Increasing employment opportunity for persons with spinal cord injury by digital working: an exampling case series from Thailand.

INTRODUCTION: Due to activity limitations and physical environmental barriers, low remunerative employment is a challenging issue for people with spinal cord injury (SCI) and relevant rehabilitation personnel. Since work opportunities in digital fields have continued to emerge, this study aims to report and discuss the possibility of using digital working as a strategy for increasing remunerative employment in people with SCI. CASE PRESENTATION: We report live experiences of four people with SCI in Thailand who have digital works with different types of jobs (image segmentation and identification for artificial intelligence development, online merchant, online streamer, cryptocurrency investor), different required digital skills (basic or intermediate digital skills), different employment statuses (employee or owner), and different incomes (from 50 to 200 USD/month). We also discuss advantages and potential risks of digital working for people with SCI and propose a model for care providers to facilitate safe digital work as a means of increasing remunerative opportunities for people with SCI. CONCLUSION: There is increasing interest in becoming involved in various types of digital work among people with SCI. Digital working could overcome many of the physical barriers; however, it also potentially introduces some potential economic and health risks for people with SCI. To minimize those risks, healthcare providers of people with SCI should prepare to develop the appropriate knowledge and attitudes regarding digital working and to learn how to properly facilitate digital working to increase remunerative employment in people with SCI.

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review.

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities.

Reliability of the international spinal cord injury urodynamic basic data set version 1.0.

STUDY DESIGN: A cross-sectional study. OBJECTIVE: To assess interrater and intrarater reliability of the International Spinal Cord Injury (SCI) Urodynamic Basic Data Set (UBS) version 1.0. SETTING: Urodynamic clinic at Maharaj Nakorn Chiang Mai Hospital. METHODS: Two raters independently analyzed urodynamic tracings from 50 patients and completed the UBS twice, each test 1 month apart. The interrater and intrarater reliability of this data set were analyzed using Kappa, Weighted Kappa, and the Intraclass correlation coefficient (ICC). RESULTS: Of the 50 patients, 72% were male. The mean (SD) age was 48.2 (16.6) years. The median time (IQR) since the injury was 27 months (0-101 months). The interrater reliability of the items of UBS were substantial to almost perfect (0.78-0.99). The intrarater reliability of the first rater was fair to almost perfect (0.37-1.00). The intrarater reliability of the second rater was moderate to almost perfect (0.51-1.00). Relatively low interrater and intrarater reliability were observed in bladder compliance and urethral function items. CONCLUSION: The first version of UBS has acceptable interrater and intrarater reliability on most items. Although bladder compliance and urethral function have problematic interrater and intrarater reliability, they have been adjusted in the second version. Due to its simplicity and reliability, UBS is clinically useful for urodynamic assessment in people with SCI.