Abstract | ||
---|---|---|
Active day-to-day analysis of hospital record entries can help alert possible disease outbreaks. Such information could help health authorities in planning prevention and minimizing the influence of the disease outbreak. Given a set of hospital entry records of patients, we developed a toolkit named BODY to analyse the data - historical and current - to present insights into disease outbreak patterns and abnormal patterns in symptom incidences. From the analysis, we aim to predict a possible outbreak of diseases detailing symptoms, time zones and mortality rates. We illustrate BODY's utility on the VAST 2010 Challenge Data containing hospital data entries from 11 cities across the world. |
Year | DOI | Venue |
---|---|---|
2010 | 10.1109/ICDMW.2010.193 | Data Mining Workshops |
Keywords | Field | DocType |
possible outbreak,disease symptoms,abnormal pattern,disease outbreak analysis,hospital entry record,hospital data entry,challenge data,disease outbreak pattern,disease outbreak,active day-to-day analysis,hospital record entry,alert possible disease outbreak,visual analytics,data visualisation,time frequency analysis,data analytics,data visualization,body,data analysis,health care,mortality rate | Health care,Data mining,Data visualization,Disease,Data analysis,Records management,Computer science,Visual analytics,Outbreak,Mortality rate | Conference |
ISBN | Citations | PageRank |
978-0-7695-4257-7 | 0 | 0.34 |
References | Authors | |
2 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hanisha Veeramachaneni | 1 | 0 | 0.34 |
Soujanya Vadapalli | 2 | 53 | 5.36 |
Kamalakar Karlapalem | 3 | 1269 | 187.45 |