the patterns of what constitutes a child at risk and the many contexts and circumstances is where big data analytics comes in to its own’ ( Solutionpath, 2014 ).…”