Literature DB >> 28066845

Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis.

Diana Ohanian1, Abigail Brown1, Madison Sunnquist1, Jacob Furst1, Laura Nicholson1, Lauren Klebek1, Leonard A Jason1.   

Abstract

It is unclear what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS). The current study compared self-report symptom data of patients with ME or CFS with those with MS. The self-report data is from the DePaul Symptom Questionnaire, and participants were recruited to take the questionnaire online. Data were analyzed using a machine learning technique called decision trees. Five symptoms best differentiated the groups. The best discriminating symptoms were from the immune domain (i.e., flu-like symptoms and tender lymph nodes), and the trees correctly categorized MS from ME or CFS 81.2% of the time, with those with ME or CFS having more severe symptoms. Our findings support the use of machine learning to further explore the unique nature of these different chronic diseases.

Entities:  

Keywords:  Chronic Fatigue Syndrome; Data Mining; Decision Trees; Multiple Sclerosis; Myalgic Encephalomyelitis

Year:  2016        PMID: 28066845      PMCID: PMC5214344     

Source DB:  PubMed          Journal:  Neurology (ECronicon)


  13 in total

1.  Detection of immunologically significant factors for chronic fatigue syndrome using neural-network classifiers.

Authors:  S J Hanson; W Gause; B Natelson
Journal:  Clin Diagn Lab Immunol       Date:  2001-05

2.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

3.  The utility of patient-reported outcome measures among patients with myalgic encephalomyelitis/chronic fatigue syndrome.

Authors:  Kyle W Murdock; Xin Shelley Wang; Qiuling Shi; Charles S Cleeland; Christopher P Fagundes; Suzanne D Vernon
Journal:  Qual Life Res       Date:  2016-09-06       Impact factor: 4.147

Review 4.  Feminist perspectives on the social construction of chronic fatigue syndrome.

Authors:  J A Richman; L A Jason; R R Taylor; S C Jahn
Journal:  Health Care Women Int       Date:  2000 Apr-May

5.  Patterns of utilization of medical care and perceptions of the relationship between doctor and patient with chronic illness including chronic fatigue syndrome.

Authors:  S W Twemlow; S L Bradshaw; L Coyne; B H Lerma
Journal:  Psychol Rep       Date:  1997-04

6.  Validating a measure of myalgic encephalomyelitis/chronic fatigue syndrome symptomatology.

Authors:  Abigail A Brown; Leonard A Jason
Journal:  Fatigue       Date:  2014-07-23

7.  Immunological abnormalities as potential biomarkers in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis.

Authors:  Ekua W Brenu; Mieke L van Driel; Don R Staines; Kevin J Ashton; Sandra B Ramos; James Keane; Nancy G Klimas; Sonya M Marshall-Gradisnik
Journal:  J Transl Med       Date:  2011-05-28       Impact factor: 5.531

8.  Distinct plasma immune signatures in ME/CFS are present early in the course of illness.

Authors:  Mady Hornig; José G Montoya; Nancy G Klimas; Susan Levine; Donna Felsenstein; Lucinda Bateman; Daniel L Peterson; C Gunnar Gottschalk; Andrew F Schultz; Xiaoyu Che; Meredith L Eddy; Anthony L Komaroff; W Ian Lipkin
Journal:  Sci Adv       Date:  2015-02       Impact factor: 14.136

9.  A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

Authors:  Lung-Cheng Huang; Sen-Yen Hsu; Eugene Lin
Journal:  J Transl Med       Date:  2009-09-22       Impact factor: 5.531

Review 10.  Machine learning applications in cancer prognosis and prediction.

Authors:  Konstantina Kourou; Themis P Exarchos; Konstantinos P Exarchos; Michalis V Karamouzis; Dimitrios I Fotiadis
Journal:  Comput Struct Biotechnol J       Date:  2014-11-15       Impact factor: 7.271

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  13 in total

1.  Differentiating Multiple Sclerosis from Myalgic Encephalomyelitis and Chronic Fatigue Syndrome.

Authors:  L A Jason; D Ohanian; A Brown; M Sunnquist; S McManimen; L Klebek; P Fox; M Sorenson
Journal:  Insights Biomed       Date:  2017-06-12

2.  Operationalizing Substantial Reduction in Functioning Among Young Adults with Chronic Fatigue Syndrome.

Authors:  Kristen D Gleason; Jamie Stoothoff; Damani McClellan; Stephanie McManimen; Taylor Thorpe; Ben Z Katz; Leonard A Jason
Journal:  Int J Behav Med       Date:  2018-08

3.  The development of a short form of the DePaul Symptom Questionnaire.

Authors:  Madison Sunnquist; Savitri Lazarus; Leonard A Jason
Journal:  Rehabil Psychol       Date:  2019-07-18

4.  Differentiating Post-Polio Syndrome from Myalgic Encephalomyelitis and Chronic Fatigue Syndrome.

Authors:  Lauren Klebek; Madison Sunnquist; Leonard A Jason
Journal:  Fatigue       Date:  2019-11-06

5.  The DePaul Symptom Questionnaire-2: A Validation Study.

Authors:  Helen Bedree; Madison Sunnquist; Leonard A Jason
Journal:  Fatigue       Date:  2019-08-12

6.  COVID-19 Symptoms Over Time: Comparing Long-Haulers to ME/CFS.

Authors:  Leonard A Jason; Mohammed Islam; Karl Conroy; Joseph Cotler; Chelsea Torres; Mady Johnson; Brianna Mabie
Journal:  Fatigue       Date:  2021-05-05

7.  Evaluating case diagnostic criteria for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): toward an empirical case definition.

Authors:  Karl E Conroy; Mohammed F Islam; Leonard A Jason
Journal:  Disabil Rehabil       Date:  2022-03-02       Impact factor: 2.439

8.  The Development of the DePaul Symptom Questionnaire: Original, Expanded, Brief, and Pediatric Versions.

Authors:  Leonard A Jason; Madison Sunnquist
Journal:  Front Pediatr       Date:  2018-11-06       Impact factor: 3.418

9.  Myalgic encephalomyelitis and chronic fatigue syndrome case definitions: effects of requiring a substantial reduction in functioning.

Authors:  Samantha Scartozzi; Madison Sunnquist; Leonard A Jason
Journal:  Fatigue       Date:  2019-04-01

Review 10.  A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.

Authors:  I S Stafford; M Kellermann; E Mossotto; R M Beattie; B D MacArthur; S Ennis
Journal:  NPJ Digit Med       Date:  2020-03-09
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