Saudi Cultural Missions Theses & Dissertations
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Item Restricted The Role of Natural Language Processing in Early Detection of Mental Health Conditions from Social Media Data(Saudi Digital Library, 2025) Alasery, Aidh; Lauria, StashaMental health disorders such as anxiety, depression, and schizophrenia are increasing rapidly and affect a significant proportion of the global population. As a result, the affected patients suffer negative consequences such as high financial costs of treatment and a poor quality of life. The reliance on traditional clinical methods to diagnose mental health problems further leads to delays in identifying the disorders among affected individuals. An emerging approach to address the delay is the adoption of artificial intelligence (AI) through natural language processing (NLP) models, which can evaluate real-time social media content to identify individuals at risk of mental health problems. The current research sought to identify how NLP techniques could be adopted for the early diagnosis and detection of mental health illnesses from social media interactions. Data was collected using the scoping review method, where 20 qualitative peer reviewed journal articles were identified and assessed. To evaluate the findings obtained in the study, thematic analysis was adopted. The generated insights indicated that using deep learning techniques, including recurrent neural networks (RNNs) and classification machine learning methods, such as decision trees (DT), facilitated the detection of mental health illnesses. Further insights revealed that techniques such as data anonymisation were effective for privacy preservation, and explainable AI (XAI) were useful in upholding the privacy of user data during the data collection phase. Additionally, various advantages of NLP models were elaborated, including accuracy, generalisability, and fairness. However, challenges such as risks of bias and breaching the privacy of user data were also identified. In future work, there is a need to investigate how the NLP models can be enhanced further by integrating more technologies, such as big data.34 0Item Restricted The current state of clinical diagnostic algorithms for mucosal oral lesions: a scoping review(2023-06) Al-Shehri, Mohammed; Madathil, Sreenath; Nicolau, BelindaBackground: Annually, oral cancer is responsible for more than 177,000 deaths worldwide. The majority of these cancers are squamous cell carcinomas that initially manifest as benign oral lesions that later undergo malignant transformation. Delay in diagnosis is a significant contributing factor to advanced-stage diagnosis and treatment of oral cancer. Early-stage diagnosis of oral lesions remains challenging for many clinicians. While some diagnostic algorithms have been proposed in the literature to assist with a clinical diagnosis and minimize delay, there is a dearth of comprehensive evidence synthesis and a discussion on their clinical and pedagogical applicability. Objectives: This review aims to systematically map out the literature for clinical diagnostic algorithms of oral lesions to identify gaps in knowledge and compile these algorithms for diagnosing oral lesions. Methods: We conducted a scoping review using the Arksey and O’Malley framework. Following this framework, a search was conducted, including studies that contained: 1) algorithms or flow diagrams that help clinicians to diagnose oral lesions in a clinical setting without additional software devices; 2) that were published in English; 3) all age groups; 4) algorithms for oral lesions of soft tissue only. We also excluded any articles where algorithms are: 1) black-box (do not provide an interpretable or human readable logic for diagnosis, i.e., machine learning-based models); 2) algorithms that required additional tests (e.g., algorithms for histopathologic assessment, laboratory tests); 3) older versions of an algorithm already included; 4) algorithms of oral lesions related to hard tissues such as bone and teeth; 5) any flow charts or algorithms intended to be used by the general public for self-screening. A list of keyword combinations was developed with the help of a librarian related to diagnostic algorithms of oral lesions, oral benign lesions, oral potentially malignant disorders, etc. The following databases were searched: Medline (Ovid), Embase (Ovid), and Web of Science, along with grey literature. Each included algorithm was reviewed by two oral pathology experts in the team to evaluate their completeness and correctness. Results: 17 clinical diagnostic algorithms from 15 peer-reviewed manuscripts and 1 online course were identified. All included papers were targeting their algorithms to be used by clinicians. Interestingly, most of the studies did not mention how the algorithms were developed, and none performed validation of these algorithms in a clinical setting. The algorithms often only covered one of two types of lesions and were found to be incomplete in the potential list of differential diagnoses. Conclusion: Very few clinical diagnostic algorithms for oral lesions are currently available in the literature. There is no standardized universal algorithm that is present for clinical application. The clinical and pedagogical utility of these algorithms needs to be evaluated.28 0