More Survival Data Mining of Multiple Time of Endpoints

2010 ◽  
pp. 193-214
Author(s):  
Patricia Cerrito ◽  
John Cerrito
Keyword(s):  
Data Mining ◽  
Survival Analysis ◽  
Organ Failure ◽  
Chronic Diseases ◽  
Survival Data ◽  
Treatment Guidelines ◽  
Multiple Time ◽  
Survival Distribution ◽  
Patients With Diabetes ◽  
Multiple Recurrences

Survival analysis is almost always reserved for an endpoint of mortality or recurrence. (Mantel, 1966) However, it can be used for many different types of endpoints as the survival distribution is defined as the time to an event. That event can be any endpoint of interest. For patients with chronic diseases, there are many endpoints to examine. For example, patients with diabetes want to avoid organ failure as well as death, and treatments that can prolong the time to organ failure will be beneficial. For patients with resistant infections, treatments that prevent one or multiple recurrences should be explored. Survival data mining differs from survival analysis in that multiple patient events can occur in sequence. The first step in survival data mining is to define an episode of treatment so that the patient events can be found for analysis. It can be thought of as a sequence of survival functions. In this chapter, we will look at the time to a switch in medications, and contrast how prescriptions are given to patients, either following or disregarding treatment guidelines.

Diabetes Mellitus in Patients With Cystic Fibrosis: Effect on Survival

PEDIATRICS ◽  
1990 ◽  
Vol 86 (3) ◽  
pp. 374-377
Author(s):  
J. Reisman ◽  
M. Corey ◽  
G. Canny ◽  
H. Levison
Keyword(s):  
Diabetes Mellitus ◽  
Cystic Fibrosis ◽  
Survival Analysis ◽  
Nutritional Status ◽  
Pulmonary Function ◽  
Survival Data ◽  
Diabetes Diagnosis ◽  
Patients With Diabetes ◽  
Insulin Dependent ◽  
Sick Children

Patient data obtained from the cystic fibrosis clinic of the Hospital for Sick Children (Toronto, Canada) over the period 1977 to 1988 were analyzed to compare the diabetic and nondiabetic cystic fibrosis patients. The pulmonary function, nutritional status, and survival data for 713 patients who attended the clinic over the 11-year period are reported. Insulin-dependent diabetes was found to exist in 37 (5.2%) of 713 patients. The patient age at time of diabetes diagnosis ranged from 2 to 34 years, with a mean ± SD of 20.0 ± 7.4 years. Patients who died in both the diabetic and nondiabetic groups had worse pulmonary and nutritional status than the surviving patients, but there were no significant differences between the diabetic and nondiabetic groups in those who died or in those who remained alive. Survival analysis showed a similar prognosis in the diabetic and nondiabetic groups. It is concluded that cystic fibrosis patients with diabetes are, for their age, not different from patients without diabetes with respect to pulmonary function, nutritional status, and survival.

Survival Analysis and Data Mining

2011 ◽  
pp. 1077-1082
Author(s):  
Qiyang Chen ◽  
Alan Oppenheim ◽  
Dajin Wang
Keyword(s):  
Data Mining ◽  
Survival Analysis ◽  
Failure Analysis ◽  
Survival Data ◽  
Event History Analysis ◽  
Real Life ◽  
Event Analysis ◽  
History Analysis ◽  
Lifetime Data Analysis ◽  
The Times

Survival analysis (SA) consists of a variety of methods for analyzing the timing of events and/or the times of transition among several states or conditions. The event of interest can happen at most only once to any individual or subject. Alternate terms to identify this process include Failure Analysis (FA), Reliability Analysis (RA), Lifetime Data Analysis (LDA), Time to Event Analysis (TEA), Event History Analysis (EHA), and Time Failure Analysis (TFA), depending on the type of application for which the method is used (Elashoff, 1997). Survival Data Mining (SDM) is a new term that was coined recently (SAS, 2004). There are many models and variations of SA. This article discusses some of the more common methods of SA with real-life applications. The calculations for the various models of SA are very complex. Currently, multiple software packages are available to assist in performing the necessary analyses much more quickly.

scholarly journals Personalized treatment options for chronic diseases using precision cohort analytics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenney Ng ◽  
Uri Kartoun ◽  
Harry Stavropoulos ◽  
John A. Zambrano ◽  
Paul C. Tang
Keyword(s):  
Decision Making ◽  
Chronic Diseases ◽  
Primary Care Physicians ◽  
Treatment Guidelines ◽  
Point Of Care ◽  
Data Extraction ◽  
Treatment Options ◽  
Personalized Treatment ◽  
Medical Decision ◽  
Similarity Model

AbstractTo support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.

scholarly journals Questions of complex treatment of extensive small pelvis phlegmons in patients with diabetes mellitus

2019 ◽  
Vol 41 part 3 (2) ◽  
pp. 21-24
Author(s):  
N. N. Veligotskiy ◽  
A. S. Trushin ◽  
A. I. Seroshtanov ◽  
A. A. Sheptukha ◽  
I. Ye. Bugakov ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Multiple Organ Failure ◽  
Organ Failure ◽  
Multiple Organ ◽  
Complex Treatment ◽  
Ozone Therapy ◽  
Treatment Results ◽  
Results Of Treatment ◽  
Patients With Diabetes

The complex treatment results of 127 patients with extensive purulent processes in diabetes mellitus that were treated in our clinic with ozone therapy and ultrasonic cavitations at 2001–2018 are presents. The problems of the course of the disease and the aggravating influence of the accompanying pathology on it are noted. The options improving the results of treatment considered.Keywords: phlegmon, small pelvis, diabetes mellitus, multiple organ failure, ozone therapy, cavitations.

scholarly journals ANALISIS SINTASAN PARAMETRIK PADA PASIEN STROKE DENGAN PENDEKATAN DISTRIBUSI WEIBULL

2019 ◽  
Vol 8 (1) ◽  
pp. 55
Author(s):  
NI MADE SRI WAHYUNI ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Body Mass Index ◽  
Survival Analysis ◽  
Weibull Distribution ◽  
Survival Data ◽  
The Body ◽  
Predictor Variables ◽  
Survival Models ◽  
Stroke Patients ◽  
Parametric Survival ◽  
Parametric Survival Analysis

Parametric survival analysis is one of the survival analysis that has a distribution of survival data that follows a certain distribution. Weibull distribution is a distribution that is often used in parametric survival analysis. The purpose of this study is to determine parametric survival models using the Weibull distribution and to determine  the factors that can influence the recovery of stroke patients. This study uses data on stroke patients in the Wangaya hospital, Denpasar in 2017. The best model obtained in this study is a model that consists of two predictor variables, namely the age and the body mass index (BMI).Therefore the  factors that can influence the recovery of stroke patients are age and BMI.

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