This application is designed using the Hill Cipher Algorithm which is one of the classic types of algorithms in the field of cryptography, but to get the maximum level of security, the algorithm key will be modified using a postal code pattern as a matrix key. The frequent misuse of prescription drugs by unauthorized persons, as well as errors by officers at the pharmacy in reading prescriptions can be fatal for the community, so a solution is needed to overcome this problem.
One form of service in the health sector that can utilize information technology is the manufacture of electronic drug prescriptions that can be sent via an application from a doctor to a pharmacist. From time to time technological advances have a rapid impact on all sectors, both private and government agencies, including the health sector. LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS.Ī drug prescription is a written request from a doctor to a pharmacist that must be kept secret because it contains certain doses of drugs and types of drugs that cannot be known by just anyone, especially those who are not interested. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). The two models produced a similar prediction in 86.6% of cases. The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The first included unstructured text extracted from electronic health records (EHRs).
LOS was predicted using two random forest models. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis.