Advancing Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Diagnosis: A Comparative Analysis of Machine Learning Methodologies.

This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. We applied various machine learning techniques to biomarker data from the previous IP4IC and ICRS studies to predict the presence of IC/BPS, a disorder impacting the urinary bladder. Data were sourced from two nationwide, crowd-sourced collections of urine samples involving 2009 participants.

Correction: Triterpenoid Dihydro-CDDO-Trifluoroethyl Amide Protects against Maladaptive Cardiac Remodeling and Dysfunction in Mice: A Critical Role of Nrf2.

[This corrects the article DOI: 10.1371/journal.pone.

Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions.

Objective: To improve diagnosis of interstitial cystitis (IC)/bladder pain syndrome(IC) we hereby developed an improved IC risk classification using machine learning algorithms.

Methods: A national crowdsourcing resulted in 1264 urine samples consisting of 536 IC (513 female, 21 male, 2 unspecified), and 728 age-matched controls (318 female, 402 male, 8 unspecified) with corresponding patient-reported outcome (PRO) pain and symptom scores. In addition, 296 urine samples were collected at three academic centers: 78 IC (71 female, 7 male) and 218 controls (148 female, 68 male, 2 unspecified).

Comparing online crowdsourcing with clinic patient enrollment: Findings from the IP4IC Study on interstitial cystitis/bladder pain syndrome.

Introduction: Interstitial cystitis/bladder pain syndrome (IC/BPS) manifests as urinary symptoms including urgency, frequency, and pain. The IP4IC Study aimed to establish a urine-based biomarker score for diagnosing IC/BPS. To accomplish this objective, we investigated the parallels and variances between patients enrolled via physician/hospital clinics and those recruited through online crowdsourcing.

A Cost, Time, and Demographic Analysis of Participant Recruitment and Urine Sample Collection Through Social Media Optimization.

Introduction: Clinical research can be expensive and time consuming due to high associated costs and/or duration of the study. We hypothesized that urine sample collection using online recruitment and engagement of research participants via social medial has the potential to reach a large population in a small timeframe, at a reasonable cost.

Methods: We performed a retrospective cost analysis of a cohort study comparing cost per sample and time per sample for both online and clinically recruited participants for urine sample collection.