A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narratives.

BACKGROUND: Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. METHODS: Injury narratives were extracted from claims filed with a worker's compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. RESULTS: When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. CONCLUSIONS: A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

Bayesian methods: a useful tool for classifying injury narratives into cause groups.

To compare two Bayesian methods (Fuzzy and Naïve) for classifying injury narratives in large administrative databases into event cause groups, a dataset of 14 000 narratives was randomly extracted from claims filed with a worker's compensation insurance provider. Two expert coders assigned one-digit and two-digit Bureau of Labor Statistics (BLS) Occupational Injury and Illness Classification event codes to each narrative. The narratives were separated into a training set of 11 000 cases and a prediction set of 3000 cases. The training set was used to develop two Bayesian classifiers that assigned BLS codes to narratives. Each model was then evaluated for the prediction set. Both models performed well and tended to predict one-digit BLS codes more accurately than two-digit codes. The overall sensitivity of the Fuzzy method was, respectively, 78% and 64% for one-digit and two-digit codes, specificity was 93% and 95%, and positive predictive value (PPV) was 78% and 65%. The Naïve method showed similar accuracy: a sensitivity of 80% and 70%, specificity of 96% and 97%, and PPV of 80% and 70%. For large administrative databases, Bayesian methods show significant promise as a means of classifying injury narratives into cause groups. Overall, Naïve Bayes provided slightly more accurate predictions than Fuzzy Bayes.

Unlicensed and off label prescribing of drugs in general practice.

AIM: To determine the incidence and nature of unlicensed and off label prescribing of drugs for children in general practice. METHODS: A retrospective analysis of all prescriptions for one year involving children (aged 12 years or under) from a single suburban general practice in the English Midlands. Prescribed drugs were categorised as licensed, unlicensed (without a product licence), or used in an off label way (outside the terms of their product licence). RESULTS: During 1997 there were 3347 prescription items involving 1175 children and 160 different drugs. A total of 2828 (84. 5%) prescriptions were for licensed medicines used in a licensed way; 10 (0.3%) were for unlicensed medicines; and 351 (10.5%) were licensed medicines used in an off label way. For 158 (4.7%) the information was insufficient to determine licence status. CONCLUSION: This is the first study to show that a significant number of drugs prescribed for children by general practitioners are off label and highlights the anomalies and inadequacies of drug information for prescribers.