Independent factors for prediction of poor outcomes in patients with febrile neutropenia.

BACKGROUND: Febrile neutropenia (FN) is a life-threatening condition that requires urgent management in the emergency department (ED). Recent progress in the treatment of neutropenic fever has underscored the importance of risk stratification. In this study, we aimed to determine independent factors for prediction of poor outcomes in patients with FN. MATERIAL AND METHODS: We retrospectively evaluated 200 chemotherapy-induced febrile neutropenic patients who visited the ED. Upon arrival at the ED, clinical data, including sex, age, vital signs, underlying systemic diseases, laboratory test results, estimated GFR, blood cultures, CRP, radiologic examinations, and Multinational Association of Supportive Care in Cancer (MASCC) score of all febrile neutropenic patients were obtained. Outcomes were categorized as "poor" if serious complications during hospitalization, including death, occurred. RESULTS: The platelet count <50 000 cells/mm3 (OR 3.90, 95% CI 1.62-9.43), pulmonary infiltration (OR 3.45, 95% CI 1.48-8.07), hypoproteinemia <6 g/dl (OR 3.30, 95% CI 1.27-8.56), respiratory rate >24/min (OR 8.75, 95% CI 2.18-35.13), and MASCC score <21 (OR 9.20, 95% CI 3.98-21.26) were determined as independent risk factors for the prediction of death. The platelet count <50 000 cells/mm3 (OR 3.93, 95% CI 1.42-10.92), serum CRP >50 mg/dl (OR 3.80, 95% CI 1.68-8.61), hypoproteinemia (OR 7.81, 95% CI 3.43-17.78), eGFR ≤90 ML/min/1.73 m2 (OR 3.06, 95% CI 1.13-8.26), and MASCC score <21 (OR 3.45, 95% CI 1.53-7.79) were determined as independent risk factors for the prediction of poor clinical outcomes of FN patients. Platelet count, protein level, respiratory rate, pulmonary infiltration, CRP, MASCC score, and eGFR were shown to have a significant association with outcome. CONCLUSIONS: The results of our study may help emergency medicine physicians to prevent serious complications with proper use of simple independent risk factors besides MASCC score.

An AI-based Decision Support System for Predicting Mental Health Disorders.

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.