A data mining approach in home healthcare: outcomes and service use

Madigan, Elizabeth A.; Curet, Olivier Louis

doi:10.1186/1472-6963-6-18

Cited by 27 publications

(17 citation statements)

References 18 publications

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“…Data Mining also offers a support to identify reliable relationship between the patients' profiling or therapy and outcome. Madigan and Curet used Data Mining to predict the length of hospitalization and destination after discharge of patients with obstructive pulmonary disease, heart failure and hip replacement (Madigan & Curet, 2006) at variance with the limits and poor suitability of traditional statistical approaches (Iezzoni, 2004). Data from 580 patients living in the US were obtained though the 2000 National Home and Hospice Care Survey (NHHCS) and the survey which was conducted by the National Center for Health statistics (NCHS).…”

Section: Data Mining In Medicinementioning

confidence: 99%

Data Mining in Neurology

Candelieri¹,

Dolce²,

Riganello³

et al. 2011

Knowledge-Oriented Applications in Data Mining

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Section: Data Mining In Medicinementioning

confidence: 99%

Data Mining in Neurology

Candelieri¹,

Dolce²,

Riganello³

et al. 2011

Knowledge-Oriented Applications in Data Mining

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“…16,17 However, to our knowledge, no studies have applied these approaches to the OASIS-C version that was initiated in 2010, which we obtained from a large home health care company to identify risk factors for rehospitalization among telehomecare patients with HF. Unfortunately, it is not possible to use the national OASIS-C sample to compare telehomecare users with non-telehomecare users because there is no specific code to identify the intervention within the data set.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Home Healthcare Electronic Health Records for Telehomecare Patients With Heart Failure

Kang

McHugh²,

Chittams³

et al. 2016

CIN: Computers, Informatics, Nursing

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Heart failure is a complex condition with a significant impact on patients’ lives. A few studies have identified risk factors associated with rehospitalization among telehomecare patients with heart failure using logistic regression or survival analysis models. To date there are no published studies that have used data mining techniques to detect associations with rehospitalizations among telehomecare patients with heart failure. This study is a secondary analysis of the home health care electronic medical record called the Outcome Assessment and Information Set (OASIS)-C for 552 telemonitored heart failure patients. Bivariate analyses using SAS™ and a decision tree technique using Waikato Environment for Knowledge Analysis were used. From the decision tree technique, the presence of skin issue(s) was identified as the top predictor of rehospitalization that could be identified during the start of care assessment, followed by patient’s living situation, patient’s overall health status, severe pain experiences, frequency of activity-limiting pain, and total number of anticipated therapy visits coombined. Examining risk factors for rehospitalization from the OASIS-C database using a decision tree approach among a cohort of telehomecare patients provided a broad understanding of the characteristics of patients who are appropriate for the use of telehomcare or who need additional supports.

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“…Methodologically, the available techniques improved a lot with the help of modern statistical pure and hybrid strategies. Data mining techniques extracts complex pattern [2,3] and relationship from a given set of data. As stated by Leslie Cauley, “This is referred to as data mining.…”

Section: Introductionmentioning

confidence: 99%

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

Us²

2010

Bioinformation

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Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, β strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.

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A data mining approach in home healthcare: outcomes and service use

Cited by 27 publications

References 18 publications

Data Mining in Neurology

Data Mining in Neurology

Utilizing Home Healthcare Electronic Health Records for Telehomecare Patients With Heart Failure

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

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