Measuring the quality of unstructured text in routinely collected electronic health data: a review and application
Bibliographic record
Abstract
Introduction: Routinely collected electronic health data (RCEHD), can be comprised of structured, semi-structured, or unstructured information. Electronic medical records (EMRs), one type of RCEHD, often contain unstructured text data (UTD), which are typically prepared for analysis (i.e., preprocessed) and analyzed using natural language processing (NLP) techniques. At present, there are few studies about the specific types of NLP methods used to preprocess UTD to address data quality issues prior to analysis or modelling. Purpose & Objectives: The purpose was to examine preprocessing methods for UTD and evaluate the quality of UTD in EMRs. The objectives were to: 1) systematically document current research and practices for preprocessing UTD to describe or improve its quality, and 2) apply data quality indicators identified from current research and practices to UTD in EMRs from the Manitoba Primary Care Research Network and describe the quality of these data. Methods: Objective 1 involved a scoping review. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature on current research and practices to prepare UTD for analysis, up to and including 2021. For objective 2, a case study was undertaken where data quality indicators and preprocessing methods identified in the scoping review were applied to UTD from EMRs. Results: 41 articles were included in the scoping review for objective 1; over 50% were published between 2016 and 2021 and over 90% were empirical research articles. Data quality indicator topics for UTD in EMRs included misspelled words, security, word variability, sources of noise, quality of annotations, ambiguous abbreviations, and manual annotations. For objective 2, we selected 193,206 clinical encounter notes from EMRs between 1985 and 2020. Overall, the clinical encounter notes contained an average (standard deviation [SD]) of 27.3 (27.0) stop words, 25.7 (27.8) punctuation symbols, 12.1 (11.1) spelling errors, and 2.9 (2.6) special characters. The average (SD) length of a clinical encounter note was 555.8 (551.1) characters, and 71.5 (59.7) words. Lexical diversity, had a mean (SD) of 86.2 (11.9). Conclusion: This study identified multiple data quality indicators that have been used to preprocess UTD in published literature and demonstrated their application to real-world data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".