Evaluation of a machine learning tool for the early identification of patients with undiagnosed psoriatic arthritis - A retrospective population-based study.

Background: Psoriatic arthritis (PsA), an immune-mediated chronic inflammatory skin and joint disease, affects approximately 0.27% of the adult population, and 20% of patients with psoriasis. Up to 10% of psoriasis patients are estimated for having undiagnosed PsA. Early diagnosis and treatment can prevent irreversible joint damage, disability and deformity. Questionnaires for screening to identify undiagnosed PsA patients require patient and physician involvement. Objective: To evaluate a proprietary machine learning tool (PredictAI™) developed for identification of undiagnosed PsA patients 1-4 years prior to the first time that they were suspected of having PsA (reference event). Methods: This retrospective study analyzed data of the adult population from Maccabi Healthcare Service between 2008 and 2020. We created 2 cohorts: The general adult population ("GP Cohort") including patients with and without psoriasis and the Psoriasis cohort ("PsO Cohort") including psoriasis patients only. Each cohort was divided into two non-overlapping train and test sets. The PredictAI™ model was trained and evaluated with 3 years of data predating the reference event by at least one year. Receiver operating characteristic (ROC) analysis was used to investigate the performance of the model, built using gradient boosted trees, at different specificity levels. Results: Overall, 2096 patients met the criteria for PsA. Undiagnosed PsA patients in the PsO cohort were identified with a specificity of 90% one and four years before the reference event, with a sensitivity of 51% and 38%, and a PPV of 36.1% and 29.6%, respectively. In the GP cohort and with a specificity of 99% and for the same time windows, the model achieved a sensitivity of 43% and 32% and a PPV of 10.6% and 8.1%, respectively. Conclusions: The presented machine learning tool may aid in the early identification of undiagnosed PsA patients, and thereby promote earlier intervention and improve patient outcomes.

Efficiently finding information on the Internet.

The Internet: a vast frontier of endless information. Despite this gold mine of knowledge, most users remain lost in cyberspace with little knowledge of what is available, where or how to find these treasures. Popular communities such as America Online have emerged because they help to organize the content into manageable pieces. But once you venture outside these confines, you get lost. This article will enable you to efficiently find general information and medically-related content on the Internet.

Fetal CNS transplants into adult spinal cord: techniques, initial effects, and caveats.

Transplantation of 11 day gestation rat fetal cortex and spinal cord into adult rat thoracic spinal cord is feasible. However, the techniques used at present for the implantation of the fetal transplant result in host spinal gray matter necrosis. One day after implantation the transplant is in a fluid-filled cyst in the host. The transplanted fetal tissue forms spherical neuroepithelia and unorganized cellular arrays. At Day 3 after transplantation the implant has sedimented to the ventral aspects of the fluid-filled cyst. By 10 days, there is an active neuroepithium with differentiating neurons and neuroglia lining the basal portion of the cyst. The transplant then proceeds to fill the cavity formed by host phagocytosis of the debris in the fluid-filled cyst.