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

BACKGROUND: The purpose of this research is to understand the performance of home healthcare practice in the US. The relationships between home healthcare patient factors and agency characteristics are not well understood. In particular, discharge destination and length of stay have not been studied using a data mining approach which may provide insights not obtained through traditional statistical analyses. METHODS: The data were obtained from the 2000 National Home and Hospice Care Survey data for three specific conditions (chronic obstructive pulmonary disease, heart failure and hip replacement), representing nearly 580 patients from across the US. The data mining approach used was CART (Classification and Regression Trees). Our aim was twofold: 1) determining the drivers of home healthcare service outcomes (discharge destination and length of stay) and 2) examining the applicability of induction through data mining to home healthcare data. RESULTS: Patient age (85 and older) was a driving force in discharge destination and length of stay for all three conditions. There were also impacts from the type of agency, type of payment, and ethnicity. CONCLUSION: Patients over 85 years of age experience differential outcomes depending on the condition. There are also differential effects related to agency type by condition although length of stay was generally lower for hospital-based agencies. The CART procedure was sufficiently accurate in correctly classifying patients in all three conditions which suggests continuing utility in home health care

Mining Medical Data: A Case Study of Endometriosis

[[abstract]]Ultrasound guided aspiration of ovarian endometrioma had been tried as an alternative therapeutic modality in patients whose desire to avoid surgery or surgical approach is contraindicated since 1991. Cyst puncture can reduce tumor volume and destruct the cyst wall, alleviate sticking circumstances and enhance the chance of recovery. But simple aspiration without other treatments results in high recurrence rate (28.5 % to 100 %). In order to reduce recurrence after aspiration, ultrasound-guided aspiration with instillation of tetracycline, methotrexate, and recombinant interleukin-2 has been combined and proven to be effective with the recurrence rates of 46.9 %, 18.1 %, and 40 % respectively. Noma et al. (2001) reported that conduct of ethanol instillation for more than 10 min particularly for a case with a single endometrial cyst is considered most effective from the standpoint of recurrence (14.9 %). Our goal is to analyze patients with recurrent pelvic cyst who underwent surgical intervention. The research data are based on clinical diagnosis, symptoms and medical intervention classification, and the cyst numbers are defined as forecast project target. The decision tree, methodology of data mining technology, is used to find the meaningful characteristic as well as each other mutually connection. The experimental result can help the clinical faculty doctors to better diagnose and provide treatment reference for future patients.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Lesion detection in demoscopy images with novel density-based and active contour approaches

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. </p> <p>Results</p> <p>To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.</p> <p>Conclusion</p> <p>We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution <abbrgrp><abbr bid="B27">27</abbr></abbrgrp> of a specific form of the Geometric Heat Partial Differential Equation <abbrgrp><abbr bid="B28">28</abbr></abbrgrp>. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.</p

Differential Matrix Rigidity Response in Breast Cancer Cell Lines Correlates with the Tissue Tropism

Metastasis to a variety of distant organs, such as lung, brain, bone, and liver, is a leading cause of mortality in the breast cancer patients. The tissue tropism of breast cancer metastasis has been recognized and studied extensively, but the cellular processes underlying this phenomenon, remain elusive. Modern technologies have enabled the discovery of a number of the genetic factors determining tissue tropism of malignant cells. However, the effect of these genetic differences on the cell motility and invasiveness is poorly understood. Here, we report that cellular responses to the mechanical rigidity of the extracellular matrix correlate with the rigidity of the target tissue. We tested a series of single cell populations isolated from MDA-MB-231 breast cancer cell line in a variety of assays where the extracellular matrix rigidity was varied to mimic the environment that these cells might encounter in vivo. There was increased proliferation and migration through the matrices of rigidities corresponding to the native rigidities of the organs where metastasis was observed. We were able to abolish the differential matrix rigidity response by knocking down Fyn kinase, which was previously identified as a critical component of the FN rigidity response pathway in healthy cells. This result suggests possible molecular mechanisms of the rigidity response in the malignant cells, indicating potential candidates for therapeutic interventions

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

[[abstract]]Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子

Mitochondrial DNA Backgrounds Might Modulate Diabetes Complications Rather than T2DM as a Whole

Mitochondrial dysfunction has been implicated in rare and common forms of type 2 diabetes (T2DM). Additionally, rare mitochondrial DNA (mtDNA) mutations have been shown to be causal for T2DM pathogenesis. So far, many studies have investigated the possibility that mtDNA variation might affect the risk of T2DM, however, when found, haplogroup association has been rarely replicated, even in related populations, possibly due to an inadequate level of haplogroup resolution. Effects of mtDNA variation on diabetes complications have also been proposed. However, additional studies evaluating the mitochondrial role on both T2DM and related complications are badly needed. To test the hypothesis of a mitochondrial genome effect on diabetes and its complications, we genotyped the mtDNAs of 466 T2DM patients and 438 controls from a regional population of central Italy (Marche). Based on the most updated mtDNA phylogeny, all 904 samples were classified into 57 different mitochondrial sub-haplogroups, thus reaching an unprecedented level of resolution. We then evaluated whether the susceptibility of developing T2DM or its complications differed among the identified haplogroups, considering also the potential effects of phenotypical and clinical variables. MtDNA backgrounds, even when based on a refined haplogroup classification, do not appear to play a role in developing T2DM despite a possible protective effect for the common European haplogroup H1, which harbors the G3010A transition in the MTRNR2 gene. In contrast, our data indicate that different mitochondrial haplogroups are significantly associated with an increased risk of specific diabetes complications: H (the most frequent European haplogroup) with retinopathy, H3 with neuropathy, U3 with nephropathy, and V with renal failure