Fluid volume status detection model for patients with heart failure based on machine learning methods.

Backgroud: Fluid volume abnormalities are a major cause of exacerbations in heart failure patients. However, there is few efficient, rapid, or cost-effective clinical approach for determining volume status, resulting in inadequate or unsatisfactory treatment. The aim was to develop an early fluid volume detection model for heart failure patients utilizing a machine learning stratification.

Automatic Identification of MALDI-TOF MS Database Using Classical Species Isolates.

Objective: To evaluate and expand the automatic identification and clustering of clinical species by MALDI-TOF MS.

Methods: Twenty-eight field isolated strains, identified by whole-gene sequencing analysis, were analyzed by MALDI-TOF MS, and the spectra obtained were used to replenish the internal database of the manufacturer. To evaluate and expand the robustness of the database, MALDI-TOF MS identified 91 clinical isolates (except those used for implementation).

BI - Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal.

Sleep apnea syndrome (SAS) is a sleeping disorder in which breathing stops regularly. Even though its prevalence is high, many cases are not reported due to the high cost of inspection and the limits of monitoring devices. To address this, based on the bidirectional long and short-term memory network (BI-LSTM), we designed a single-channel electroencephalography (EEG) sleep monitoring model that can be used in portable SAS monitoring devices.