ArticleMayo Clinic proceedings. Innovations, quality & outcomes2017
Automated Diabetes Case Identification Using Electronic Health Record Data at a Tertiary Care Facility.
Article in Mayo Clinic proceedings. Innovations, quality & outcomes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Enabling reproducible type 1 diabetes polygenic risk scoring for clinical and translational applications.medRxiv : the preprint server for health sciences · 2025Article
- Algorithm to Identify Type 2 Diabetes Using Electronic Health Record and Self-Reported Data.Metabolic syndrome and related disorders · 2025Article
- Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.JMIR medical informatics · 2025Article
- Automated sample annotation for diabetes mellitus in healthcare integrated biobanking.Computational and structural biotechnology journal · 2024Article
- Major adverse cardiovascular events' reduction and their association with glucose-lowering medications and glycemic control among patients with type 2 diabetes: A retrospective cohort study using electronic health records.Journal of diabetes · 2024Article
- Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan.Nature communications · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Leveraging Healthcare System Data to Identify High-Risk Dyslipidemia Patients.Current cardiology reports · 2022Review
- Application of machine learning methods for the prediction of true fasting status in patients performing blood tests.Scientific reports · 2022Article
- A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data.Healthcare (Basel, Switzerland) · 2022Article
- Comparing ascertainment of chronic condition status with problem lists versus encounter diagnoses from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Predicting Diabetes in Patients with Metabolic Syndrome Using Machine-Learning Model Based on Multiple Years' Data.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- Algorithmic identification of atypical diabetes in electronic health record (EHR) systems.PloS one · 2022Article
- Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.Diabetes spectrum : a publication of the American Diabetes Association · 2022Article
- Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study.BMC medical research methodology · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies.Journal of clinical medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.