Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning
Luis E. Sandoval-Araujo BA
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review & editing
Search for more papers by this authorClaire E. Cusack MS
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Conceptualization, Data curation, Methodology, Writing - original draft
Search for more papers by this authorChristina Ralph-Nearman PhD
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Conceptualization, Supervision, Writing - original draft, Writing - review & editing
Search for more papers by this authorSofie Glatt BA
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Writing - original draft, Writing - review & editing
Search for more papers by this authorYuchen Han MS
Department of Biostatistics & Bioinformatics, University of Louisville, Louisville, Kentucky, USA
Contribution: Data curation
Search for more papers by this authorJeffrey Bryan BS
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Writing - original draft
Search for more papers by this authorMadison A. Hooper MA, MEd
Department of Psychology, Vanderbilt University, Nashville, Tennessee, USA
Contribution: Writing - review & editing
Search for more papers by this authorAndrew Karem PhD
Department of Computer Science & Engineering, University of Louisville, Louisville, Kentucky, USA
Contribution: Supervision, Validation
Search for more papers by this authorCorresponding Author
Cheri A. Levinson PhD
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Correspondence
Cheri A. Levinson, Department of Psychological & Brain Sciences, University of Louisville, Life Sciences 317, Louisville, KY 40292, USA.
Email: [email protected]
Contribution: Conceptualization, Funding acquisition, Resources, Supervision, Writing - review & editing
Search for more papers by this authorLuis E. Sandoval-Araujo BA
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review & editing
Search for more papers by this authorClaire E. Cusack MS
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Conceptualization, Data curation, Methodology, Writing - original draft
Search for more papers by this authorChristina Ralph-Nearman PhD
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Conceptualization, Supervision, Writing - original draft, Writing - review & editing
Search for more papers by this authorSofie Glatt BA
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Writing - original draft, Writing - review & editing
Search for more papers by this authorYuchen Han MS
Department of Biostatistics & Bioinformatics, University of Louisville, Louisville, Kentucky, USA
Contribution: Data curation
Search for more papers by this authorJeffrey Bryan BS
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Contribution: Writing - original draft
Search for more papers by this authorMadison A. Hooper MA, MEd
Department of Psychology, Vanderbilt University, Nashville, Tennessee, USA
Contribution: Writing - review & editing
Search for more papers by this authorAndrew Karem PhD
Department of Computer Science & Engineering, University of Louisville, Louisville, Kentucky, USA
Contribution: Supervision, Validation
Search for more papers by this authorCorresponding Author
Cheri A. Levinson PhD
Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA
Correspondence
Cheri A. Levinson, Department of Psychological & Brain Sciences, University of Louisville, Life Sciences 317, Louisville, KY 40292, USA.
Email: [email protected]
Contribution: Conceptualization, Funding acquisition, Resources, Supervision, Writing - review & editing
Search for more papers by this authorAbstract
Objective
Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two.
Methods
Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI.
Results
Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%).
Discussion
Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered.
Public Significance
There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.
CONFLICT OF INTEREST STATEMENT
LES, CEC, SG, YH, JB, MAH, and AK report no conflicts of interest. CAL and CRN report founding members of Awaken Digital Health Solutions and CAL reports financial interest in the Behavioral Wellness Clinic; however, these interests are unrelated to the submitted publication.
Open Research
OPEN RESEARCH BADGES
This article has earned an Open Materials badge for making publicly available the components of the research methodology needed to reproduce the reported procedure and analysis. All materials are available at https://github.com/cecusack/anaan_ml.
DATA AVAILABILITY STATEMENT
All data and code associated with the project is available at https://github.com/cecusack/anaan_ml.
Supporting Information
| Filename | Description |
|---|---|
| eat24160-sup-0001-Supinfo.docxWord 2007 document , 32.3 KB | DATA S1: Supplementary Information. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- American Medical Association. (2023). AMA adopts new policy clarifying role of BMI as a measure in medicine [Press release]. https://www.ama-assn.org/press-center/press-releases/ama-adopts-new-policy-clarifying-role-bmi-measure-medicine
- American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (DSM-5-TR). American Psychiatric Association Publishing. https://doi.org/10.1176/appi.books.9780890425787
10.1176/appi.books.9780890425787 Google Scholar
- Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996). Comparison of Beck Depression Inventories-IA and-II in psychiatric outpatients. Journal of Personality Assessment, 67(3), 588–597. https://doi.org/10.1207/s15327752jpa6703_13
10.1207/s15327752jpa6703_13 Google Scholar
- Blanchard, E. B., Jones-Alexander, J., Buckley, T. C., & Forneris, C. A. (1996). Psychometric properties of the PTSD checklist (PCL). Behaviour Research and Therapy, 34(8), 669–673. https://doi.org/10.1016/0005-7967(96)00033-2
- Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees ( 1st ed.). Routledge. https://doi.org/10.1201/9781315139470
10.1201/9781315139470 Google Scholar
- Brennan, C., Illingworth, S., Cini, E., & Bhakta, D. (2023). Medical instability in typical and atypical adolescent anorexia nervosa: A systematic review and meta-analysis. Journal of Eating Disorders, 11(1), 58. https://doi.org/10.1186/s40337-023-00779-y
- Chen, J., Patil, K. R., Yeo, B. T. T., & Eickhoff, S. B. (2023). Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research. Biological Psychiatry, 93(1), 18–28. https://doi.org/10.1016/j.biopsych.2022.07.025
- Claes, L., Hart, T. A., Smits, D., Van den Eynde, F., Mueller, A., & Mitchell, J. E. (2012). Validation of the social appearance anxiety scale in female eating disorder patients. European Eating Disorders Review, 20, 406–409. https://doi.org/10.1002/erv.1147
- Crow, S. J., Peterson, C. B., Swanson, S. A., Raymond, N. C., Specker, S., Eckert, E. D., & Mitchell, J. E. (2009). Increased mortality in bulimia nervosa and other eating disorders. American Journal of Psychiatry, 166(12), 1342–1346. https://doi.org/10.1176/appi.ajp.2009.09020247
- Cunning, A., & Rancourt, D. (2023). Stigmatization of anorexia nervosa versus atypical anorexia nervosa: An experimental study. Stigma and Health. https://doi.org/10.1037/sah0000432
10.1037/sah0000432 Google Scholar
- Dalle Grave, R., Sartirana, M., El Ghoch, M., & Calugi, S. (2018). DSM-5 severity specifiers for anorexia nervosa and treatment outcomes in adult females. Eating Behaviors, 31, 18–23. https://doi.org/10.1016/j.eatbeh.2018.07.006
- Fairburn, C. G., & Beglin, S. (2008). Eating disorder examination questionnaire (EDE-Q 6.0). In C. G. Fairburn (Ed.), Cognitive behavior therapy and eating disorders. Guilford Press.
- Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self-report questionnaire? International Journal of Eating Disorders, 16(4), 363–370. https://doi.org/10.1002/1098-108X(199412)16:4<363::AID-EAT2260160405>3.0.CO;2-%23
10.1002/1098-108X(199412)16:4<363::AID-EAT2260160405>3.0.CO;2-# CAS PubMed Web of Science® Google Scholar
- Fardouly, J., Crosby, R. D., & Sukunesan, S. (2022). Potential benefits and limitations of machine learning in the field of eating disorders: Current research and future directions. Journal of Eating Disorders, 10(1), 66. https://doi.org/10.1186/s40337-022-00581-2
- First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2016). Structured clinical interview for DSM-5 disorders, clinician version (SCID-5-CV). American Psychiatric Association.
- Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., Hajcak, G., Salkovskis, P. M., Foa, E. B., Kozak, M. J., Salkovskis, P. M., Coles, M. E., & Amir, N. (2002). The obsessive-compulsive inventory: Development and validation of a short version. Psychological Assessment, 14, 485–496. https://doi.org/10.1037/1040-3590.14.4.485
- Forbush, K. T., Wildes, J. E., & Hunt, T. K. (2014). Gender norms, psychometric properties, and validity for the eating pathology symptoms inventory. The International Journal of Eating Disorders, 47(1), 85–91. https://doi.org/10.1002/eat.22180
- Forbush, K. T., Wildes, J. E., Pollack, L. O., Dunbar, D., Luo, J., Patterson, K., Petruzzi, L., Pollpeter, M., Miller, H., Stone, A., Bright, A., & Watson, D. (2013). Development and validation of the eating pathology symptoms inventory (EPSI). Psychological Assessment, 25(3), 859–878. https://doi.org/10.1037/a0032639
- Frost, R. O., Marten, P., Lahart, C., & Rosenblate, R. (1990). The dimensions of perfectionism. Cognitive Therapy and Research, 14(5), 449–468. https://doi.org/10.1007/BF01172967
- Garber, A. K., Cheng, J., Accurso, E. C., Adams, S. H., Buckelew, S. M., Kapphahn, C. J., Kreiter, A., Le Grange, D., Machen, V. I., Moscicki, A.-B., Saffran, K., Sy, A. F., Wilson, L., & Golden, N. H. (2019). Weight loss and illness severity in adolescents with atypical anorexia nervosa. Pediatrics, 144(6), e20192339. https://doi.org/10.1542/peds.2019-2339
- Golden, N. H. (2023). Atypical anorexia nervosa is not atypical at all! Commentary on Walsh et al. (2022). International Journal of Eating Disorders, 56(4), 826–827. https://doi.org/10.1002/eat.23871
- Hackeling, G. (2017). Mastering machine learning with scikit-learn: Learn to implement and evaluate machine learning solutions with scikit-learn ( 2nd ed.). Packt Publishing.
- Harrop, E. N., Hutcheson, R., Harner, V., Mensinger, J. L., & Lindhorst, T. (2023). “You Don't look anorexic”: Atypical anorexia patient experiences of weight stigma in medical care. Body Image, 46, 48–61. https://doi.org/10.1016/j.bodyim.2023.04.008
- Harrop, E. N., Mensinger, J. L., Moore, M., & Lindhorst, T. (2021). Restrictive eating disorders in higher weight persons: A systematic review of atypical anorexia nervosa prevalence and consecutive admission literature. The International Journal of Eating Disorders, 54(8), 1328–1357. https://doi.org/10.1002/eat.23519
- Hart, T. A., Flora, D. B., Palyo, S. A., Fresco, D. M., Holle, C., & Heimberg, R. G. (2008). Development and examination of the social appearance anxiety scale. Assessment, 15, 48–59. https://doi.org/10.1177/1073191107306673
- Haynos, A. F., Wang, S. B., Lipson, S., Peterson, C. B., Mitchell, J. E., Halmi, K. A., Agras, W. S., & Crow, S. J. (2021). Machine learning enhances prediction of illness course: A longitudinal study in eating disorders. Psychological Medicine, 51(8), 1392–1402. https://doi.org/10.1017/S0033291720000227
- Jablonski, M., Schebendach, J., Walsh, B. T., & Steinglass, J. E. (2024). Eating behavior in atypical anorexia nervosa. International Journal of Eating Disorders, 57(4), 780–784. https://doi.org/10.1002/eat.23886
- Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised machine learning: A brief primer. Behavior Therapy, 51(5), 675–687. https://doi.org/10.1016/j.beth.2020.05.002
- Johnson-Munguia, S., Negi, S., Chen, Y., Thomeczek, M. L., & Forbush, K. T. (2024). Eating disorder psychopathology, psychiatric impairment, and symptom frequency of atypical anorexia nervosa versus anorexia nervosa: A systematic review and meta-analysis. International Journal of Eating Disorders, 57(4), 761–779. https://doi.org/10.1002/eat.23989
- Kass, A. E., Kolko, R. P., & Wilfley, D. E. (2013). Psychological treatments for eating disorders. Current Opinion in Psychiatry, 26(6), 549–555. https://doi.org/10.1097/YCO.0b013e328365a30e
- Kim, Y., An, Z., & Treasure, J. (2023). Atypical anorexia nervosa: Implications of clinical features and BMI cutoffs. International Journal of Eating Disorders, 56(4), 828–830. https://doi.org/10.1002/eat.23911
- Krug, I., Linardon, J., Greenwood, C., Youssef, G., Treasure, J., Fernandez-Aranda, F., Karwautz, A., Wagner, G., Collier, D., Anderluh, M., Tchanturia, K., Ricca, V., Sorbi, S., Nacmias, B., Bellodi, L., & Fuller-Tyszkiewicz, M. (2023). A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: Results from the multi-centre European project on healthy eating. Psychological Medicine, 53(7), 2913–2922. https://doi.org/10.1017/S003329172100489X
- Lebow, J., Sim, L. A., & Kransdorf, L. N. (2015). Prevalence of a history of overweight and obesity in adolescents with restrictive eating disorders. Journal of Adolescent Health, 56(1), 19–24. https://doi.org/10.1016/j.jadohealth.2014.06.005
- Levinson, C. A., & Byrne, M. (2015). The fear of food measure: A novel measure for use in exposure therapy for eating disorders. International Journal of Eating Disorders, 48(3), 271–283. https://doi.org/10.1002/eat.22344
- Levinson, C. A., Cash, E., Welch, K., Epskamp, S., Hunt, R. A., Williams, B. M., Keshishian, A. C., & Spoor, S. P. (2020). Personalized networks of eating disorder symptoms predicting eating disorder outcomes and remission. International Journal of Eating Disorders, 53(12), 2086–2094. https://doi.org/10.1002/eat.23398
- Levinson, C. A., Christian, C., Ram, S. S., Vanzhula, I., Brosof, L. C., Michelson, L. P., & Williams, B. M. (2020). Eating disorder symptoms and core eating disorder fears decrease during online imaginal exposure therapy for eating disorders. Journal of Affective Disorders, 276, 585–591. https://doi.org/10.1016/j.jad.2020.07.075
- Levinson, C. A., Ralph-Nearman, C., Brown, M. L., Gardner, T., Cardi, V., Treasure, J., Purves, K., & Eley, T. C. (2021). A pilot randomized control trial of online exposure for eating disorders and mechanisms of change delivered after discharge from intensive eating disorder care: A registered report. International Journal of Eating Disorders, 54(11), 2066–2074. https://doi.org/10.1002/eat.23603
- Levinson, C. A., & Rodebaugh, T. L. (2011). Validation of the social appearance anxiety scale: Factor, convergent, and divergent validity. Assessment, 18(3), 350–356. https://doi.org/10.1177/1073191111404808
- Levinson, C. A., Trombley, C. M., Brosof, L., Williams, B., & Hunt, R. (2023). Binge eating, purging, and restriction symptoms: Increasing accuracy of prediction using machine learning. Behavior Therapy, 54(2), 247–259. https://doi.org/10.1016/j.beth.2022.08.006
- Levinson, C. A., Vanzhula, I., & Brosof, L. C. (2018). Longitudinal and personalized networks of eating disorder cognitions and behaviors: Targets for precision intervention a proof of concept study. The International Journal of Eating Disorders, 51(11), 1233–1243. https://doi.org/10.1002/eat.22952
- Levinson, C. A., Vanzhula, I. A., & Christian, C. (2019). Development and validation of the eating disorder fear questionnaire and interview: Preliminary investigation of eating disorder fears. Eating Behaviors, 35, 101320. https://doi.org/10.1016/j.eatbeh.2019.101320
- Levinson, C. A., Williams, B., Christian, C., Hunt, R., Keshishian, A., Brosof, L., Vanzhula, I., Davis, G., Brown, M., Bridges-Curry, Z., Sandoval-Araujo, L. E., & Ralph-Nearman, C. (2023). Personalizing eating disorder treatment using idiographic models: An open series trial. Journal of Consulting and Clinical Psychology, 91(1), 14–28. https://doi.org/10.1037/ccp0000785
- Lindner, T., Puck, J., & Verbeke, A. (2022). Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research. Journal of International Business Studies, 53, 1307–1314. https://doi.org/10.1057/s41267-022-00549-z
- Maj, M. (2018). Why the clinical utility of diagnostic categories in psychiatry is intrinsically limited and how we can use new approaches to complement them. World Psychiatry, 17(2), 121–122. https://doi.org/10.1002/wps.20512
- Matthews, A., Gordon, K., Kramer, R., Lin, J., & Ziv, A. (2023). Clinical characteristics of medically hospitalized youth with anorexia nervosa/atypical anorexia nervosa and premorbid overweight/obesity. Eating Disorders, 31(3), 201–211. https://doi.org/10.1080/10640266.2022.2097361
- Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy, 28, 487–495. https://doi.org/10.1016/0005-7967(90)90135-6
- Murray, S. B., Quintana, D. S., Loeb, K. L., Griffiths, S., & Grange, D. L. (2019). Treatment outcomes for anorexia nervosa: A systematic review and meta-analysis of randomized controlled trials. Psychological Medicine, 49(4), 535–544. https://doi.org/10.1017/S0033291718002088
- Nagata, J. M., Carlson, J. L., Golden, N. H., Long, J., Murray, S. B., & Peebles, R. (2019). Comparisons of bone density and body composition among adolescents with anorexia nervosa and atypical anorexia nervosa. International Journal of Eating Disorders, 52(5), 591–596. https://doi.org/10.1002/eat.23048
- Nuttall, F. Q. (2015). Body mass index: Obesity, BMI, and health a critical review. Nutrition Today, 50(3), 117–128. https://doi.org/10.1097/NT.0000000000000092
- Pargent, F., Schedel, R., & Stachl, C. (2023). Best practices in supervised machine learning: A tutorial for psychologists. Advanced in Methods and Practices in Psychological Science, 6(3), 1–35. https://doi.org/10.1177/25152459231162559
- Pedregosa, F., Varoquaux, G."e., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
- Peebles, R., Hardy, K. K., Wilson, J. L., & Lock, J. D. (2010). Are diagnostic criteria for eating disorders markers of medical severity? Pediatrics, 125(5), e1193–e1201. https://doi.org/10.1542/peds.2008-1777
- Penwell, T. E., Spoor, S., Eyre, R., & Levinson, C. A. (2023). Eating disorder treatment access climate assessment in the United States: Perceived inequities among reported treatment seekers [preprint]. Open Science Framework. https://doi.org/10.31219/osf.io/bn78p
10.31219/osf.io/bn78p Google Scholar
- Ralph-Nearman, C., Sandoval-Araujo, L. E., Karem, A., Cusack, C. E., Glatt, S., Hooper, M. A., Pena, R., Allen, S., Cash, E. D., Welch, K. C., & Levinson, C. A. (2023). Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life. Psychological Medicine, 1–7. https://doi.org/10.1017/S003329172300288X
- Sadeh-Sharvit, S., Fitzsimmons-Craft, E. E., Taylor, C. B., & Yom-Tov, E. (2020). Predicting eating disorders from internet activity. International Journal of Eating Disorders, 53(9), 1526–1533. https://doi.org/10.1002/eat.23338
- Sawyer, S. M., Whitelaw, M., Le Grange, D., Yeo, M., & Hughes, E. K. (2016). Physical and psychological morbidity in adolescents with atypical anorexia nervosa. Pediatrics, 137, 20154080. https://doi.org/10.1542/peds.2015-4080
- Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., Hergueta, T., Baker, R., & Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl 20), 22–33.
- Silén, Y., Raevuori, A., Jüriloo, E., Tainio, V.-M., Marttunen, M., & Keski-Rahkonen, A. (2015). Typical versus atypical anorexia nervosa among adolescents: Clinical characteristics and implications for ICD-11. European Eating Disorders Review: The Journal of the Eating Disorders Association, 23, 345–351. https://doi.org/10.1002/erv.2370
- Sotardi, V. A., & Dubien, D. (2019). Perfectionism, wellbeing, and university performance: A sample validation of the Frost Multidimensional Perfectionism Scale (FMPS) in New Zealand. Personality and Individual Differences, 143, 103–106. https://doi.org/10.1016/j.paid.2019.02.023
- Squires, M., Tao, X., Elangovan, S., Gururajan, R., Zhou, X., Acharya, U. R., & Li, Y. (2023). Deep learning and machine learning in psychiatry: A survey of current progress in depression detection, diagnosis and treatment. Brain Informatics, 10(1), 10. https://doi.org/10.1186/s40708-023-00188-6
- Stice, E., Fisher, M., & Martinez, E. (2004). Eating disorder diagnostic scale: Additional evidence of reliability and validity. Psychological Assessment, 16(1), 60–71.
- Stice, E., Marti, C. N., & Rohde, P. (2013). Prevalence, incidence, impairment, and course of the proposed DSM-5 eating disorder diagnoses in an 8-year prospective community study of young women. Journal of Abnormal Psychology, 122(2), 445–457. https://doi.org/10.1037/a0030679
- Swets, J. A., & Pickett, R. M. (1982). Evaluation of diagnostic systems: Methods from signal detection theory. Academic Press.
- Veillette, L. A. S., Serrano, J. M., & Brochu, P. M. (2018). What's weight got to do with it? Mental health trainees' perceptions of a client with anorexia nervosa symptoms. Frontiers in Psychology, 9, 2574. https://doi.org/10.3389/fpsyg.2018.02574
- Walsh, B. T., Hagan, K. E., & Lockwood, C. (2022). A systematic review comparing atypical anorexia nervosa and anorexia nervosa. International Journal of Eating Disorders, 56(4), 798–820. https://doi.org/10.1002/eat.23856
- Wang, S. B. (2021). Machine learning to advance the prediction, prevention and treatment of eating disorders. European Eating Disorders Review, 29(5), 683–691. https://doi.org/10.1002/erv.2850
- Weathers, F. W., Litz, B., Herman, D., Juska, J., & Keane, T. (1993). PTSD Checklist—Civilian Version [dataset]. In PsycTESTS Dataset. American Psychological Association (APA). https://doi.org/10.1037/t02622-000
- Whitelaw, M., Lee, K. J., Gilbertson, H., & Sawyer, S. M. (2018). Predictors of complications in anorexia nervosa and atypical anorexia nervosa: Degree of underweight or extent and recency of weight loss? Journal of Adolescent Health, 63(6), 717–723. https://doi.org/10.1016/j.jadohealth.2018.08.019
10.1016/j.jadohealth.2018.08.019 Google Scholar
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