Skip to main content

Prediction

Description

Illnesses like infections, cold, influenza and so on in type 1 diabetes mellitus (T1DM) patients, can compromise the daily patient administered diabetes treatment. This in turn may result in fluctuating blood glucose concentrations, especially hyperglycemia for prolonged periods, which over time can cause serious late complications. The illness prediction project at Tromsø Telemedicine Laboratory aims to construct a prediction model that, through use of patient observable parameters, for example, blood glucose, insulin injections and body temperature, can significantly identify risk of developing illnesses, before onset of symptoms and before illness onset.

Such a model could potentially enable T1DM patients to fight the illnesses, and prepare for an adequate change in the T1DM-management earlier on.

 

Objective

To develop an illness prediction model that can predict illness in T1DM patients before onset of symptoms, using the patient’s observable parameters.

Submitted by hparton on
Description

Dengue is a mosquito-borne viral disease, and there is considerable evidence that case numbers are rising and geographical distribution of the disease is widening within the United States, and around the world. 

The accuracy and reporting frequency of dengue morbidity and mortality information varies geographically, and often is an underestimation of the actual number of dengue infections. As traditional methods of disease surveillance may not accurately capture the true impact of this disease, it is important to gather professional observations and opinions from individuals in the public health, medical, and vector control fields of practice. Prediction markets are one way of supplementing traditional surveillance and quantifying the observations and predictions of professionals in the field. 

Prediction markets have been successfully used to forecast future events, including future influenza activity. For these markets, we divided the possible outcomes for each question into multiple mutually exclusive contracts to forecast dengue-related events. This differed from many previous prediction markets that offered single sets of yes-no questions and used ‘real’ money in the form of educational grants. However, with more detailed contracts, we were able to generate more refined predictions of dengue activity.

 

Objective

The objective of this project is to use prediction markets to forecast the spread of dengue.

Submitted by hparton on
Description

Homelessness in general is a major issue in the US today. The risk factors of homelessness are myriad, including inadequate income, lack of affordable housing, mental health and substance abuse issues, lack of social support, and nonadherence to treatment/follow-up appointments. Early identification of these factors from clinical documents may help detect or even predict homelessness cases, allowing adequate intervention and prevention measures.

Objective

 We demonstrate a semi-automated approach to induce and curate lexical domain knowledge for identification of evidence and risk factors for homelessness found in VA clinical documents. This domain knowledge can be used to support training and evaluation of automated methods such as Natural Language Processing (NLP) systems for detection and prediction of homelessness among veterans. This could serve as a proxy for public health and other surveillance involving homeless individuals. Similar methods could be used to identify other conditions of interest.

Submitted by Magou on
Description

Predictionmarkets have been successfully used to forecast future events in other fields. We adapted this method to provide estimates of the likelihood of H5N1 influenza related events.

 

Objective

The purpose of this study is to compare the results of an H5N1 influenza prediction market model with a standard statistical model.

Submitted by hparton on
Description

A new TB case can be classified as: 1) a source case for transmission leading to other, secondary active TB cases; 2) a secondary case, resulting from recent transmission; or 3) an isolated case, uninvolved in recent transmission (i.e. neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, currently accepted method for the classification, DNA fingerprint analysis, takes many weeks to produce the results; therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and socio-demographic features are known to be associated with TB transmission. By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary, and isolated.

Objective

To develop and validate a prediction model which estimates the probability of a newly diagnosed tuberculosis (TB) case being involved in ongoing chain of transmission, based on the case's clinical and socio-demographic attributes available at the time of diagnosis.

Submitted by elamb on
Description

Dengue fever is endemic in over 100 countries and there are an estimated 50 - 100 million cases annually. There is no vaccine for dengue fever yet, and the mortality rate of the severe form of the disease, dengue hemorrhagic fever, ranges from 10-20% but may be greater than 40% if dengue shock occurs. A predictive method for dengue fever would forecast when and where an outbreak will occur before its emergence. This is a challenging task and truly predictive models for emerging infectious diseases are still in their infancy.

 

Objective

This paper addresses the problem of predicting high incidence rates of dengue fever in Peru several weeks in advance.

Referenced File
Submitted by elamb on
Description

Recent events have focused on the role of emerging and re-emerging diseases not only as a significant public health threat but also as a serious threat to the economy and security of nations. The lead time to detect and contain a novel emerging disease or events with public health importance has become much shorter, making developing countries particularly vulnerable to both natural and man-made threats. There is a need to develop disease surveillance systems flexible enough to adapt to the local existing infrastructure of developing countries but which will still be able to provide valid alerts and early detection of significant public health threats.

 

Objective

To determine system usefulness of the ESSENCE Desktop Edition in detecting increases in the number of dengue cases in the Philippines.

Submitted by elamb on
Description

This paper describes a method to predict syndromic data for surveillance of public health using the method of recursive least squares and a new method of correcting for the day of week effect in order to have a prediction of the background upon which detections of actual events can be computed

Submitted by elamb on
Description

Researchers have developed varied methods for forecasting influenza activity using surveillance data with predictive models, but real-world applications in public health programs are rare. To inform consideration of whether and how public health practice should incorporate influenza forecasting, we conducted a systematic review of these methods.

Objective

To assess studies of epidemiological forecasting models for human influenza activity.

Submitted by knowledge_repo… on
Description

Bordetella Pertussis outbreaks cause morbidity in all age groups, but the infection is most dangerous for young infants. Pertussis is difficult to diagnose, especially in its early stages, and definitive test results are not available for several days. Because of temporal and geographic variability of pertussis outbreaks, delay in diagnostic test results and ramifications of incorrect management decisions at the point of care, pertussis represents a prototypical disease where realtime public health surveillance data might inform, guide and improve medical decision making. Previously, we showed that diagnostic accuracy for meningitis can be improved when information about recent, local disease incidence is accounted for. Here, we quantify the contribution of epidemiologic context to a clinical prediction model for pertussis using a state public health data stream.

 

Objective

To explore the integration of epidemiological context – current population-level disease incidence data – into a clinical prediction model for pertussis.

Submitted by elamb on