John Reilly

Rajib Rana, Niall Higgins, Kazi N. Haque et al.

Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's problems. Auditing clinical documentation on an electronic health record (EHR) is challenging as it requires resource-intensive manual efforts to identify keywords in relevant sections of specific forms. Furthermore, clinicians and healthcare professionals often do not use keywords; their clinical language can vary greatly and may contain various jargon and acronyms. Also, the relevant information may be recorded elsewhere. This study describes how we developed advanced Natural Language Processing (NLP) algorithms, a branch of Artificial Intelligence (AI), to analyse EHR data automatically. Method: Advanced Optical Character Recognition techniques were used to process unstructured data sets, such as portable document format (pdf) files. Free text data was cleaned and pre-processed using Normalisation of Free Text techniques. We developed algorithms and tools to unify the free text. Finally, the formulation was checked for the presence of each concept based on similarity using NLP-powered semantic matching techniques. Results: We extracted information indicative of formulation and assessed it to cover the relevant concepts. This was achieved using a Weighted Score to obtain a Confidence Level. Conclusion: The rigour to which formulation is completed is crucial to effectively using EHRs, ensuring correct and timely identification, engagement and interventions that may potentially avoid many suicide attempts and suicides.

Rajib Rana, Margee Hume, John Reilly et al.

Opportunistic affect sensing offers unprecedented potential for capturing spontaneous affect ubiquitously, obviating biases inherent in the laboratory setting. Facial expression and voice are two major affective displays, however most affect sensing systems on smartphone avoid them due to extensive power requirement. Encouragingly, due to the recent advent of low-power DSP (Digital Signal Processing) co-processor and GPU (Graphics Processing Unit) technology, audio and video sensing are becoming more feasible. To properly evaluate opportunistically captured facial expression and voice, contextual information about the dynamic audio-visual stimuli needs to be inferred. This paper discusses recent advances of affect sensing on the smartphone and identifies the key barriers and potential solutions of implementing opportunistic and context-aware affect sensing on smartphone platforms.

Rajib Rana, John Reilly, Raja Jurdak et al.

Due to increasing sensing capacity, smartphones offer unprecedented opportunity to monitor human health. Affect sensing is one such essential monitoring that can be achieved on smartphones. Information about affect can be useful for many modern applications. In particular, it can be potentially used for understanding cognitive decline in aging population. In this paper we present an overview of the existing literature that offer affect sensing on smartphone platform. Most importantly, we present the challenges that need to be addressed to make affect sensing on smartphone a reality.