Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring

Evaluating the quality of home care in community health service centres: A machine learning approach

AIMS

The aim of the study is to develop a model using a machine learning approach that can effectively identify the quality of home care in communities.

DESIGN

A cross-sectional design.

METHODS

In this study, we evaluated the quality of home care in 170 community health service centres between October 2022 and February 2023. The Home Care Service Quality Questionnaire was used to collect information on home care structure, process and outcome quality. Then, an intelligent and comprehensive evaluation model was developed using a convolutional neural network, and its performance was compared with random forest and logistic regression models through various performance indicators.

RESULTS

The convolutional neural network model was built upon seven variables, which encompassed the qualification of home nursing staff, developing and practicing emergency plan to cope with different emergency rescues in home environment, being equipped with medication and supplies for first aid according to specific situations, assessing nutrition condition of home patients, allocation of the number of home nursing staff, cases of new pressure ulcers and patient satisfaction rate. Remarkably, the convolutional neural network model demonstrated superior performance, outperforming both the random forest and regression models.

CONCLUSION

The successful development and application of the convolutional neural network model highlight its ability to leverage data from community health service centres for rapid and accurate grading of home care quality. This research points the way to home care quality improvement.

IMPACT

The model proposed in this study, coupled with the aforementioned factors, is expected to enhance the accuracy and efficiency of a comprehensive evaluation of home care quality. It will also help managers to take purposeful measures to improve the quality of home care.

REPORTING METHOD

The reporting of this study (Observational, cross-sectional study) conforms to the STROBE statement.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

The application of this model has the potential to contribute to the advancement of high-quality home care, particularly in lower-middle-income communities.

Impedance-Based Ventilation Detection and Signal Quality Control During Out-of-Hospital Cardiopulmonary Resuscitation

Feedback on ventilation could help improve cardiopulmonary resuscitation quality and survival from out-of-hospital cardiac arrest (OHCA). However, current technology that monitors ventilation during OHCA is very limited. Thoracic impedance (TI) is sensitive to air volume changes in the lungs, allowing ventilations to be identified, but is affected by artifacts due to chest compressions and electrode motion. This study introduces a novel algorithm to identify ventilations in TI during continuous chest compressions in OHCA. Data from 367 OHCA patients were included, and 2551 one-minute TI segments were extracted. Concurrent capnography data were used to annotate 20724 ground truth ventilations for training and evaluation. A three-step procedure was applied to each TI segment: First, bidirectional static and adaptive filters were applied to remove compression artifacts. Then, fluctuations potentially due to ventilations were located and characterized. Finally, a recurrent neural network was used to discriminate ventilations from other spurious fluctuations. A quality control stage was also developed to anticipate segments where ventilation detection could be compromised. The algorithm was trained and tested using 5-fold cross-validation, and outperformed previous solutions in the literature on the study dataset. The median (interquartile range, IQR) per-segment and per-patient F 1-scores were 89.1 (70.8-99.6) and 84.1 (69.0-93.9), respectively. The quality control stage identified most low performance segments. For the 50% of segments with highest quality scores, the median per-segment and per-patient F 1-scores were 100.0 (90.9-100.0) and 94.3 (86.5-97.8). The proposed algorithm could allow reliable, quality-conditioned feedback on ventilation in the challenging scenario of continuous manual CPR in OHCA.

N-Net: Lesion region segmentations using the generalized hybrid dilated convolutions for polyps in colonoscopy images

Colorectal cancer is the cancer with the second highest and the third highest incidence rates for the female and the male, respectively. Colorectal polyps are potential prognostic indicators of colorectal cancer, and colonoscopy is the gold standard for the biopsy and the removal of colorectal polyps. In this scenario, one of the main concerns is to ensure the accuracy of lesion region identifications. However, the missing rate of polyps through manual observations in colonoscopy can reach 14%–30%. In this paper, we focus on the identifications of polyps in clinical colonoscopy images and propose a new N-shaped deep neural network (N-Net) structure to conduct the lesion region segmentations. The encoder-decoder framework is adopted in the N-Net structure and the DenseNet modules are implemented in the encoding path of the network. Moreover, we innovatively propose the strategy to design the generalized hybrid dilated convolution (GHDC), which enables flexible dilated rates and convolutional kernel sizes, to facilitate the transmission of the multi-scale information with the respective fields expanded. Based on the strategy of GHDC designing, we design four GHDC blocks to connect the encoding and the decoding paths. Through the experiments on two publicly available datasets on polyp segmentations of colonoscopy images: the Kvasir-SEG dataset and the CVC-ClinicDB dataset, the rationality and superiority of the proposed GHDC blocks and the proposed N-Net are verified. Through the comparative studies with the state-of-the-art methods, such as TransU-Net, DeepLabV3+ and CA-Net, we show that even with a small amount of network parameters, the N-Net outperforms with the Dice of 94.45%, the average symmetric surface distance (ASSD) of 0.38 pix and the mean intersection-over-union (mIoU) of 89.80% on the Kvasir-SEG dataset, and with the Dice of 97.03%, the ASSD of 0.16 pix and the mIoU of 94.35% on the CVC-ClinicDB dataset.