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Temporal Modification of Apriori to find Seasonal Variations between Symptoms and Diagnoses

Published: 26 June 2018 Publication History

Abstract

Medical data can be mined for patterns, which may be used to predict candidate diagnoses according to symptoms and other parameters of care. Our hypothesis is that the admission (initial) patient assessment, when combined with seasonal information can provide more accurate insights for the patient diagnosis. For instance, when cough is the symptom, the probability for flu could be higher during the winter (flu season). We hereby present a method to estimate the temporal variation of the probability for a diagnosis, when the initial patient assessment is known. In order to develop the model, we utilized a large synthetic medical claims dataset from the Centers for Medicare and Medicaid Services. We used the Apriori algorithm to calculate the support and confidence for each 'admission_diagnosis~final_diagnosis' itemset. For each itemset, 52 rules were generated, one for each week of a calendar year. The Apriori output was filtered so that only itemsets with the 'admission diagnosis' on the Left Hand Side(LHS) are extracted. We furthermore smoothened, using the Exponentially Weighted Moving Average (EWMA) algorithm, and then visualized the week-by-week variability of confidence, for any 'admission_diagnosis~fmal_diagnosis' pair of interest. With our approach, researchers can observe seasonal variations of the diagnosis element, and further study these variations for causal knowledge discovery.

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  • (2018)Seasonality of Discrepancies between Admission and Discharge Diagnosis for Medicare PatientsTechnologies10.3390/technologies60401116:4(111)Online publication date: 27-Nov-2018

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  1. Temporal Modification of Apriori to find Seasonal Variations between Symptoms and Diagnoses

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      cover image ACM Other conferences
      PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
      June 2018
      591 pages
      ISBN:9781450363907
      DOI:10.1145/3197768
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • NSF: National Science Foundation

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      Published: 26 June 2018

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      Author Tags

      1. Apriori
      2. Association Rule Mining
      3. Clinical Decision Making
      4. Health Informatics
      5. Seasonal Variations

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      • (2018)Seasonality of Discrepancies between Admission and Discharge Diagnosis for Medicare PatientsTechnologies10.3390/technologies60401116:4(111)Online publication date: 27-Nov-2018

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