Mobile Android application for suitable pharmaceutical selection support

Cilj završnog rada je razrada modela mobilne Android aplikacije s ciljem pružanja potpore za izbor najprikladnijeg lijeka, što je pokazano za arterijsku hipertenziju korištenjem metode eliminiranja i prioritetnog sortiranja prikladnih lijekova. Aplikacija je ostvarena u programskom jeziku Java pomoću razvojnog okruženja Android Studio. Baza podataka je ostvarena pomoću platforme Firebase, te njezine dvije usluge – Authentication, te Realtime Database. Korisniku je omogućen unos vrijednosti krvnih tlakova, te čimbenika rizika koji određuju prikladan tip terapije. Zatim, ako je potrebna farmakološka terapija, korisnik unosi svoje dodatne kontraindikacije i indikacije, te dobiva listu prikladnih lijekova sortiranih po broju indikacija. Aplikacija također generira osnovna upozorenja za kombiniranje lijekova. Korisnik na kraju ima mogućnost odabira svojih nuspojava, te unosa novih, nepostojećih, u bazu podataka. Ispitivanjem funkcionalnosti aplikacije za tri slučaja koji predstavljaju moguće ulazne parametre bolesnika, utvrđeno je da aplikacija omogućuje navedene funkcionalnosti i daje ispravne rezultate, odnosno omogućuje ispravan izbor lijeka za unesene čimbenike rizika, te ostale ulazne parametre. Ova aplikacija može se nadograditi dodatnim funkcionalnostima i aktivnostima, te drugim skupinama bolesti i lijekova koji bi proširili njezinu primjenu.The aim of this final paper is to develop a model of a mobile Android application in order to support the selection of the most appropriate drug, as demonstrated for arterial hypertension using the method of elimination and prioritization of suitable drugs. The application is written in the Java programming language in the Android Studio development environment. The database was created using the Firebase platform and its two services - Authentication and Realtime Database. The user is allowed to enter blood pressure values and risk factors that determine the appropriate type of therapy. Then, if pharmacological therapy is required, the user enters their additional contraindications and indications, and receives a list of suitable medications sorted by number of indications. The app also generates basic alerts for combining drugs. In the end, the user has the opportunity to select their side effects and to enter new, nonexistent ones in the database. By examining the functionality of the application for three cases that represent possible patient input parameters, it was found that the application provides the stated functionality and gives the correct results, that is, enables the correct choice of medicine for the entered risk factors, and other input parameters. This application can be upgraded with additional functionalities and activities, and other disease and drug classes that would expand its application

Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called 'genetically optimized Bayesian adaptive resonance theory mapping' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS. © 2013 IEEE