Busenur Kizilaslan

• Mortier, Philippe; Amigo, Franco; Bhargav, Madhav; Conde, Susana; Ferrer, Montse & Flygare, Oskar [Show all 21 contributors for this article] (2024). Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project. BMC Psychiatry. 24(1). doi: 10.1186/s12888-024-05659-6. Full text in Research Archive Show summary

  Background Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. Methods PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS’ practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. Discussion Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

• Kizilaslan, Busenur; Egrioglu, Erol & Evren, Atif Ahmet (2020). Intuitionistic fuzzy ridge regression functions. Communications in Statistics - Simulation and Computation. ISSN 0361-0918. 49(3), p. 699–708. doi: 10.1080/03610918.2019.1626887. Full text in Research Archive
• Inan, Deniz; Egrioglu, Erol; Kizilaslan, Busenur; Yigit, Oykum Esra & Tez, Mujgan (2017). Particle swarm optimization based Liu-type estimator. Communications in Statistics - Theory and Methods. ISSN 0361-0926. 46(22), p. 11358–11369. doi: 10.1080/03610926.2016.1267759. Full text in Research Archive
• Kizilaslan, Busenur; Egrioglu, Erol & Asikgil, Baris (2016). A new hybrid method for time series forecasting: AR–ANFIS. Neural Computing & Applications. ISSN 0941-0643. 29, p. 749–760. doi: 10.1007/s00521-016-2475-5. Full text in Research Archive

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• Kizilaslan, Busenur & Qin, Ping (2025). Beyond the Surface: Understanding Self-Harm Subgroups through Latent Class Analysis of Hospital-treated Patients and Their Prospective Health Outcomes. Full text in Research Archive

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