PTE -MED: AI -based Early Detection of Pulmonary Embolism
- سال انتشار: 1403
- محل انتشار: چهارمین همایش بین المللی و سیزدهمین همایش ملی بیوانفورماتیک
- کد COI اختصاصی: IBIS13_144
- زبان مقاله: انگلیسی
- تعداد مشاهده: 54
نویسندگان
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Emergency Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده
Timely diagnosis of pulmonary embolism (PE) is a significant challenge in clinical medicine, mainly due to the condition's non-specific symptoms. The PTE-MED artificial intelligence system has been developed to accurately predict the likelihood of PE by analyzing clinical data. Current research indicates that over ۵۰% of suspected PE cases undergoing CT angiography yield negative imaging results (Li et al., ۲۰۲۱). This not only results in unnecessary exposure to contrast agents and radiation but also poses serious risks for vulnerable populations, including patients with renal conditions and pregnant women. The PTE-MED system employs advanced machine-learning algorithms to analyze critical variables such as age, gender, medical history, and clinical symptoms. Imaging results from CT angiography are also incorporated as vital inputs for predictive modeling (Valente Silva et al., ۲۰۲۳). This approach enables PTE-MED to provide early predictions regarding the probability of PE, generating interpretable results for individual patients through AI-driven analytical tools. By supporting healthcare professionals in making informed decisions, PTE-MED has the potential to enhance the management of this complex and urgent medical condition. To improve accessibility for healthcare providers, a mobile application named PTE-MED is being developed. This application will allow physicians and specialists to input patient symptoms and medical history and subsequently receive predictive insights about the likelihood of PE. Preliminary modeling results demonstrate that the CatBoost model achieves an Area Under the Curve (AUC) of ۰.۷۶۸, an accuracy of ۷۱.۱%, a precision of ۷۴.۰%, a recall of ۷۱.۰%, and an F۱ score of ۷۲.۰%. In conclusion, this system assists healthcare providers in making better-informed treatment decisions by increasing the accuracy of predictions, addressing a key concern for emergency physicians, surgeons, cardiologists, infectious disease specialists, and obstetricians. The PTE-MED artificial intelligence system not only improves diagnostic accuracy but also potentially reduces the financial and temporal burdens associated with unnecessary diagnostic procedures. By implementing this system, healthcare providers can mitigate the risks associated with invasive diagnostic methods and contribute to enhanced public health outcomes.کلیدواژه ها
Pulmonary embolism, artificial intelligence, machine learning, diagnosis, medical informatics, emergency medicineاطلاعات بیشتر در مورد COI
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