Association between caregiver engagement and patient-reported healthcare utilization after stroke: a mixed-methods study

Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking

Introduction

The development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications for healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes and optimize healthcare resources. This study focuses on evaluating and optimizing machine learning models using a dataset of 10 diseases and 9,572 samples.

Methods

The dataset was divided into training and testing sets to facilitate model training and evaluation. The following models were selected and optimized: Decision Tree, Random Forest, Naive Bayes, Logistic Regression and K-Nearest Neighbors. Evaluation metrics included accuracy, F1 scores, and 10-fold cross-validation. ROC-AUC and precision-recall curves were also utilized to assess model performance, particularly in scenarios with imbalanced datasets. Clinical vignettes were employed to gauge the real-world applicability of the models.

Results

The performance of the models was evaluated using accuracy, F1 scores, and 10-fold cross-validation. The use of ROC-AUC curves revealed that model performance improved with increasing complexity. Precision-recall curves were particularly useful in evaluating model sensitivity in imbalanced dataset scenarios. Clinical vignettes demonstrated the robustness of the models in providing accurate diagnoses.

Discussion

The study underscores the importance of comprehensive model evaluation techniques. The use of clinical vignette testing and analysis of ROC-AUC and precision-recall curves are crucial in ensuring the reliability and sensitivity of symptom-based health checkers. These techniques provide a more nuanced understanding of model performance and highlight areas for further improvement.

Conclusion

This study highlights the significance of employing diverse evaluation metrics and methods to ensure the robustness and accuracy of machine learning models in symptom-based health checkers. The integration of clinical vignettes and the analysis of ROC-AUC and precision-recall curves are essential steps in developing reliable and sensitive diagnostic tools.

Roles and Problems of Stroke Caregivers: A Qualitative Study in Yogyakarta, Indonesia

Background: Caregivers play a central role in post-stroke patients care. However, the role of and problems managed by caregivers have not been widely studied, particularly in Indonesia. This study aims to explore the roles and problems of caregivers in post- stroke patients' care. Method: This was a qualitative study. Caregivers of post-stroke patients from the homecare clinic of Dr Sardjito General Hospital were purposely selected during January 2017 to June 2018. Focus group discussions were conducted to explore the roles and problems of caregiving. Results: Themes related to caregivers' roles were: connecting patients with medical personnel and other family members, maintaining patients' health conditions by fulfilling basic needs and assisting rehabilitation, as well as maintaining patients' psychological conditions by encouraging conversation, telling jokes, or recreation. On the other hand, themes related to caregivers' problems were: lack of knowledge caused by education inadequacy, underappreciated and unconcerned family, suboptimal service including limited physiotherapy and pharmacy resource, unthorough administration, lack of communication, physical limitations, and burnout, as well as uncooperative patients. Conclusions: Caregivers play essential roles as communicators and help to maintain patient's health conditions. Common problems are related to a lack of knowledge about strokes and a lack of attention from family. Understanding the roles and problems of caregivers may help facilitate better management and increase the quality of life for both patients and their caregivers.

Roles and Problems of Stroke Caregivers: A Qualitative Study in Yogyakarta, Indonesia

Background: Caregivers play a central role in post-stroke patients' care. However, the role of and problems managed by caregivers have not been widely studied, particularly in Indonesia. This study aims to explore the roles and problems of caregivers in post- stroke patients' care. Method: This was a qualitative study. Seven caregivers of post-stroke patients from the homecare clinic of Dr Sardjito General Hospital were purposely selected during January 2017 to June 2018. Focus group discussions were conducted to explore the roles and problems of caregiving. Results: Themes related to caregivers' roles were: connecting patients with medical personnel and other family members, maintaining patients' health conditions by fulfilling basic needs and assisting rehabilitation, as well as maintaining patients' psychological conditions by encouraging conversation, telling jokes, or recreation. On the other hand, themes related to caregivers' problems were: lack of knowledge caused by education inadequacy, underappreciated and unconcerned family, suboptimal service including limited physiotherapy and pharmacy resource, unthorough administration, lack of communication, physical limitations, and burnout, as well as uncooperative patients. Conclusions: Caregivers play essential roles as communicators and help to maintain patient's health conditions. Common problems are related to a lack of knowledge about strokes and a lack of attention from family. Further research to study the effects of these findings on the quality of life of both patient and their caregiver, as well as how to handle the caregiver issues should be investigated.