Automated Diabetes Case Identification Using Electronic Health Record Data at a Tertiary Care Facility

Abstract

ObjectiveTo develop and validate a phenotyping algorithm for the identification of patients with type 1 and type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic health records.Patients and MethodsWe used first-order logic rules (if-then-else rules) to imply the presence or absence of DM types 1 and 2. The “if” clause of each rule is a conjunction of logical and, or predicates that provides evidence toward or against the presence of DM. The rule includes International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes, outpatient prescription information, laboratory values, and positive annotation of DM in patients’ clinical notes. This study was conducted from March 2, 2015, through February 10, 2016. The performance of our rule-based approach and similar approaches proposed by other institutions was evaluated with a reference standard created by an expert reviewer and implemented for routine clinical care at an academic medical center.ResultsA total of 4208 surgical patients (mean age, 52 years; males, 48%) were analyzed to develop the phenotyping algorithm. Expert review identified 685 patients (16.28% of the full cohort) as having DM. Our proposed method identified 684 patients (16.25%) as having DM. The algorithm performed well—99.70% sensitivity, 99.97% specificity—and compared favorably with previous approaches.ConclusionAmong patients undergoing surgery, determination of DM can be made with high accuracy using simple, computationally efficient rules. Knowledge of patients’ DM status before surgery may alter physicians’ care plan and reduce postsurgical complications. Nevertheless, future efforts are necessary to determine the effect of first-order logic rules on clinical processes and patient outcomes.

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

“…Demographic data such as age, gender, Body Mass Index (BMI) were often adopted features used for predicting the onset of T2DM ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ). Laboratory tests such as fast plasma glucose, Hemoglobin A1c, high-density lipoprotein cholesterol were commonly seen features used by the regarded T2DM studies ( Anderson et al, 2015 ; Maniruzzaman et al, 2017 ; Talaei-Khoei & Wilson, 2018 ; Upadhyaya et al, 2017 ; Wu et al, 2018 ). Vital signs such as diastolic blood pressure and systolic blood pressure were also used by those reviewed studies ( Alghamdi et al, 2017 ; Nilashi et al, 2017 ; Talaei-Khoei & Wilson, 2018 ; Wu et al, 2018 ).…”

“…Life-style features such as physical activity, work stress, salty-food preference ( Pei et al, 2019 ), shortness of breath, frequent urination at night, excessive thirst ( Maniruzzaman et al, 2017 ; Nilashi et al, 2017 ; Talaei-Khoei & Wilson, 2018 ), and sedentary lifestyle ( Alghamdi et al, 2017 ) were also included. Finally, history features such as family history of diabetes ( Maniruzzaman et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ) or prescriptions of diabetes-related medication ( Esteban et al, 2017 ; Kagawa et al, 2017 ; Upadhyaya et al, 2017 ) were included. In practice, it is not easy to collect all the features utilized in the above-discussed studies since some features require extra efforts to acquire.…”

“…As shown in Table 3 , we can find that most samples were taken in the United States ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Maniruzzaman et al, 2017 ; Nilashi et al, 2017 ; Upadhyaya et al, 2017 ; Wu et al, 2018 ), while fewer study samples were drawn from Asian countries ( Kagawa et al, 2017 ; Pei et al, 2019 ). The evidence reviewed here clearly highlights the present need that more diversified samples should be included and analyzed aiming to better clarify their relations with the onset of T2DM.…”

“…Numerous studies have utilized machine-learning techniques to predict the onset of T2DM. While previous DM prediction studies have shown a potential for detecting the onset of T2DM ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Esteban et al, 2017 ; Kagawa et al, 2017 ; Maniruzzaman et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ; Upadhyaya et al, 2017 ; Wu et al, 2018 ), no studies, to our knowledge, have been aimed at predicting a suitable diagnosis code for T2DM patients. Further, ensemble machine-learning techniques such as bagging and boosting approaches are less utilized in these studies ( Esteban et al, 2017 ).…”

Peer Review #1 of "A multi-class classification model for supporting the diagnosis of type II diabetes mellitus (v0.1)"

“…Lifestyle features such as physical activity, work stress, salty-food preference (Pei et al 2019), shortness of breath, frequent urination at night, excessive thirst (Maniruzzaman et al 2017;Nilashi et al 2017;Talaei-Khoei & Wilson 2018), and sedentary lifestyle (Alghamdi et al 2017) were also included. Finally, history features such as family history of diabetes (Maniruzzaman et al 2017;Nilashi et al 2017;Pei et al 2019;Talaei-Khoei & Wilson 2018) or prescriptions of diabetes-related medication (Esteban et al 2017;Kagawa et al 2017;Upadhyaya et al 2017) were included. In practice, it is not easy to collect all the features utilized in the above-discussed studies since some features require extra efforts to acquire.…”