Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel proteomics-based risk prediction model specifically for Asian SLE populations to enhance personalized disease management and early intervention. Methods: A longitudinal cohort study was conducted over 48 weeks, including 139 SLE patients monitored every 12 weeks. Patients were classified into flare (n = 53) and non-flare (n = 86) groups. Baseline plasma samples underwent data-independent acquisition (DIA) proteomics analysis, and phenome-wide Mendelian randomization (PheWAS) was performed to evaluate causal relationships between proteins and clinical predictors. Logistic regression (LR) and random forest (RF) models were used to integrate proteomic and clinical data for flare risk prediction. Results: Five proteins (SAA1, B4GALT5, GIT2, NAA15, and RPIA) were significantly associated with SLE Disease Activity Index-2K (SLEDAI-2K) scores and 1-year flare risk, implicating key pathways such as B-cell receptor signaling and platelet degranulation. SAA1 demonstrated causal effects on flare-related clinical markers, including hemoglobin and red blood cell counts. A combined model integrating clinical and proteomic data achieved the highest predictive accuracy (AUC = 0.769), surpassing individual models. SAA1 was highlighted as a priority biomarker for rapid flare discrimination. Conclusion: The integration of proteomic and clinical data significantly improves flare prediction in Asian SLE patients. The identification of key proteins and their causal relationships with flare-related clinical markers provides valuable insights for proactive SLE management and personalized therapeutic approaches.

Iacucci and her colleagues recruited patients from 11 international centers between September 2016 and November 2019. Eligible participants had a confirmed diagnosis of ulcerative colitis for at least one year without regard to disease activity and an indication for a colonoscopy. At least two tissue samples were obtained from the rectum and the sigmoid because they are common areas representative of healing and inflammation. The endoscopic exam was recorded in the same area. Clinical outcomes used as proxies for disease flare-ups for the purpose of prognosis assessment included ulcerative colitis-related hospitalizations or surgery and increase in initiation of or changes in ulcerative colitis treatments, such as immunomodulators, biologics, or steroids, due to worsening symptoms.

An upcoming study will investigate how well icobrain MS, an artificial intelligence (AI)-based technology, can interpret MRI data from people with multiple sclerosis (MS) and how its use might influence patient care. The project, called AssistMS and led by Icometrix -- the technology's developer -- and Queen Mary University of London (QMUL), is supported by an AI Award in Health and Care from the U.K.'s National Institute for Care and Health Research (NIHR). Called "Artificial intelligence-assisted magnetic resonance imaging for quality, efficiency and equity in the NHS care of multiple sclerosis (AssistMS)," the project ultimately aims to improve MS care, according to Icometrix. "If successful, AssistMS will have a significant impact on [MS patient's] quality of life as well as equity and efficiency of MS care across the UK," Klaus Schmierer, PhD, a professor of neurology at QMUL and a study lead investigator, said in an Icometrix press release. MS patients routinely undergo MRI scans of the brain and spinal cord to monitor disease activity and response to disease-modifying treatments.

According to the researchers, all variables used in the ML models are available to rheumatologists in their electronic health record systems or are short PROs that can easily be captured in a remote patient monitoring program. Among the 500 patients, all initiating treatment with either golimumab or infliximab, 36% achieved low-disease activity (LDA)--indicated by a CDAI score of 10 or less. The CDAI has 4 components: patient global, physician global, tender joint count, and swollen joint count. The group found that the positive predictive value (PPV) to accurately classify LDA among the patients exceeded 80% at a sensitivity rate of 60% or greater for the best performing models. Among 8 PROs from the Patient-Reported Outcomes Measurement Information System (PROMIS) and the Short Form 36 (SF-36), several were considered useful for classification, although not including information from SF-36 had a minimal effect on model performance.

Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue. Machine learning (ML) has the potential to identify patterns in patient electronic health records (EHR) to forecast the best clinical treatment to improve patient outcomes. This study introduced a Drug Response Prediction (DRP) framework with two main goals: 1) design a data processing pipeline to extract information from tabular clinical data, and then preprocess it for functional use, and 2) predict RA patient's responses to drugs and evaluate classification models' performance. We propose a novel two-stage ML framework based on European Alliance of Associations for Rheumatology (EULAR) criteria cutoffs to model drug effectiveness. Our model Stacked-Ensemble DRP was developed and cross-validated using data from 425 RA patients. The evaluation used a subset of 124 patients (30%) from the same data source. In the evaluation of the test set, two-stage DRP leads to improved classification accuracy over other end-to-end classification models for binary classification. Our proposed method provides a complete pipeline to predict disease activity scores and identify the group that does not respond well to anti-TNF treatments, thus showing promise in supporting clinical decisions based on EHR information.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation of the large intestine. The severity of UC is often represented by the Mayo Endoscopic Subscore (MES) which quantifies mucosal disease activity from endoscopy videos. In clinical trials, an endoscopy video is assigned an MES based upon the most severe disease activity observed in the video. For this reason, severe inflammation spread throughout the colon will receive the same MES as an otherwise healthy colon with severe inflammation restricted to a small, localized segment. Therefore, the extent of disease activity throughout the large intestine, and overall response to treatment, may not be completely captured by the MES. In this work, we aim to automatically estimate UC severity for each frame in an endoscopy video to provide a higher resolution assessment of disease activity throughout the colon. Because annotating severity at the frame-level is expensive, labor-intensive, and highly subjective, we propose a novel weakly supervised, ordinal classification method to estimate frame severity from video MES labels alone. Using clinical trial data, we first achieved 0.92 and 0.90 AUC for predicting mucosal healing and remission of UC, respectively. Then, for severity estimation, we demonstrate that our models achieve substantial Cohen's Kappa agreement with ground truth MES labels, comparable to the inter-rater agreement of expert clinicians. These findings indicate that our framework could serve as a foundation for novel clinical endpoints, based on a more localized scoring system, to better evaluate UC drug efficacy in clinical trials.

Researchers within Mayo Clinic's Center for Individualized Medicine and Division of Rheumatology have developed a first-of-its-kind machine learning algorithm that can predict rheumatoid arthritis disease activity in a patient. "Having fast, reliable and scalable measures for predicting the clinical course of disease activity is an important unmet need for patients with rheumatoid arthritis,'' says Jaeyun Sung, Ph.D., a computational biologist within the Center for Individualized Medicine and co-senior author of the study. Dr. Sung develops computational analytical approaches to understand the intricate relationship between microbial organisms and human metabolic and immune health. The study, which was published in Arthritis Research & Therapy, lays the groundwork for monitoring rheumatoid arthritis disease progression and systemic inflammation using blood samples alone. The findings provide direction for the potential future development of clinical laboratory tests and digital diagnostics to further enable precision medicine for rheumatoid arthritis patients. "We turned to the blood because it could potentially provide a treasure-trove of novel biomarkers for assessing not only disease activity, but also clinical subgroups, risk factors and predictors of treatment response that complement current standard laboratory tests.

Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

While objective indicators are more transparent and robust, the subjective evaluation contains a wealth of expert knowledge and intuition. In this work, we demonstrate the potential of pairwise ranking methods to align the subjective evaluation with objective indicators, creating a new score that combines their advantages and facilitates diagnosis. In a case study on patients at risk for developing Psoriatic Arthritis, we illustrate that the resulting score (1) increases classification accuracy when detecting disease presence/absence, (2) is sparse and (3) provides a nuanced assessment of severity for subsequent analysis.

We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.