BACKGROUND: We developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic. OBJEC...
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BACKGROUND: We developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic. OBJECTIVE: To adapt NLP-PAC in a different health care setting, Sanford Children Hospital, by assessing its external validity. METHODS: The study was designed as a retrospective cohort study that used a random sample of 2011-2012 Sanford Birth cohort (n = 595). Manual chart review was performed on the cohort for asthma ascertainment on the basis of the PAC. We then used half of the cohort as a training cohort (n = 298) and the other half as a blind test cohort to evaluate the adapted NLP-PAC algorithm. Association of known asthma-related risk factors with the Sanford-NLP algorithm-driven asthma ascertainment was tested. RESULTS: Among the eligible test cohort (n = 297), 160 (53%) were males, 268 (90%) white, and the median age was 2.3 years (range, 1.5-3.1 years). NLP-PAC, after adaptation, and the human abstractor identified 74 (25%) and 72 (24%) subjects, respectively, with 66 subjects identified by both approaches. Sensitivity, specificity, positive predictive value, and negative predictive value for the NLP algorithm in predicting asthma status were 92%, 96%, 89%, and 97%, respectively. The known risk factors for asthma identified by NLP (eg, smoking history) were similar to the ones identified by manual chart review. CONCLUSIONS: Successful implementation of NLP-PAC for asthma ascertainment in 2 different practice settings demonstrates the feasibility of automated asthma ascertainment leveraging electronic health record data with a potential to enable large-scale, multisite asthma studies to improve asthma care and research. (C) 2017 American Academy of Allergy, Asthma & Immunology
The state-of-the-art algorithms of fingerprint segmentation,usually based on square block,are too dependent on the images of high quality and regular shape,so they have many deficiencies in dealing with low quality or...
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The state-of-the-art algorithms of fingerprint segmentation,usually based on square block,are too dependent on the images of high quality and regular shape,so they have many deficiencies in dealing with low quality or irregular fingerprint images,such as high complexity,time-consuming and unsatisfactory segmentation results,*** order to adapt to different quality images,the proposed algorithm follows the natural characteristics of human fingers and implements an adaptive segmentation based on rectangle ***,the images are divided into non-overlapping rectangular blocks with rows and columns of the ratio of 4:***,the algorithm, according to the statistical analysis,will determine whether each block is the prospect or not and end with the removal of isolated blocks by a smoothing *** results show that the proposed algorithm has the advantages of time-saving and self-adaptability in dealing with different quality and shape of fingerprint images in comparison with the baseline.
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