Name
Abstract | ||
|---|---|---|
The digitization of healthcare data has been consolidated in the last decade as a must to manage the vast amount of data generated by healthcare organizations. Carrying out this process effectively represents an enabling resource that will improve healthcare services provision, as well as on-the-edge related applications, ranging from clinical text mining to predictive modelling, survival analysis, patient similarity, genetic data analysis and many others. The application presented in this work concerns the digitization of medical prescriptions, both to provide authorization for healthcare services or to grant reimbursement for medical expenses. The proposed system first extract text from scanned medical prescription, then Natural Language Processing and machine learning techniques provide effective classification exploiting embedded terms and categories about patient/doctor personal data, symptoms, pathology, diagnosis and suggested treatments. A REST ful Web Service is introduced, together with results of prescription classification over a set of 800K+ of diagnostic statements. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.15439/2019F197 | 2019 Federated Conference on Computer Science and Information Systems (FedCSIS) |
Keywords | Field | DocType |
medical prescription classification,NLP-based approach,digitization,healthcare data,healthcare organizations,healthcare services provision,clinical text mining,survival analysis,patient similarity,genetic data analysis,medical prescriptions,medical expenses,scanned medical prescription,machine learning techniques,REST ful Web Service | Health care,Digitization,Text mining,Computer science,Authorization,Reimbursement,Artificial intelligence,Natural language processing,Predictive modelling,Web service,Medical prescription | Conference |
ISSN | ISBN | Citations |
2325-0348 | 978-1-5386-8005-6 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincenza Carchiolo | 1 | 261 | 51.62 |
| Alessandro Longheu | 2 | 142 | 29.98 |
| Giuseppa Reitano | 3 | 0 | 0.34 |
| Luca Zagarella | 4 | 0 | 0.34 |