Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus.

In an era of increasing need for precision medicine, machine learning has shown promise in making accurate acute myocardial infarction outcome predictions. The accurate assessment of high-risk patients is a crucial component of clinical practice. Type 2 diabetes mellitus (T2DM) complicates ST-segment elevation myocardial infarction (STEMI), and currently, there is no practical method for predicting or monitoring patient prognosis. The objective of the study was to compare the ability of machine learning models to predict in-hospital mortality among STEMI patients with T2DM. We compared six machine learning models, including random forest (RF), CatBoost classifier (CatBoost), naive Bayes (NB), extreme gradient boosting (XGBoost), gradient boosting classifier (GBC), and logistic regression (LR), with the Global Registry of Acute Coronary Events (GRACE) risk score. From January 2016 to January 2020, we enrolled patients aged > 18 years with STEMI and T2DM at the Affiliated Hospital of Zunyi Medical University. Overall, 438 patients were enrolled in the study [median age, 62 years; male, 312 (71%); death, 42 (9.5%]). All patients underwent emergency percutaneous coronary intervention (PCI), and 306 patients with STEMI who underwent PCI were enrolled as the training cohort. Six machine learning algorithms were used to establish the best-fit risk model. An additional 132 patients were recruited as a test cohort to validate the model. The ability of the GRACE score and six algorithm models to predict in-hospital mortality was evaluated. Seven models, including the GRACE risk model, showed an area under the curve (AUC) between 0.73 and 0.91. Among all models, with an accuracy of 0.93, AUC of 0.92, precision of 0.79, and F1 value of 0.57, the CatBoost model demonstrated the best predictive performance. A machine learning algorithm, such as the CatBoost model, may prove clinically beneficial and assist clinicians in tailoring precise management of STEMI patients and predicting in-hospital mortality complicated by T2DM.

Ethnobotany of wild edible plants in multiethnic areas of the Gansu-Ningxia-Inner Mongolia junction zone.

INTRODUCTION: In recent years, research on wild edible plant resources has become increasingly popular. The Hassan Nature Reserve is a multiethnic area mainly composed of people belonging to the Han, Hui, and Mongolian groups. The utilization of edible wild plant resources in this area is extremely high. However, with the advancement of urbanization and the development of modern agricultural technology, these resources have been seriously damaged, and related traditional knowledge, such as that related to national medicine, has been lost. METHODS: Based on a literature study, interviews with village and community organizations, participatory observation, and quantitative evaluation of ethnobotanical resources, wild edible plants in the Gansu-Ningxia-Inner Mongolia junction zone, were investigated. RESULTS: The survey results showed that there were 53 species (varieties) of wild edible plants belonging to 24 families in this area. The Compositae and Liliaceae families were the most abundant, with 8 and 7 species, respectively. The young stems and leaves were the most edible parts of the plants, as observed for 17 species, followed by fruits (including young fruits), which were considered the edible part of 16 species. Other edible parts included the roots or rhizomes (bulbs), seeds, whole plants, skins, etc. The edible plants were consumed in two forms: raw and cooked; raw plants, mainly fruit, were typically consumed as snacks. The cooked foods mainly consisted of vegetables, with tender stems and leaves as the main food source. These components were also used as seasoning, in medicinal diets, and as an emergency food source in times of famine. Important (CFSI > 500) wild edible plants used in health care in the region include Mulgedium tataricum (L.) DC., Nostoc commune Vaucher ex Bornet & Flahault, Sonchus arvensis L., Taraxacum mongolicum Hand.-Mazz., Allium schoenoprasum L., Robinia pseudoacacia L., Hemerocallis citrina Baroni, Elaeagnus angustifolia L., Medicago sativa L., Ulmus pumila L., Stachys sieboldii Miq., and Toona sinensis (Juss.) M. Roem., and these plants had high utilization values and rates locally. CONCLUSION: In summary, the species of wild edible plants and their edible parts, categories, consumption forms and roles in health care in this area are diverse. The utilization of traditional knowledge is rich, and some wild plants have high development value.