The system decides if a patient fulfills the diagnostic criteria for an eating disorder and what type of eating disorder such as anorexia nervosa, bulimia nervosa or binge eating disorder. So patients tend to be more honest when answering a computer than a physical person. And, of course, the system is saving time for the clinician.
The purpose of the method is to improve the reliability of eating disorder diagnosis, assisting clinicians in selecting those who suffer from an eating disorder and referring those who do not fulfill eating disorder criteria to other treatments or no treatments. Demonstrating the procedures will be Alkioni Glibi, a research assistant at our laboratory. Upon patient referral, navigate to the web landing page using any modern browser.
Use an existing account associated with a clinician to log into the web tool. Fill in the patient registration form, including patient ID, birth date, age, and sex, social security number. Press the Save button to register a new patient.
Open the questionnaire application on a smart device. Fill in the social security number and first visit date for the patient. The current date is used by default.
Then fill in the information corresponding to the patient's weight, height and age. Next, fill in the information corresponding to behavior such as induced vomiting, snack frequency and eating rate, followed by particulars corresponding to cognitive and emotional items, such as fear of gaining weight and feelings of body dysmorphia. Press the Done button to finish the questionnaire.
Navigate to the web landing page using any web browser. Use an existing account to log into the web tool. Search for the patient using the patient's social security number or patient ID.Add measured weight and height to the system.
Press the Result tab to get the algorithmic decision of whether the patient has an eating disorder, or ED and what type of ED.Then press the tab questions one through 20 or questions 21 through 34 to display the questions where the patient responses deviate from answers by healthy individuals. Select a final diagnosis under the Result tab, based on the algorithm and the clinician's expertise. An example of a risk assessment page for recommended diagnosis and the estimated probability of accuracy from zero to one is shown here.
From the recommended diagnosis and questionnaire responses, healthy and deviating responses were generated. The algorithm estimating the probability of different types of eating disorders for the individual is demonstrated here. The accuracy of the model was determined to be 97.1 for ED and 82.8 for ED diagnosis.
Another benefit of the system is that it provides the answers responsible for the suggested diagnosis, which allows the system to teach clinicians to diagnose patients better and to consult additional health professionals in difficult cases.
