- Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...🔍
- Automated Detection and Classification of Type 1 Versus Type 2 ...🔍
- Automated identification of miscoded and misclassified cases of ...🔍
- Data Mining Approach to Identify Disease Cohorts from Primary ...🔍
- Validation of Diagnostic Coding for Diabetes Mellitus in Hospitalized ...🔍
- Research and Scholarly Projects🔍
- Determining diagnosis date of diabetes using structured electronic ...🔍
- Identifying patients with diabetes and the earliest date of diagnosis ...🔍
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...
This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems.
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach · Detecting ...
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach · Figures and Tables · Topics · 5 ...
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...
Our experimental study took inspiration from the experiment run in [37] that predicted miscoded diabetes ICD-10 labels in a large EHR dataset ...
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health ...
Mentioning: 3 - Background Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in ...
Automated Detection and Classification of Type 1 Versus Type 2 ...
The diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled ...
Automated identification of miscoded and misclassified cases of ...
Diagnostic codes were processed and stratified into: definite, probable and possible diagnosis of Type 1 or Type 2 diabetes. Diagnostic accuracy ...
Data Mining Approach to Identify Disease Cohorts from Primary ...
At the time of diabetes diagnosis, identified patients ... A review of approaches to identifying patient phenotype cohorts using electronic health records.
Validation of Diagnostic Coding for Diabetes Mellitus in Hospitalized ...
A missing diabetes code (MDC) was defined using 2 criteria. MDC1 was defined as the presence of any of the following: blood glucose ≥200 (x2), ...
Miscoding, misclassification and misdiagnosis of diabetes in primary ...
... 14 Misclassification of diabetes type is common when diagnostic codes alone are used to identify patients with T1DM and T2DM. 15 The first three steps of ...
Research and Scholarly Projects | Stony Brook Dept of Biomedical ...
2020;3(4):518-522. Rashidian S, Abell-Hart K, Hajagos J, et al. Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement ...
Determining diagnosis date of diabetes using structured electronic ...
An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted ...
Automated Detection and Classification of Type 1 Versus Type 2 ...
health surveillance. Sources of error in- cluded physician miscoding (type 2 di- agnosis codes assigned to patients with type 1 diabetes, diabetes diagnosis ...
Identifying patients with diabetes and the earliest date of diagnosis ...
... diagnosis in real time: an electronic health record case-finding ... diabetes encounter codes (ICD-9 250) to identify the diagnosis date.
how does the GP's method of coding clinical data affect incidence ...
Design A cross-sectional analysis examining diabetes coding from 1995 to 2014 and how the choice of codes (diagnosis codes vs codes which suggest diagnosis) and ...
Scalable and accurate deep learning with electronic health records
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach · Medicine, Computer Science.
Sina Rashidian - Google Scholar
Detecting miscoded diabetes diagnosis codes in electronic health records for quality improvement: temporal deep learning approach. S Rashidian, K Abell-Hart, J ...
Is diabetes mellitus correctly registered and classified in primary ...
Overall, 2.2% of subjects were potential miscoding errors if we take into account 4849 patients with multiple codes and 3424 with UDM code. Patients in the UDM ...
Detecting miscoded diabetes diagnosis codes in electronic health records for quality improvement: temporal deep learning approach. S Rashidian, K Abell-Hart, J ...
Algorithms to define diabetes type using data from administrative ...
... diabetes diagnostic codes (12/24) also appear to work well in detecting ... classify type 1 and 2 diabetes according to age at diagnosis using electronic health ...