Early detection of depression using a conversational AI bot: a non-clinical trial

Background Artificial intelligence (AI) has gained momentum in behavioural health interventions in recent years. However, a limited number of studies use or apply such methodologies in the early detection of depression. A large population needing psychological—intervention is left unidentified due to barriers such as cost, location, stigma and a global shortage of health workers. Therefore, it is essential to develop a mass screening integrative approach that can identify people with depression at its early stage to avoid a potential crisis. Objectives This study aims to understand the feasibility and efficacy of using AI-enabled chatbots in the early detection of depression. Methods We use Dialogflow as a conversation interface to build a Depression Analysisn (DEPRA) chatbot. A structured and authoritative early detection depression interview guide, which contains 27 questions combining the structured interview guide for the Hamilton Depression Scale (SIGH-D) and the inventory of depressive symptomatology (IDS-C), underpins the design of the conversation flow. To attain better accuracy and a wide variety of responses, we train Dialogflow with the utterances collected from a focus group of 10 people. The occupation of the focus group members included academics and HDR candidates who are conscious, vigilant and have a clear understanding of the questions. In addition, DEPRA is integrated with a social media platform to provide practical access to all the participants. For the non-clinical trial, we recruited 50 participants aged between 18 and 80 from across Australia. To evaluate the practicability and performance of DEPRA, we also asked participants to submit a user satisfaction survey at the end of the conversation. Results A sample of 50 participants, with an average age of 34.7 years, completed this non-clinical trial. More than half of the participants (54%) are male and the major ethnicities are Asian (63%), Middle Eastern (25%), and others 12%. The first group comprises professional academic staff and HDR candidates, the second and third groups comprise relatives, friends, and volunteers who were recruited via social media promotions. DEPRA uses two scientific scoring systems, QIDS-SR and IDS-SR to verify the results of early depression detection. As the results indicate, both scoring systems return a similar outcome with slight variations for different depression levels. According to IDS-SR, 30% of participants were healthy, 14% mild, 22% moderate, 14% severe, and 20% very severe. QIDS-SR suggests 32% were healthy, 18% mild, 10% moderate, 18% severe, and 22% very severe. Furthermore, the overall satisfaction rate of using DEPRA was 79% indicating that the participants had a high rate of user satisfaction and engagement. Conclusion DEPRA shows promises as a feasible option for developing a mass screening integrated approach for early detection of depression. Although the chatbot is not intended to replace the functionality of mental health professionals, it does show promise as a means of assisting with automation and concealed communication with verified scoring systems

Fractal feature selection model for enhancing high-dimensional biological problems

The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%

A low complexity high capacity ecg signal watermark for wearable sensor-net health monitoring system,”

Abstrac

Resilient to shared spectrum noise scheme for protecting cognitive radio smart grid readings - BCH based steganographic approach

Cognitive Radio smart grids have recently attracted attention because of high efficiency and throughput performance. They transmit (1) periodically collected readings (e.g. monitoring) and (2) highly sensitive data (e.g. geometric location). However, robustness, efficiency and security of the transmitted data compose an unaddressed unique challenge due to CR shared spectrum possible noise. This paper proposes the first novel hybrid model that combines advanced steganographic algorithms with error detection and correction techniques (BCH syndrome codes) in the CR smart meter context. This will allow us to (a) detect and recover any loss from the hidden confidential information without privacy disclosure, and (b) remedy the received normal readings by using the corrected version of the secret hidden data. To randomize hiding and minimize the distortion, 3D wavelet is used to decompose normal readings into a set of coefficients. To strengthen the security, a key is utilized to generate a 3D randomly selected order used in the hiding process. To accurately measure the detection and recovery capabilities, random noise levels are applied to the transmitted readings. The recovered sensitive information and stego readings are extensively measured using BER, PRD and RMS. It is obvious from the experiments that our technique has robust recovery capabilities (i.e. BER = 0, PRD < 1% and RMS < 0.01%)