Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes

Effects of predictive nursing interventions on pressure ulcer in elderly bedridden patients

The study explores the impact of predictive nursing interventions on pressure ulcers (PUs) in elderly bedridden patients. A total of 120 elderly bedridden patients from the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture between August 2019 and August 2023 were selected as the subjects of the study and were randomly divided into an observation group and a control group using a random number table method. The control group received conventional nursing care, while the observation group received predictive nursing interventions. The study compared the incidence of PUs, Braden scale scores, the onset time of PUs, self-rating anxiety scale (SAS) scores, self-rating depression scale (SDS) scores, and nursing satisfaction between the two groups. In elderly bedridden patients, the application of predictive nursing interventions significantly reduced the incidence of PUs (p < 0.001), significantly lowered the SAS and SDS scores (p < 0.001), and also significantly increased Braden scale scores (p < 0.001) and delayed the onset time of PUs (p < 0.001). Additionally, it improved patients' nursing satisfaction (p = 0.008). Predictive nursing interventions in elderly bedridden patients have good application effects, reducing the occurrence of PUs, delaying the time of onset in patients, improving patients' negative emotions and enhancing nursing satisfaction rates. It is worthy of widespread use.

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients.

Effect of Algoplaque Hydrocolloid Dressing Combined with Nanosilver Antibacterial Gel under Predictive Nursing in the Treatment of Medical Device-Related Pressure Injury

It was aimed at the clinical value of predictive nursing and Algoplaque hydrocolloid dressing (AHD) combined with nanosilver antibacterial gel in treating medical device-related pressure injury (MDRPI). 100 patients, who underwent surgery in Chongqing Qijiang District People's Hospital from February 2019 to February 2020, were selected as the research objects and were randomly divided into the experimental group (50 cases) and the control group (50 cases). For the characterization test, a nanosilver antibacterial gel was created first. Patients in both groups received predictive nursing, but those in the experimental group received AHD and nanosilver antibacterial gel, and those in the control group received gauzes. MDRPI incidence, pressed skin injury severity, comfort level, clothing changes, nursing satisfaction, and other factors were all compared. The particle size of the nanosilver gel was 45-85 nm, with a relatively homogeneous distribution with the medium size, according to the findings. The incidence of MDRPI in the experimental group was lower than that in the control group significantly (6% vs. 30%, P < 0.05). The degree of injury of pressured skin in the experimental group was milder than that in the control group (P < 0.05), the degree of comfort and nursing satisfaction was higher in the experimental group than in the control group (P < 0.05), and dressing change count was lower than that in the control group (P < 0.05). In the treatment of MDRPI, predictive nursing and AHD using nanosilver antibacterial gel showed high clinical application value.