Background Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. Method Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers’ interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation. Results The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. Conclusions The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

African Swine Fever (ASF) is a highly contagious hemorrhagic viral disease of domestic and wild pigs. ASF has led to huge economic loss and social impact worldwide. The biological mechanism of ASF’s infections is still not fully understood, and the lack of preventative options at the individual level further complicates this major global health challenge. In this paper, we propose a novel method to model the spread of ASF in China by integrating the data of pork import/export, transportation networks, and port distribution centers. We first empirically analyze the overall patterns of ASF spread and performs extensive experiments to evaluate the efficacy of a number of distance measures. These empirical analyses show that the arrival of ASF is not purely based on the geographic distance from existing infected regions. The pork supply-demand patterns have clearly influenced the spread of ASF, which cannot be well explained by conventional geographical distance and the recent effective distance methods. Predictions based on the new distance measure achieve better performance in predicting the disease spreading among Chinese provinces and thus have the potential to enable more proactive and accurate deployment of interventions.

BackgroundThis study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach.MethodsInformation on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two‐step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root‐mean‐squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model.ResultsIn total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models.ConclusionThe two‐step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule‐based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.