KI EndoLIST: Endometriosis Longitudinal Individualized Symptoms Tracking Dataset

@article{PhysioNet-ki-endolist-1.0.0,
  author = {Zelovich, Tamar and Klaitman, Vered and Feiglin, Shaked and Zelovich, Alon and Rubinstein, Ronya and Endevelt, Ronit and Akiva, Pinchas and Bivas-Benita, Maytal and Yanover, Chen},
  title = {{KI EndoLIST: Endometriosis Longitudinal Individualized Symptoms Tracking Dataset}},
  journal = {{PhysioNet}},
  year = {2026},
  month = apr,
  note = {Version 1.0.0},
  doi = {10.13026/k99q-fm63},
  url = {https://doi.org/10.13026/k99q-fm63}
}

Abstract

Endometriosis affects approximately 10% of reproductive-age women globally, yet the time to diagnosis is four to twelve years due to clinical challenges and the normalization of symptoms by patients and healthcare providers. Additionally, highly diverse symptom profiles of patients lead to suboptimal treatment approaches and prolonged patient suffering.

This unique database addresses the critical gap in curating individual endometriosis symptoms longitudinally. Unlike periodical standardized questionnaires, our custom-developed app allowed each of 34 Israeli endometriosis patients to document their unique disease burden daily, using individualized symptom sets and severity scales.

The dataset includes an onboarding patient information file, an onboarding code dictionary, per-user longitudinal daily symptom monitoring data (and a corresponding data dictionary), and standardized mapping of symptoms to the Medical Dictionary for Regulatory Activities (MedDRA) for clinical interpretation. It enables dynamic evaluation of symptom variability, severity, and individual disease complexity at the patient level.

This dataset represents a valuable resource for researchers and clinicians seeking to understand the true complexity of endometriosis symptom experience. It enables examination of personalized symptom patterns and severity, with the purpose of promoting optimized clinician-patient engagement and individualized treatment approaches. By capturing the nuanced reality of living with endometriosis, this dataset can inform more effective diagnostic strategies and management protocols tailored to individual patient needs.

Background

Endometriosis is a chronic gynecological condition affecting approximately 6-10% of reproductive-age women worldwide, yet it remains one of the most underdiagnosed and misunderstood diseases in women's health. Despite its significant prevalence, patients with endometriosis experience diagnostic delays up to twelve years, with an average delay of 6.8 years [1–4]. This substantial delay stems from multiple factors, including the heterogeneous nature of symptoms, lack of awareness among healthcare providers, and the tendency to normalize menstrual pain in societal contexts [5].

The clinical presentation of endometriosis is complex and manifests as a wide range of symptoms affecting multiple organ systems. Individuals demonstrate different and unique sets of symptoms which may include pelvic pain, dysmenorrhea, dyspareunia, gastrointestinal symptoms, urinary dysfunction, and fatigue [6–8]. This symptom variability creates significant challenges for both patients attempting to articulate their experiences and clinicians trying to establish accurate diagnoses. Traditional approaches to symptom assessment rely primarily on retrospective patient recall during clinical consultations, which often fails to capture the dynamic nature of endometriosis symptoms across menstrual cycles and their impact on daily functioning [9].

Recent advances in digital health technologies have opened new possibilities for real-time symptom monitoring and patient-reported outcome measures. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential for facilitating chronic pain assessment and management [10]. Nevertheless, while patient self-tracking for enigmatic diseases, such as endometriosis, could serve as a complementary data source to medical records and promote a more comprehensive evaluation of patients’ status, most mobile apps provide only basic symptom tracking without personalized approaches or comprehensive data collection capabilities [10, 11]. This limitation compromises timely diagnosis and hampers optimal treatment of suffering patients.

This endometriosis symptom-tracking dataset was created to address this issue, moving beyond standardized questionnaires toward personalized symptom profiling. It was collected as part of a related research study [12] and provides longitudinal self-monitoring data from patients with endometriosis, including unique symptom sets and severity scales. This dataset aims to enhance research efforts that can bridge the gap between patient experiences and clinical understanding, ultimately leading to earlier diagnosis, better symptom management, and improved quality of life for individuals living with endometriosis.

Methods

The study protocol was reviewed and approved by the Research Ethics Committee at the University of Haifa (approval no. 367/23). To ensure patient privacy, identifiable information was removed.

Study population

Study eligibility required participants to have received a formal endometriosis diagnosis from a recognized specialist listed by the Endometriosis Foundation of Israel, following Israeli clinical guidelines that do not mandate surgical confirmation. Additional inclusion criteria included: (i) no current use of hormonal treatment; (ii) regular menstrual cycles occurring monthly; (iii) premenopausal status; and (iv) a minimum three-month interval following any endometriosis surgical procedures.

Data collection

Data were collected through a designated mobile app, developed to enhance engagement and adherence. The app employed individualized symptom profiles, enabling each participant to monitor their unique combination of symptoms, severity scales, and medications. Two individual interviews were conducted to accurately assess eligibility and optimally capture each patient’s symptom profile. After signing consent, participants received guidance on how to use the app and were supported during onboarding, building their unique symptom profile, and attaching the appropriate scale to each symptom. Participants tracked their selected symptoms using intensity scales (0–10 or 0–4, according to personal preference) ranging from “symptom-free” to “highest symptom intensity”. Additional tracked items included general physical and emotional conditions (both on scales of 1–10 or 1–4, with 1 representing the worst condition), whether they were menstruating (1 indicating yes and 0 indicating no), and, in most particpants, their bleeding intensity (0–4).

Symptom classification

To facilitate standardized analysis and enhance cross-study comparability, we constructed a comprehensive symptom database encompassing more than 200 symptoms and conditions, systematically mapped to the Medical Dictionary for Regulatory Activities (MedDRA). MedDRA provides internationally recognized medical terminology with hierarchical classification capabilities. This structured approach enables precise symptom categorization and systematic analysis of relationships between symptoms, conditions, and organ systems. Individual symptoms were systematically classified within the MedDRA hierarchy. The mapping process progressed through all hierarchical levels: Lowest Level Terms (LLT), Preferred Terms (PT), High-Level Terms (HLT), High-Level Group Terms (HLGT), and System Organ Classes (SOC), ensuring standardization of the symptom data.

Data Description

The Endometriosis Symptoms Tracking Dataset includes:

• Patient onboarding data collected through a questionnaire, including demographics and background endometriosis-related information (Patient_onboarding_info.csv), and a corresponding data dictionary (Onboarding_codes_dictionary.csv).
• Individual patient tracking files with longitudinal symptom tracking collected through the designated app (UserX.csv), and a corresponding data dictionary (Longitudinal_tracking_data_dictionary.csv).
• Mapping of tracked symptoms to MedDRA entities.
• An example R script that demonstrates how to read and decode the onboarding data and the individual tracking files (gettingStarted.R).

In all data files, blank cells indicate missing values.

Study characteristics

^{^} Symptoms mapped to MedDRA, excluding General emotional condition, General physical condition, Ovulation test values, Bleeding, medications, and well-being-related entities; ^# Percent of days with reports for at least half of the symptoms; ^* Median [IQR]; ^† Number of participants (% of the study cohort).

Usage Notes

This dataset has been developed as part of the study “Patient-Tailored Symptom Tracking in Endometriosis: A Framework to Explore Disease Variability and Burden” [12]. It may be used for research on endometriosis symptoms diversity and patterns, exploratory models of symptoms burden and co-appearance, and development of innovative approaches to facilitate patient-healthcare personnel communication. The dataset can also support studies aimed at improving treatment decision-making and personalizing care strategies for individuals with endometriosis.

This dataset may have several limitations:

1. Our research team offered participants guidance on symptom and scale definitions and conducted monthly check-ins. Although it may potentially influenced symptom reporting, it was essential for sustained engagement and continuous data collection;
2. The low number of participants calls for caution in generalizability of the findings.

The dataset has been fully anonymized in compliance with GDPR and the Israeli Privacy Protection Law. All identifiers have been removed or generalized to prevent re-identification, and a risk assessment confirmed that the likelihood of identification is negligible. Users are prohibited from any re-identification attempts, in accordance with the PhysioNet Restricted Health Data License.

Release Notes

Version 1.0.0: Initial release.

Ethics

The study protocol was approved by the Research Ethics Committee of the University of Haifa (approval no. 367/23). Study participants signed an informed consent form before starting symptom tracking.

Conflicts of Interest

The authors have no conflicts of interest to declare

References

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12. Zelovich T, Zelovich A, Feiglin S, Klaitman-Mayer V, Elhadad N, Rubinstein R, Endevelt R, Akiva P, Bivas-Benita M, Yanover C. Patient-Tailored Symptom Tracking in Endometriosis: A Framework to Explore Disease Variability and Burden. Manuscript under review.