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Balogh DB, Hudelist G, Bļizņuks D, Raghothama J, Becker CM, Horace R, Krentel H, Horne AW, Bourdel N, Marki G, Tomassetti C, Kirk UB, Acs N, Bokor A. FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial. PLoS One 2024; 19:e0300186. [PMID: 38722932 PMCID: PMC11081275 DOI: 10.1371/journal.pone.0300186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 02/22/2024] [Indexed: 05/13/2024] Open
Abstract
INTRODUCTION Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years. AIMS This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization. METHODS A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning. CONCLUSIONS We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.
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Affiliation(s)
- Dora B. Balogh
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
| | - Gernot Hudelist
- Department of Gynecology, Center for Endometriosis, Hospital St. John of God, Vienna, Austria
- Rudolfinerhaus Private Clinic and Campus, Vienna, Austria
| | - Dmitrijs Bļizņuks
- Department of Computer Control and Computer Networks, Riga Technical University, Riga, Latvia
| | - Jayanth Raghothama
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Christian M. Becker
- Oxford Endometriosis CaRe Centre, Nuffield Department of Women’s and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Roman Horace
- Franco-European Multidisciplinary Endometriosis Institute (IFEMEndo), Clinique Tivoli-Ducos, Bordeaux, France
| | - Harald Krentel
- Department of Obstetrics, Gynecology, Gynecologic Oncology and Senology, Bethesda Hospital Duisburg, Duisburg, Germany
| | - Andrew W. Horne
- Centre for Reproductive Health, University of Edinburgh, Institute of Inflammation and Repair, Edinburgh, United Kingdom
| | - Nicolas Bourdel
- Department of Surgical Gynecology, University of Clermont Auvergne, Clermont-Ferrand, France
| | | | - Carla Tomassetti
- Leuven University Endometriosis Center of Expertise, Leuven University Fertility Center, Department of Obstetrics and Gynecology, UZ Gasthuisberg, Leuven, Belgium
| | - Ulrik Bak Kirk
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Research Unit for General Practice, Aarhus, Denmark
| | - Nandor Acs
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
| | - Attila Bokor
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
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Zippl AL, Reiser E, Seeber B. Endometriosis and mental health disorders: identification and treatment as part of a multimodal approach. Fertil Steril 2024; 121:370-378. [PMID: 38160985 DOI: 10.1016/j.fertnstert.2023.12.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
Endometriosis is a disease marked by more than just pain and infertility, as it transcends the well-characterized physical symptoms to be frequently associated with mental health issues. This review focuses on the associations between endometriosis and anxiety, depression, sexual dysfunction, and eating disorders, all of which show a higher prevalence in women with the disease. Studies show that pain, especially the chronic pelvic pain of endometriosis, likely serves as a mediating factor. Recent studies evaluating genetic predispositions for endometriosis and mental health disorders suggest a shared genetic predisposition. Healthcare providers who treat women with endometriosis should be aware of these associations to best treat their patients. A holistic approach to care by gynecologists as well as mental health professionals should emphasize prompt diagnosis, targeted medical interventions, and psychological support, while also recognizing the role of supportive relationships in improving the patient's quality of life.
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Affiliation(s)
- Anna Lena Zippl
- Department of Gynecological Endocrinology and Reproductive Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Elisabeth Reiser
- Department of Gynecological Endocrinology and Reproductive Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Beata Seeber
- Department of Gynecological Endocrinology and Reproductive Medicine, Medical University of Innsbruck, Innsbruck, Austria.
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Edgley K, Horne AW, Saunders PTK, Tsanas A. Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects. Cell Rep Med 2023; 4:101192. [PMID: 37729869 PMCID: PMC10518625 DOI: 10.1016/j.xcrm.2023.101192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/12/2023] [Accepted: 08/18/2023] [Indexed: 09/22/2023]
Abstract
Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking. We postulate that these pervasive and increasingly affordable technologies present promising opportunities toward developing decision-support tools assisting healthcare professionals and empowering patients with endometriosis to make better-informed choices about symptom management.
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Affiliation(s)
- Katherine Edgley
- EXPPECT and MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4UU, Scotland, UK.
| | - Andrew W Horne
- EXPPECT and MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4UU, Scotland, UK
| | - Philippa T K Saunders
- Centre for Inflammation Research, University of Edinburgh, Edinburgh EH16 4UU, Scotland, UK
| | - Athanasios Tsanas
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh EH16 4UX, Scotland, UK; Alan Turing Institute, London NW1 2DB, UK
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Sarria-Santamera A, Yemenkhan Y, Terzic M, Ortega MA, Asunsolo del Barco A. A Novel Classification of Endometriosis Based on Clusters of Comorbidities. Biomedicines 2023; 11:2448. [PMID: 37760889 PMCID: PMC10525703 DOI: 10.3390/biomedicines11092448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/27/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Endometriosis is a heterogeneous, complex, and still challenging disease, due to its epidemiological, etiological and pathogenic, diagnostic, therapeutic, and prognosis characteristics. The classification of endometriosis is contentious, and existing therapies show significant variability in their effectiveness. This study aims to capture and describe clusters of women with endometriosis based on their comorbidity. With data extracted from electronic records of primary care, this study performs a hierarchical clustering with the Ward method of women with endometriosis with a subsequent analysis of the distribution of comorbidities. Data were available for 4055 women with endometriosis, and six clusters of women were identified: cluster 1 (less comorbidity), cluster 2 (anxiety and musculoskeletal disorders), cluster 3 (type 1 allergy or immediate hypersensitivity); cluster 4 (multiple morbidities); cluster 5 (anemia and infertility); and cluster 6 (headache and migraine). Clustering aggregates similar units into similar clusters, partitioning dissimilar objects into other clusters at a progressively finer granularity-in this case, groups of women with similarities in their comorbidities. Clusters may provide a deeper insight into the multidimensionality of endometriosis and may represent diverse "endometriosis trajectories" which may be associated with specific molecular and biochemical mechanisms. Comorbidity-based clusters may be important to the scientific study of endometriosis, contributing to the clarification of its clinical complexity and variability. An awareness of those comorbidities may help elucidate the etiopathogenesis and facilitate the accurate earlier diagnosis and initiation of treatments targeted toward particular subgroups.
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Affiliation(s)
- Antonio Sarria-Santamera
- Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan
| | - Yerden Yemenkhan
- Department of Medicine, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan;
| | - Milan Terzic
- Department of Surgery, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan;
- Clinical Academic Department of Women’s Health, National Research Center for Maternal and Child Health, University Medical Center, Astana 010000, Kazakhstan
- Department of Obstetrics, Gynecology and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Miguel A. Ortega
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain;
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
- Cancer Registry and Pathology Department, Hospital Universitario Principe de Asturias, 28805 Alcalá de Henares, Spain
| | - Angel Asunsolo del Barco
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcala, 28801 Alcalá de Henares, Spain
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, The City University of New York, New York, NY 10017, USA
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Giudice LC, Oskotsky TT, Falako S, Opoku‐Anane J, Sirota M. Endometriosis in the era of precision medicine and impact on sexual and reproductive health across the lifespan and in diverse populations. FASEB J 2023; 37:e23130. [PMID: 37641572 PMCID: PMC10503213 DOI: 10.1096/fj.202300907] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 07/26/2023] [Indexed: 08/31/2023]
Abstract
Endometriosis is a common estrogen-dependent disorder wherein uterine lining tissue (endometrium) is found mainly in the pelvis where it causes inflammation, chronic pelvic pain, pain with intercourse and menses, and infertility. Recent evidence also supports a systemic inflammatory component that underlies associated co-morbidities, e.g., migraines and cardiovascular and autoimmune diseases. Genetics and environment contribute significantly to disease risk, and with the explosion of omics technologies, underlying mechanisms of symptoms are increasingly being elucidated, although novel and effective therapeutics for pain and infertility have lagged behind these advances. Moreover, there are stark disparities in diagnosis, access to care, and treatment among persons of color and transgender/nonbinary identity, socioeconomically disadvantaged populations, and adolescents, and a disturbing low awareness among health care providers, policymakers, and the lay public about endometriosis, which, if left undiagnosed and under-treated can lead to significant fibrosis, infertility, depression, and markedly diminished quality of life. This review summarizes endometriosis epidemiology, compelling evidence for its pathogenesis, mechanisms underlying its pathophysiology in the age of precision medicine, recent biomarker discovery, novel therapeutic approaches, and issues around reproductive justice for marginalized populations with this disorder spanning the past 100 years. As we enter the next revolution in health care and biomedical research, with rich molecular and clinical datasets, single-cell omics, and population-level data, endometriosis is well positioned to benefit from data-driven research leveraging computational and artificial intelligence approaches integrating data and predicting disease risk, diagnosis, response to medical and surgical therapies, and prognosis for recurrence.
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Affiliation(s)
- Linda C. Giudice
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Center for Reproductive SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Tomiko T. Oskotsky
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Bakar Computational Health Sciences InstituteUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Simileoluwa Falako
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Columbia University Vagelos College of Physicians and SurgeonsNew YorkNew YorkUSA
| | - Jessica Opoku‐Anane
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Division of Gynecologic Specialty SurgeryColumbia UniversityNew YorkNew YorkUSA
| | - Marina Sirota
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Bakar Computational Health Sciences InstituteUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PediatricsUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
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Penrod N, Okeh C, Velez Edwards DR, Barnhart K, Senapati S, Verma SS. Leveraging electronic health record data for endometriosis research. Front Digit Health 2023; 5:1150687. [PMID: 37342866 PMCID: PMC10278662 DOI: 10.3389/fdgth.2023.1150687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Endometriosis is a chronic, complex disease for which there are vast disparities in diagnosis and treatment between sociodemographic groups. Clinical presentation of endometriosis can vary from asymptomatic disease-often identified during (in)fertility consultations-to dysmenorrhea and debilitating pelvic pain. Because of this complexity, delayed diagnosis (mean time to diagnosis is 1.7-3.6 years) and misdiagnosis is common. Early and accurate diagnosis of endometriosis remains a research priority for patient advocates and healthcare providers. Electronic health records (EHRs) have been widely adopted as a data source in biomedical research. However, they remain a largely untapped source of data for endometriosis research. EHRs capture diverse, real-world patient populations and care trajectories and can be used to learn patterns of underlying risk factors for endometriosis which, in turn, can be used to inform screening guidelines to help clinicians efficiently and effectively recognize and diagnose the disease in all patient populations reducing inequities in care. Here, we provide an overview of the advantages and limitations of using EHR data to study endometriosis. We describe the prevalence of endometriosis observed in diverse populations from multiple healthcare institutions, examples of variables that can be extracted from EHRs to enhance the accuracy of endometriosis prediction, and opportunities to leverage longitudinal EHR data to improve our understanding of long-term health consequences for all patients.
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Affiliation(s)
- Nadia Penrod
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Chelsea Okeh
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, United States
| | - Digna R. Velez Edwards
- Department of Obstetrics and Gynecology, Vanderbilt University, Nashville, TN, United States
| | - Kurt Barnhart
- Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Suneeta Senapati
- Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Shefali S. Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, United States
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Goldstein A, Cohen S. Self-report symptom-based endometriosis prediction using machine learning. Sci Rep 2023; 13:5499. [PMID: 37016132 PMCID: PMC10073113 DOI: 10.1038/s41598-023-32761-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 04/01/2023] [Indexed: 04/06/2023] Open
Abstract
Endometriosis is a chronic gynecological condition that affects 5-10% of reproductive age women. Nonetheless, the average time-to-diagnosis is usually between 6 and 10 years from the onset of symptoms. To shorten time-to-diagnosis, many studies have developed non-invasive screening tools. However, most of these studies have focused on data obtained from women who had/were planned for laparoscopy surgery, that is, women who were near the end of the diagnostic process. In contrast, our study aimed to develop a self-diagnostic tool that predicts the likelihood of endometriosis based only on experienced symptoms, which can be used in early stages of symptom onset. We applied machine learning to train endometriosis prediction models on data obtained via questionnaires from two groups of women: women who were diagnosed with endometriosis and women who were not diagnosed. The best performing model had AUC of 0.94, sensitivity of 0.93, and specificity of 0.95. The model is intended to be incorporated into a website as a self-diagnostic tool and is expected to shorten time-to-diagnosis by referring women with a high likelihood of having endometriosis to further examination. We also report the importance and effectiveness of different symptoms in predicting endometriosis.
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Affiliation(s)
- Anat Goldstein
- Department of Industrial Engineering and Management, Ariel University, 65 Ramat HaGolan St., Ariel, Israel.
| | - Shani Cohen
- Department of Computer Science, Ariel University, 65 Ramat HaGolan St., Ariel, Israel
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South K, Bakken S, Koleck T, Barcelona V, Elhadad N, Dreisbach C. Women's Experiences of Symptoms of Suspected or Confirmed COVID-19 Illness During the Pandemic. Nurs Womens Health 2022; 26:450-461. [PMID: 36265561 PMCID: PMC9575040 DOI: 10.1016/j.nwh.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/19/2022] [Accepted: 09/22/2022] [Indexed: 04/13/2023]
Abstract
OBJECTIVE To explore experiences of symptoms of suspected or confirmed COVID-19 illness among women using the CovidWatcher mobile citizen science app. DESIGN Convergent parallel mixed-methods design. PARTICIPANTS Twenty-eight self-identified women consented for follow-up after using CovidWatcher. Participants' ages ranged from 18 to 83 years old. METHODS We collected data via semistructured, virtual interviews and surveys: the COVID-19 Exposure and Family Impact Survey and Patient-Reported Outcomes Measurement Information System measures. We used directed content analysis to develop codes, categories, themes, and subthemes from the qualitative data and summarized survey data with descriptive statistics. RESULTS We derived five themes related to symptom experiences: (a) Physical Symptoms, (b) Mental Health Symptoms, (c) Symptom Intensity, (d) Symptom Burden, and (e) Symptom Trajectories. Subthemes reflected more nuanced experiences of suspected or confirmed COVID-19 disease. For those without COVID-19, anxiety and mental health symptoms were still present. Of those who attested to one of the PROMIS-measured symptoms, all but one had at least mild severity in one of their reported symptoms. CONCLUSION This study demonstrates the cross-cutting impact of the COVID-19 pandemic on individuals who identify as women. Future research and clinical practice guidelines should focus on alleviating physical and mental health symptoms related to the ongoing pandemic, regardless of COVID-19 diagnosis. Furthermore, clinicians should consider how patients can use symptom reconciliation apps and tracking systems.
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Ensari I, Lipsky-Gorman S, Horan EN, Bakken S, Elhadad N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open 2022; 12:e059280. [PMID: 35851021 PMCID: PMC9297219 DOI: 10.1136/bmjopen-2021-059280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVES This study investigates the association of daily physical exercise with pain symptoms in endometriosis. We also examined whether an individual's typical weekly (ie, habitual) exercise frequency influences (ie, moderates) the relationship between their pain symptoms on a given day (day t) and previous-day (day t-1) exercise. PARTICIPANTS The sample included 90 382 days of data from 1009 participants (~85% non-Hispanic white) living with endometriosis across 38 countries. STUDY DESIGN This was an observational, retrospective study conducted using data from a research mobile app (Phendo) designed for collecting self-reported data on symptoms and self-management of endometriosis. PRIMARY OUTCOME MEASURES The two primary outcomes were the composite day-level pain score that includes pain intensity and location, and the change in this score from previous day (Δ-score). We applied generalised linear mixed-level models to examine the effect of previous-day exercise and habitual exercise frequency on these outcomes. We included an interaction term between the two predictors to assess the moderation effect, and adjusted for previous-day pain, menstrual status, education level and body mass index. RESULTS The association of previous-day (day t-1) exercise with pain symptoms on day t was moderated by habitual exercise frequency, independent of covariates (rate ratio=0.96, 95% CI=0.95 to 0.98, p=0.0007 for day-level pain score, B=-0.14, 95% CI=-0.26 to -0.016, p=0.026 for Δ-score). Those who regularly engaged in exercise at least three times per week were more likely to experience favourable pain outcomes after having a bout of exercise on the previous day. CONCLUSIONS Regular exercise might influence the day-level (ie, short-term) association of pain symptoms with exercise. These findings can inform exercise recommendations for endometriosis pain management, especially for those who are at greater risk of lack of regular exercise due to acute exacerbation in their pain after exercise.
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Affiliation(s)
- Ipek Ensari
- Data Science Institute, Columbia University, New York, New York, USA
| | - Sharon Lipsky-Gorman
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Emma N Horan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Suzanne Bakken
- Data Science Institute, Columbia University, New York, New York, USA
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- School of Nursing, Columbia University, New York, New York, USA
| | - Noémie Elhadad
- Data Science Institute, Columbia University, New York, New York, USA
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
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10
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Burgermaster M, Rodriguez VA. Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning. Ann Behav Med 2022; 56:1258-1271. [PMID: 35445699 DOI: 10.1093/abm/kaac012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors. PURPOSE To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics. METHODS In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2). RESULTS We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design. CONCLUSIONS By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.
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Affiliation(s)
- Marissa Burgermaster
- Department of Nutritional Sciences, College of Natural Sciences, University of Texas at Austin, Austin, TX, USA.,Department of Population Health, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Victor A Rodriguez
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA
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11
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Acosta JN, Falcone GJ, Rajpurkar P. The Need for Medical Artificial Intelligence That Incorporates Prior Images. Radiology 2022; 304:283-288. [PMID: 35438563 DOI: 10.1148/radiol.212830] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The use of artificial intelligence (AI) has grown dramatically in the past few years in the United States and worldwide, with more than 300 AI-enabled devices approved by the U.S. Food and Drug Administration (FDA). Most of these AI-enabled applications focus on helping radiologists with detection, triage, and prioritization of tasks by using data from a single point, but clinical practice often encompasses a dynamic scenario wherein physicians make decisions on the basis of longitudinal information. Unfortunately, benchmark data sets incorporating clinical and radiologic data from several points are scarce, and, therefore, the machine learning community has not focused on developing methods and architectures suitable for these tasks. Current AI algorithms are not suited to tackle key image interpretation tasks that require comparisons to previous examinations. Focusing on the curation of data sets and algorithm development that allow for comparisons at different points will be required to advance the range of relevant tasks covered by future AI-enabled FDA-cleared devices.
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Affiliation(s)
- Julián N Acosta
- From the Department of Neurology, Yale School of Medicine, New Haven, Conn (J.N.A., G.J.F.); and Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115 (P.R.)
| | - Guido J Falcone
- From the Department of Neurology, Yale School of Medicine, New Haven, Conn (J.N.A., G.J.F.); and Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115 (P.R.)
| | - Pranav Rajpurkar
- From the Department of Neurology, Yale School of Medicine, New Haven, Conn (J.N.A., G.J.F.); and Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115 (P.R.)
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12
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Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022; 28:31-38. [PMID: 35058619 DOI: 10.1038/s41591-021-01614-0] [Citation(s) in RCA: 494] [Impact Index Per Article: 247.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/05/2021] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.
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Affiliation(s)
- Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard University, Cambridge, MA, USA
| | - Emma Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Oishi Banerjee
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Eric J Topol
- Scripps Translational Science Institute, San Diego, CA, USA.
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Bendifallah S, Puchar A, Suisse S, Delbos L, Poilblanc M, Descamps P, Golfier F, Touboul C, Dabi Y, Daraï E. Machine learning algorithms as new screening approach for patients with endometriosis. Sci Rep 2022; 12:639. [PMID: 35022502 PMCID: PMC8755739 DOI: 10.1038/s41598-021-04637-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Endometriosis-a systemic and chronic condition occurring in women of childbearing age-is a highly enigmatic disease with unresolved questions. While multiple biomarkers, genomic analysis, questionnaires, and imaging techniques have been advocated as screening and triage tests for endometriosis to replace diagnostic laparoscopy, none have been implemented routinely in clinical practice. We investigated the use of machine learning algorithms (MLA) in the diagnosis and screening of endometriosis based on 16 key clinical and patient-based symptom features. The sensitivity, specificity, F1-score and AUCs of the MLA to diagnose endometriosis in the training and validation sets varied from 0.82 to 1, 0-0.8, 0-0.88, 0.5-0.89, and from 0.91 to 0.95, 0.66-0.92, 0.77-0.92, respectively. Our data suggest that MLA could be a promising screening test for general practitioners, gynecologists, and other front-line health care providers. Introducing MLA in this setting represents a paradigm change in clinical practice as it could replace diagnostic laparoscopy. Furthermore, this patient-based screening tool empowers patients with endometriosis to self-identify potential symptoms and initiate dialogue with physicians about diagnosis and treatment, and hence contribute to shared decision making.
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Affiliation(s)
- Sofiane Bendifallah
- Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France.
- Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), Paris, France.
- Department of Surgical Oncology, Tenon University Hospital, 4 Rue de la Chine, 75020, Paris, France.
| | - Anne Puchar
- Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France
- Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), Paris, France
| | | | - Léa Delbos
- Department of Obstetrics and Reproductive Medicine-CHU d'Angers, Angers, France
- Endometriosis Expert Center-Pays de la Loire, La Réunion, France
| | - Mathieu Poilblanc
- Department of Obstetrics and Reproductive Medicine, Lyon South University Hospital, Lyon Civil Hospices, Bron, France
- Endometriosis Expert Center-Steering Center of the EndAURA Network, Paris, France
| | - Philippe Descamps
- Department of Obstetrics and Reproductive Medicine-CHU d'Angers, Angers, France
- Endometriosis Expert Center-Pays de la Loire, La Réunion, France
| | - Francois Golfier
- Department of Obstetrics and Reproductive Medicine, Lyon South University Hospital, Lyon Civil Hospices, Bron, France
- Endometriosis Expert Center-Steering Center of the EndAURA Network, Paris, France
| | - Cyril Touboul
- Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France
- Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), Paris, France
| | - Yohann Dabi
- Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France
- Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), Paris, France
| | - Emile Daraï
- Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France
- Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), Paris, France
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Mbuguiro W, Gonzalez AN, Mac Gabhann F. Computational Models for Diagnosing and Treating Endometriosis. FRONTIERS IN REPRODUCTIVE HEALTH 2021; 3:699133. [DOI: 10.3389/frph.2021.699133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 11/23/2021] [Indexed: 11/13/2022] Open
Abstract
Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.
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15
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Adnan T, Coull BA, Jukic AM, Mahalingaiah S. The real-world applications of the symptom tracking functionality available to menstrual health tracking apps. Curr Opin Endocrinol Diabetes Obes 2021; 28:574-586. [PMID: 34560714 PMCID: PMC8631160 DOI: 10.1097/med.0000000000000682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW The goal of this review was to evaluate whether the fields available in iOS mobile phone apps for recording menstrual cycle symptoms are able to be harmonized across platforms for potential use in research, such as aggregated data analysis. RECENT FINDINGS Symptom tracking capabilities are a common feature among menstrual health apps but have been the subject of limited investigations. Apps differ with respect to which symptoms are included and the rationale for these differences is unclear. Epidemiologic studies are poised to incorporate these data; however, a thorough exploration of symptom tracking functionality across apps is required. SUMMARY Our review finds that the language used to describe symptoms and the specificity with which symptoms are collected varies greatly across the most used iOS tracking apps. Although some apps allow numerical and qualitative description of symptoms, such as sleep and mood, most simply record the presence or absence of a symptom. Collaborative efforts between clinicians and researchers to guide what and how data is collected may allow tracking apps to realize their potential diagnostic applicability. Regardless, with the increasing use of menstrual health tracking apps, it is imperative that data retrieved from such apps can realize its potential in the research and clinical ecosystems.
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Affiliation(s)
| | - Brent A. Coull
- Department of Environmental Health
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Anne Marie Jukic
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina
| | - Shruthi Mahalingaiah
- Department of Environmental Health
- Department of Obstetrics, Gynecology and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
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16
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Urteaga I, Li K, Shea A, Vitzthum VJ, Wiggins CH, Elhadad N. A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2021; 149:535-566. [PMID: 35072087 PMCID: PMC8782440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We explore how to quantify uncertainty when designing predictive models for healthcare to provide well-calibrated results. Uncertainty quantification and calibration are critical in medicine, as one must not only accommodate the variability of the underlying physiology, but adjust to the uncertain data collection and reporting process. This occurs not only on the context of electronic health records (i.e., the clinical documentation process), but on mobile health as well (i.e., user specific self-tracking patterns must be accounted for). In this work, we show that accurate uncertainty estimation is directly relevant to an important health application: the prediction of menstrual cycle length, based on self-tracked information. We take advantage of a flexible generative model that accommodates under-dispersed distributions via two degrees of freedom to fit the mean and variance of the observed cycle lengths. From a machine learning perspective, our work showcases how flexible generative models can not only provide state-of-the art predictive accuracy, but enable well-calibrated predictions. From a healthcare perspective, we demonstrate that with flexible generative models, not only can we accommodate the idiosyncrasies of mobile health data, but we can also adjust the predictive uncertainty to per-user cycle length patterns. We evaluate the proposed model in real-world cycle length data collected by one of the most popular menstrual trackers worldwide, and demonstrate how the proposed generative model provides accurate and well-calibrated cycle length predictions. Providing meaningful, less uncertain cycle length predictions is beneficial for menstrual health researchers, mobile health users and developers, as it may help design more usable mobile health solutions.
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Affiliation(s)
- Iñigo Urteaga
- Department of Applied Physics and Applied Mathematics, Data Science Institute Columbia University, New York, NY, USA
| | - Kathy Li
- Department of Applied Physics and Applied Mathematics, Data Science Institute Columbia University, New York, NY, USA
| | - Amanda Shea
- Clue by BioWink, Adalbertstraße 7-8, 10999 Berlin, Germany
| | - Virginia J Vitzthum
- Kinsey Institute & Department of Anthropology Indiana University, Bloomington, IN, USA
| | - Chris H Wiggins
- Department of Applied Physics and Applied Mathematics, Data Science Institute Columbia University, New York, NY, USA
| | - Noémie Elhadad
- Department of Biomedical Informatics, Data Science Institute Columbia University, New York, NY, USA
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17
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Abstract
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.
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Affiliation(s)
- Irene Y Chen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
| | | | - Marzyeh Ghassemi
- Vector Institute, Toronto, Ontario M5G 1M1, Canada; .,Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Rajesh Ranganath
- Department of Computer Science, Courant Institute, New York University, New York, NY 10012, USA.,Center for Data Science, New York University, New York, NY 10012, USA.,Department of Population Health, New York University Grossman School of Medicine, New York, NY 10016, USA
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18
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Delanerolle G, Yang X, Shetty S, Raymont V, Shetty A, Phiri P, Hapangama DK, Tempest N, Majumder K, Shi JQ. Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care. ACTA ACUST UNITED AC 2021; 17:17455065211018111. [PMID: 33990172 PMCID: PMC8127586 DOI: 10.1177/17455065211018111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.
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Affiliation(s)
| | - Xuzhi Yang
- Southern University of Science and Technology, Shenzhen, China
| | | | | | - Ashish Shetty
- University College London, London, UK.,University College London NHS Foundation Trust, London, UK
| | - Peter Phiri
- Southern Health NHS Foundation Trust, Southampton, UK.,Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK
| | | | | | - Kingshuk Majumder
- University of Manchester Hospitals NHS Foundation Trust, Manchester, UK
| | - Jian Qing Shi
- Southern University of Science and Technology, Shenzhen, China.,The Alan Turing Institute, London, UK
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19
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Geller S, Levy S, Ashkeloni S, Roeh B, Sbiet E, Avitsur R. Predictors of Psychological Distress in Women with Endometriosis: The Role of Multimorbidity, Body Image, and Self-Criticism. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073453. [PMID: 33810403 PMCID: PMC8037734 DOI: 10.3390/ijerph18073453] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/20/2021] [Accepted: 03/23/2021] [Indexed: 02/04/2023]
Abstract
While large numbers of women report high levels of psychological distress associated with endometriosis, others report levels of distress that are comparable to those of healthy women. Thus, the aim of the current study was to develop an explanatory model for the effect of endometriosis on women’s psychological distress. Furthermore, it sought to further investigate the role of body image, self-criticism, and pain intensity on the psychological distress associated with endometriosis and establish the effect of chronic illness load on the development of this distress. This study comprised a total of 247 women aged 20–49 (M = 31.3, SD = 6.4)—73 suffering from endometriosis only, 62 suffering from endometriosis and an additional chronical illness (ACI), and 112 healthy peers (HP)—who completed the Patient Health Questionnaire, the Generalized Anxiety Disorder-Item Scale, the Body Appreciation Scale-2, and the Self-Criticism Sub-Scale. When comparing each endometriosis group to their HP’s, we found that the differences between HP and endometriosis ACI in depression and anxiety were mediated by body image (Betas = 0.17 and 0.09, respectively, p’s < 0.05) and self-criticism (Betas = 0.23 and 0.26, respectively, p’s < 0.05). When comparing endometriosis participants to endometriosis ACI participants, differences in depression were mediated by body image, self-criticism, and pain intensity (Betas = 0.12, 0.13, 0.13 respectively, p’s < 0.05), and the differences in anxiety were mediated by self-criticism and pain intensity (Betas = 0.19, 0.08, respectively, p’s < 0.05). Physicians and other health professionals are advised to detect women with endometriosis ACI who are distressed, and to offer them appropriate intervention.
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Affiliation(s)
- Shulamit Geller
- School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel-Aviv 68182, Israel; (S.A.); (B.R.); (E.S.); (R.A.)
- Correspondence:
| | - Sigal Levy
- Statistical Education Unit, The Academic College of Tel Aviv-Yaffo, Tel-Aviv 68182, Israel;
| | - Sapir Ashkeloni
- School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel-Aviv 68182, Israel; (S.A.); (B.R.); (E.S.); (R.A.)
| | - Bar Roeh
- School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel-Aviv 68182, Israel; (S.A.); (B.R.); (E.S.); (R.A.)
| | - Ensherah Sbiet
- School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel-Aviv 68182, Israel; (S.A.); (B.R.); (E.S.); (R.A.)
| | - Ronit Avitsur
- School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel-Aviv 68182, Israel; (S.A.); (B.R.); (E.S.); (R.A.)
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20
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Saeedi A, Yadollahpour P, Singla S, Pollack B, Wells W, Sciurba F, Batmanghelich K. Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2021; 149:478-505. [PMID: 35098143 PMCID: PMC8797254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Probabilistic topic models, have been widely deployed for various applications such as learning disease or tissue subtypes. Yet, learning the parameters of such models is usually an ill-posed problem and may result in losing valuable information about disease severity. A common approach is to add a discriminative loss term to the generative model's loss in order to learn a representation that is also predictive of disease severity. However, finding a balance between these two losses is not straightforward. We propose an alternative way in this paper. We develop a framework which allows for incorporating external covariates into the generative model's approximate posterior. These covariates can have more discriminative power for disease severity compared to the representation that we extract from the posterior distribution. For instance, they can be features extracted from a neural network which predicts disease severity from CT images. Effectively, we enforce the generative model's approximate posterior to reside in the subspace of these discriminative covariates. We illustrate our method's application on a large-scale lung CT study of Chronic Obstructive Pulmonary Disease (COPD), a highly heterogeneous disease. We aim at identifying tissue subtypes by using a variant of topic model as a generative model. We quantitatively evaluate the predictive performance of the inferred subtypes and demonstrate that our method outperforms or performs on par with some reasonable baselines. We also show that some of the discovered subtypes are correlated with genetic measurements, suggesting that the identified subtypes may characterize the disease's underlying etiology.
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Affiliation(s)
| | | | | | | | - William Wells
- Harvard Medical School / Brigham and Women's Hospital
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21
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Winkler IT, Bobel C, Houghton LC, Elhadad N, Gruer C, Paranjothy V. The Politics, Promises, and Perils of Data: Evidence-Driven Policy and Practice for Menstrual Health. WOMEN'S REPRODUCTIVE HEALTH (PHILADELPHIA, PA.) 2020; 7:227-243. [PMID: 36199294 PMCID: PMC9531916 DOI: 10.1080/23293691.2020.1820240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/19/2020] [Accepted: 04/15/2020] [Indexed: 06/16/2023]
Abstract
Data determine what we know about the menstrual cycle; they inform policy and program decisions; they can point us to neglected issues and populations. But collecting and analyzing data are complicated and often fraught processes, because data are political and subjective, decisions on what data we collect and what data we do not collect are not determined by accident. As a result, despite the significant potential of the current rise in attention to menstruation, we also see risks: a lack of a solid evidence base for program decisions and resulting sensationalization; concerns about data privacy; an overreliance on participants' recall, on the one hand, while not involving participants adequately in decision making, on the other hand; and a lack of contextualized and disaggregated data. Yet better communication, contextualization, and collaboration can address many of these risks.
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Affiliation(s)
- Inga T. Winkler
- Institute for the Study of Human Rights, Columbia University, New York City, NY, USA
| | - Chris Bobel
- Department of Women’s, Gender, and Sexuality Studies, University of Massachusetts Boston, Boston, MA, USA
| | | | - Noémie Elhadad
- Department of Biomedical Informatics, Columbia University, New York City, NY, USA
| | - Caitlin Gruer
- Department of Sociomedical Sciences, New York City, NY, USA
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22
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Ensari I, Pichon A, Lipsky-Gorman S, Bakken S, Elhadad N. Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis. Appl Clin Inform 2020; 11:769-784. [PMID: 33207385 PMCID: PMC7673957 DOI: 10.1055/s-0040-1718755] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 07/14/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement. OBJECTIVES This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR. METHODS This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking. RESULTS Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%). CONCLUSION For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.
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Affiliation(s)
- Ipek Ensari
- Data Science Institute, Columbia University, New York, New York, United States
| | - Adrienne Pichon
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - Sharon Lipsky-Gorman
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
| | - Suzanne Bakken
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
- Columbia School of Nursing, Columbia University, New York, New York, United States
| | - Noémie Elhadad
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
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