A Regionalization Model to Increase Equity of Access to Maternal and Neonatal Care Services in Iran / 예방의학회지

Objectives@#Access to maternal and neonatal care services (MNCS) is an important goal of health policy in developing countries. In this study, we proposed a 3-level hierarchical location-allocation model to maximize the coverage of MNCS providers in Iran. @*Methods@#First, the necessary criteria for designing an MNCS network were explored. Birth data, including gestational age and birth weight, were collected from the data bank of the Iranian Maternal and Neonatal Network national registry based on 3 service levels (I, II, and III). Vehicular travel times between the points of demand and MNCS providers were considered. Alternative MNCS were mapped in some cities to reduce access difficulties. @*Results@#It was found that 130, 121, and 86 MNCS providers were needed to respond to level I, II, and III demands, respectively, in 373 cities. Service level III was not available in 39 cities within the determined travel time, which led to an increased average travel time of 173 minutes to the nearest MNCS provider. @*Conclusions@#This study revealed inequalities in the distribution of MNCS providers. Management of the distribution of MNCS providers can be used to enhance spatial access to health services and reduce the risk of neonatal mortality and morbidity. This method may provide a sustainable healthcare solution at the policy and decision-making level for regional, or even universal, healthcare networks.

[Using data mining for identify patients at high risk to hepatocellular carcinoma in the cirrhosis liver: preliminary report]

Data mining has an interdisciplinary field including various scientific disciplines such as: database systems, statistics, machine learning, artificial intelligence and the others. In the field of medical, data mining algorithms can help physicians to diagnose diseases and chose the best type of treatment. Hepatocellular carcinoma has the most common type of liver cancer. Given the poor prognosis, Hepatocellular carcinoma [HCC] has the fourth leading cause of cancer-related deaths. In this article we aimed to build a decision support system which helps physicians for identify patients at risk to liver cancer. We analyzed 258 patients with cirrhosis liver. Patients have followed up for four years. We have used decision tree as a data mining tool, for identify patient at high risk to Hepatocellular carcinoma. Decision tree determined the importance of attributes such as creatinine, INR and BMI which could be useful for prediction of cancer. From decision tree model, cirrhosis disease classification rules were extracted and used to improve the prediction of HCC. Decision tree could identify patients at risk to liver cancer with the accuracy of 88% for patients with Sustained virological response [SVR] and the accuracy of 92% for patients with non SVR found. According to decision tree results, attributes such as etiology, age, BMI, Platelet, Total Bilirubin, INR, Creatinine, Alfafetoproteina [AFP], and Serum Albumin can predict HCC in patient with cirrhosis. It is suggest that results examine with a greater number of patient

[Discovering hidden cluster structures in patients with cirrhosis based on laboratory data]

Liver cirrhosis was one of the most important liver diseases. Other chronic liver diseases could be lead to liver cirrhosis. Liver cirrhosis could be lead one kind of liver cancers named hepatocellular carcinoma. Cirrhosis in the early stages just by laboratory and imaging testes could be diagnosed. In this study cirrhotic patients were classified based on laboratory symptoms. For this purpose data mining approach has been used in this research. Data mining was an interdisciplinary science that discovers the hidden knowledge in the data. We used K-Means algorithm to categorize the statues of cirrhotic patients. In order to determine the quality of clustering results and to find the best number of clusters, we have used silhouette indices. Our data consists of 410 records which have been collected from Dr. Shariati hospital. The number of features in this study are 0.1 items and sampling were divided into two main groups. At first, we have done clustering based on 21 attributes and the average silhouette was 41 percent. We improved the model, in order to reach a reasonable structure. Finally, based on 7 attributes, a reasonable clustering model was derived. The new model provides 64 percent average silhouette, and based on patients' status, patients are divided into 2 main categories. The risk of HCC in the first cluster is 23 percent and in the second cluster is 14 percent