Cancer surveillance using data warehousing, data mining, and decision support systems.

This article discusses how data warehousing, data mining, and decision support systems can reduce the national cancer burden or the oral complications of cancer therapies, especially as related to oral and pharyngeal cancers. An information system is presented that will deliver the necessary information technology to clinical, administrative, and policy researchers and analysts in an effective and efficient manner. The system will deliver the technology and knowledge that users need to readily: (1) organize relevant claims data, (2) detect cancer patterns in general and special populations, (3) formulate models that explain the patterns, and (4) evaluate the efficacy of specified treatments and interventions with the formulations. Such a system can be developed through a proven adaptive design strategy, and the implemented system can be tested on State of Maryland Medicaid data (which includes women, minorities, and children).

Can the US minimum data set be used for predicting admissions to acute care facilities?

This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value.

Effect of hydrothermal treatment of rice straw on its composition and in sacco digestibility and in vitro fermentation by rumen microorganisms.

Chemical composition, in sacco rumen disappearance of various cell wall constituents (CWC) and in vitro fermentation pattern of hydrothermally treated (1 to 14 kp/cm2 pressure for 5 min) rice straw was examined. At 10 kp/cm2 pressure treatment (maximum effect) the contents of dry matter (DM), organic matter (OM), neutral detergent fibre (NDF), acid detergent fibre (ADF), hemicellulose (HC) and cellulose (CE) were decreased by 32.5, 35.3, 27.8, 10.2, 61.2 and 25.1%, respectively (P < 0.05), over the untreated control. The in sacco rumen disappearance of DM, OM, HC and CE from rice straw treated at 8 kp/cm2 pressure (maximum effect) and incubated for 48 h was increased from 53.2 to 77.7, 52.4 to 80.3, 49.5 to 82.0 and 49.2 to 79.3%, respectively (P < 0.01). In vitro production of total volatile fatty acids and the content of TCA-insoluble protein was also significantly higher (P < 0.05) on treated compared with untreated straw.