Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction

The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets

Measuring the impact of ambulatory red blood cell transfusion on home functional status: study protocol for a pilot randomized controlled trial

SPIRIT 2013: SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Checklist for clinical trial protocols. (DOCX 65 kb

Use of mechanical circulatory support devices among patients with acute myocardial infarction complicated by cardiogenic shock

Importance: Mechanical circulatory support (MCS) devices, including intravascular microaxial left ventricular assist devices (LVADs) and intra-aortic balloon pumps (IABPs), are used in patients who undergo percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI) complicated by cardiogenic shock despite limited evidence of their clinical benefit. Objective: To examine trends in the use of MCS devices among patients who underwent PCI for AMI with cardiogenic shock, hospital-level use variation, and factors associated with use. Design, Setting, and Participants: This cross-sectional study used the CathPCI and Chest Pain-MI Registries of the American College of Cardiology National Cardiovascular Data Registry. Patients who underwent PCI for AMI complicated by cardiogenic shock between October 1, 2015, and December 31, 2017, were identified from both registries. Data were analyzed from October 2018 to August 2020. Exposures: Therapies to provide hemodynamic support were categorized as intravascular microaxial LVAD, IABP, TandemHeart, extracorporeal membrane oxygenation, LVAD, other devices, combined IABP and intravascular microaxial LVAD, combined IABP and other device (defined as TandemHeart, extracorporeal membrane oxygenation, LVAD, or another MCS device), or medical therapy only. Main Outcomes and Measures: Use of MCS devices overall and specific MCS devices, including intravascular microaxial LVAD, at both patient and hospital levels and variables associated with use. Results: Among the 28 304 patients included in the study, the mean (SD) age was 65.4 (12.6) years and 18 968 were men (67.0%). The overall MCS device use was constant from the fourth quarter of 2015 to the fourth quarter of 2017, although use of intravascular microaxial LVADs significantly increased (from 4.1% to 9.8%; P \u3c .001), whereas use of IABPs significantly decreased (from 34.8% to 30.0%; P \u3c .001). A significant hospital-level variation in MCS device use was found. The median (interquartile range [IQR]) proportion of patients who received MCS devices was 42% (30%-54%), and the median proportion of patients who received intravascular microaxial LVADs was 1% (0%-10%). In multivariable analyses, cardiac arrest at first medical contact or during hospitalization (odds ratio [OR], 1.82; 95% CI, 1.58-2.09) and severe left main and/or proximal left anterior descending coronary artery stenosis (OR, 1.36; 95% CI, 1.20-1.54) were patient characteristics that were associated with higher odds of receiving intravascular microaxial LVADs only compared with IABPs only. Conclusions and Relevance: This study found that, among patients who underwent PCI for AMI complicated by cardiogenic shock, overall use of MCS devices was constant, and a 2.5-fold increase in intravascular microaxial LVAD use was found along with a corresponding decrease in IABP use and a significant hospital-level variation in MCS device use. These trends were observed despite limited clinical trial evidence of improved outcomes associated with device use

A new WHO bottle bioassay method to assess the susceptibility of mosquito vectors to public health insecticides: results from a WHO-coordinated multi-centre study.

BACKGROUND: The continued spread of insecticide resistance in mosquito vectors of malaria and arboviral diseases may lead to operational failure of insecticide-based interventions if resistance is not monitored and managed efficiently. This study aimed to develop and validate a new WHO glass bottle bioassay method as an alternative to the WHO standard insecticide tube test to monitor mosquito susceptibility to new public health insecticides with particular modes of action, physical properties or both. METHODS: A multi-centre study involving 21 laboratories worldwide generated data on the susceptibility of seven mosquito species (Aedes aegypti, Aedes albopictus, Anopheles gambiae sensu stricto [An. gambiae s.s.], Anopheles funestus, Anopheles stephensi, Anopheles minimus and Anopheles albimanus) to seven public health insecticides in five classes, including pyrethroids (metofluthrin, prallethrin and transfluthrin), neonicotinoids (clothianidin), pyrroles (chlorfenapyr), juvenile hormone mimics (pyriproxyfen) and butenolides (flupyradifurone), in glass bottle assays. The data were analysed using a Bayesian binomial model to determine the concentration-response curves for each insecticide-species combination and to assess the within-bioassay variability in the susceptibility endpoints, namely the concentration that kills 50% and 99% of the test population (LC50 and LC99, respectively) and the concentration that inhibits oviposition of the test population by 50% and 99% (OI50 and OI99), to measure mortality and the sterilizing effect, respectively. RESULTS: Overall, about 200,000 mosquitoes were tested with the new bottle bioassay, and LC50/LC99 or OI50/OI99 values were determined for all insecticides. Variation was seen between laboratories in estimates for some mosquito species-insecticide combinations, while other test results were consistent. The variation was generally greater with transfluthrin and flupyradifurone than with the other compounds tested, especially against Anopheles species. Overall, the mean within-bioassay variability in mortality and oviposition inhibition were < 10% for most mosquito species-insecticide combinations. CONCLUSION: Our findings, based on the largest susceptibility dataset ever produced on mosquitoes, showed that the new WHO bottle bioassay is adequate for evaluating mosquito susceptibility to new and promising public health insecticides currently deployed for vector control. The datasets presented in this study have been used recently by the WHO to establish 17 new insecticide discriminating concentrations (DCs) for either Aedes spp. or Anopheles spp. The bottle bioassay and DCs can now be widely used to monitor baseline insecticide susceptibility of wild populations of vectors of malaria and Aedes-borne diseases worldwide

Unsupervised labeling of data for supervised learning and its application to medical claims prediction

Tyt. z nagłówka.Bibliogr. s. 213-214.The task identifying changes and irregularities in medical insurance claim payments is a difficult process of which the traditional practice involves querying historical claims databases and flagging potential claims as normal or abnormal. Because what is considered as normal payment is usually unknown and may change over time, abnormal payments often pass undetected; only to be discovered when the payment period has passed. This paper presents the problem of on-line unsupervised learning from data streams when the distribution that generates the data changes or drifts over time. Automated algorithms for detecting drifting concepts in a probability distribution of the data are presented. The idea behind the presented drift detection methods is to transform the distribution of the data within a sliding window into a more convenient distribution. Then, a test statistics p-value at a given significance level can be used to infer the drift rate, adjust the window size and decide on the status of the drift. The detected concepts drifts are used to label the data, for subsequent learning of classification models by a supervised learner. The algorithms were tested on several synthetic and real medical claims data sets.Dostępny również w formie drukowanej.KEYWORDS: unsupervised learning, concept drift, medical claims