A similarity measure for case based reasoning modeling with temporal abstraction based on cross-correlation

Methods for a similarity measure for clinical attributes based on survival data analysis

Background

Case-based reasoning is a proven method that relies on learned cases from the past for decision support of a new case. The accuracy of such a system depends on the applied similarity measure, which quantifies the similarity between two cases. This work proposes a collection of methods for similarity measures especially for comparison of clinical cases based on survival data, as they are available for example from clinical trials.

Methods

Our approach is intended to be used in scenarios, where it is of interest to use longitudinal data, such as survival data, for a case-based reasoning approach. This might be especially important, where uncertainty about the ideal therapy decision exists. The collection of methods consists of definitions of the local similarity of nominal as well as numeric attributes, a calculation of attribute weights, a feature selection method and finally a global similarity measure. All of them use survival time (consisting of survival status and overall survival) as a reference of similarity. As a baseline, we calculate a survival function for each value of any given clinical attribute.

Results

We define the similarity between values of the same attribute by putting the estimated survival functions in relation to each other. Finally, we quantify the similarity by determining the area between corresponding curves of survival functions. The proposed global similarity measure is designed especially for cases from randomized clinical trials or other collections of clinical data with survival information. Overall survival can be considered as an eligible and alternative solution for similarity calculations. It is especially useful, when similarity measures that depend on the classic solution-describing attribute “applied therapy” are not applicable. This is often the case for data from clinical trials containing randomized arms.

Conclusions

In silico evaluation scenarios showed that the mean accuracy of biomarker detection in k = 10 most similar cases is higher (0.909–0.998) than for competing similarity measures, such as Heterogeneous Euclidian-Overlap Metric (0.657–0.831) and Discretized Value Difference Metric (0.535–0.671). The weight calculation method showed a more than six times (6.59–6.95) higher weight for biomarker attributes over non-biomarker attributes. These results suggest that the similarity measure described here is suitable for applications based on survival data.

An incremental knowledge acquisition-based system for supporting decisions in biomedical domains

In Intensive Care Units doctors have to manage several alarm situations in patients. When a doctor analyzes the state of the patient, (s)he has to decide if there is an alarm situation and make decisions about what actions to perform. It is desirable to detect these situations before they occur, because the solution could be easier and the doctor has more time to react. An intelligent system could analyze the information, extract conclusions, format and order the causes leading to the severe condition. This would be helpful for a doctor, and would make the decision-making process easier. A system capable of performing such operations is presented here. This is not a diagnosis application but a tool to detect alarm situations for patient safety. A prototype capable of making retrospective evaluation of the condition of the patients has been developed. This system is based on the MCRDR technology, which has been extended to deal with the requirements of this domain. The evaluation of the system is also reported in this paper.

Temporal reasoning with medical data--a review with emphasis on medical natural language processing

Temporal information is crucial in electronic medical records and biomedical information systems. Processing temporal information in medical narrative data is a very challenging area. It lies at the intersection of temporal representation and reasoning (TRR) in artificial intelligence and medical natural language processing (MLP). Some fundamental concepts and important issues in relation to TRR have previously been discussed, mainly in the context of processing structured data in biomedical informatics; however, it is important that these concepts be re-examined in the context of processing narrative data using MLP. Theoretical and methodological TRR studies in biomedical informatics can be classified into three main categories: category 1 applies theories and models from temporal reasoning in AI; category 2 defines frameworks that meet needs from clinical applications; category 3 resolves issues such as temporal granularity and uncertainty. Currently, most MLP systems are not designed with a formal representation of time, and their ability to reason about temporal relations among medical events is limited. Previous work in processing time with clinical narrative data includes processing time in clinical reports, modeling textual temporal expressions in clinical databases, processing time in clinical guidelines, and building time standards for data exchange and integration. In addition to common problems in MLP, there are challenges specific to TRR in medical text, which occur at each level of linguistic structure and analysis. Despite advances in temporal reasoning in biomedical informatics, processing time in medical text deserves more attention. Besides the need for more research in temporal granularity, fuzzy time, temporal contradiction, intermittent events and uncertainty, broad areas for future research include enhancing functions of current MLP systems on processing temporal information, incorporating medical knowledge into temporal reasoning systems, resolving coreference, integrating narrative data with structured data and evaluating these systems.

Exploration of quantitative scoring metrics to compare systems biology modeling approaches

In this paper, we report a focused case study to assess whether quantitative metrics are useful to evaluate molecular-level system biology models on cellular metabolism. Ideally, the bio-modeling community shall be able assess systems biology models based on objective and quantitative metrics. This is because metric-based model design not only can accelerate the validation process, but also can improve the efficacy of model design. In addition, the metric will enable researchers to select models with any desired quality standards to study biological pathway. In this case study, we compare popular systems biology modeling approaches such as Michaelis-Menten kinetics and generalized mass action and flux balance analysis to examine the difficulties in developing quantitative metrics for bio-model assessment. We created a set of guidelines in evaluating the efficacy of various bio-modeling approaches and system analysis in several "bio-systems of interest". We found that quantitative scoring metrics are essential aids for (i) model adopters and users to determine fundamental distinctions among bio-models, and (ii) model developers to improve key areas in bio-modeling. Eventually, we want to extend this evaluation practice to broad systems biology modeling.