Validation of a machine learning approach to estimate Clinical Disease Activity Index Scores for rheumatoid arthritis

A survey of artificial intelligence in rheumatoid arthritis

The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis

Background

Disability assessment using the Expanded Disability Status Scale (EDSS) is important to inform treatment decisions and monitor the progression of multiple sclerosis. Yet, EDSS scores are documented infrequently in electronic medical records.

Objective

To validate a machine learning model to estimate EDSS scores for multiple sclerosis patients using clinical notes from neurologists.

Methods

A machine learning model was developed to estimate EDSS scores on specific encounter dates using clinical notes from neurologist visits. The OM1 MS Registry data were used to create a training cohort of 2632 encounters and a separate validation cohort of 857 encounters, all with clinician-recorded EDSS scores. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV), calculated using a binarized version of the outcome. The Spearman R and Pearson R values were calculated. The model was then applied to encounters without clinician-recorded EDSS scores in the MS Registry.

Results

The model had a PPV of 0.85, NPV of 0.85, and AUC of 0.91. The model had a Spearman R value of 0.75 and Pearson R value of 0.74 when evaluating performance using the continuous estimated EDSS and clinician-recorded EDSS scores. Application of the model to eligible encounters resulted in the generation of eEDSS scores for an additional 190,282 encounters from 13,249 patients.

Conclusion

EDSS scores can be estimated with very good performance using a machine learning model applied to clinical notes, thus increasing the utility of real-world data sources for research purposes.