Denis Klimov

Intelligent Interactive Visual Exploration of Temporal Associations among Multiple Time-oriented Patient Records

OBJECTIVES To design, implement and evaluate the functionality and usability of a methodology and a tool for interactive exploration of time and value associations among multiple-patient longitudinal data and among meaningful concepts derivable from these data. METHODS We developed a new, user-driven, interactive knowledge-based visualization technique, called Temporal Association Charts (TACs). TACs support the investigation of temporal and statistical associations within multiple patient records among both concepts and the temporal abstractions derived from them. The TAC methodology was implemented as part of an interactive system, called VISITORS, which supports intelligent visualization and exploration of longitudinal patient data. The TAC module was evaluated for functionality and usability by a group of ten users, five clinicians and five medical informaticians. Users were asked to answer ten questions using the VISITORS system, five of which required the use of TACs. RESULTS Both types of users were able to answer the questions in reasonably short periods of time (a mean of 2.5 +/- 0.27 minutes) and with high accuracy (95.3 +/- 4.5 on a 0-100 scale), without a significant difference between the two groups. All five questions requiring the use of TACs were answered with similar response times and accuracy levels. Similar accuracy scores were achieved for questions requiring the use of TACs and for questions requiring the use only of general exploration operators. However, response times when using TACs were slightly longer. CONCLUSIONS TACs are functional and usable. Their use results in a uniform performance level, regardless of the type of clinical question or user group involved.

An intelligent, interactive tool for exploration and visualization of time-oriented security data

The detection of known and unknown attacks usually requires the interpretation and presentation of very large amounts of time-oriented security data. Using regular means for displaying the data, such as text or tables, is often ineffective. Furthermore, displaying only raw data is not sufficient, because the security expert is still required to derive meaningful conclusions from large amounts of data. In addition, in many cases (e.g., for detecting a virus spreading in the network), an aggregated view of multiple network devices is more effective than a view of each individual device. In this paper we propose an intelligent interface used by a distributed architecture that was described in our previous work, specific to the tasks of knowledge-based interpretation, summarization, query, visualization and interactive exploration of large numbers of time-oriented data. In order to support the interpretation and computation process, we provide automated mechanisms that perform derivation of context-specific, interval-based abstract interpretations (also known as Temporal Abstractions) from raw time-stamped security data, by using a domain-specific knowledge-base (e.g., a period of 5 hours, during the night, of a high number of FTP connections within the context of No User Activity, which might indicate the existence of a Trojan in the computer). The proposed visualization tool includes several functionalities for querying, visualization and exploration of both raw and abstracted time-oriented security data regarding single and multiple network devices.

A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge

Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians’ assessment was significantly lower than the assessment of the knowledge engineers.

MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains

MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching CIG knowledge with a highly-adaptive patient model, the parameters of which are stored in a personal health record (PHR). The PHR integrates data from hospital medical records, mobile biosensors, data entered by patients, and recommendations and abstractions output by the DSS. CIGs are customized to consider the patients’ psycho-social context and their preferences; shared decision making is supported via decision trees instantiated with patient utilities. The central DSS “projects” personalized CIG-knowledge to a mobile DSS operating on the patients’ smart phones that applies that knowledge locally. In this paper we explain the knowledge elicitation and specification methodologies that we have developed for making CIGs patient-centered and enabling their personalization. We then demonstrate feasibility, in two very different clinical domains, and two different geographic sites, as part of a multi-national feasibility study, of the full architecture that we have designed and implemented. We analyze usage patterns and opinions collected via questionnaires of the 10 atrial fibrillation (AF) and 20 gestational diabetes mellitus (GDM) patients and their care providers. The analysis is guided by three hypotheses concerning the effect of the personal patient model on patients and clinicians’ behavior and on patients’ satisfaction. The results demonstrate the sustainable usage of the system by patients and their care providers and patients’ satisfaction, which stems mostly from their increased sense of safety. The system has affected the behavior of clinicians, which have inspected the patients’ models between scheduled visits, resulting in change of diagnosis for two of the ten AF patients and anticipated change in therapy for eleven of the twenty GDM patients.

Gnaeus: utilizing clinical guidelines for knowledge-assisted visualisation of EHR cohorts

The advanced visualization of electronic health records (EHRs), supporting a scalable analysis from single patients to cohorts, intertwining patients’ conditions with executed treatments, and handling the complexity of timeoriented data, is an open challenge of visual analytics for health care. We propose an approach that, according to the knowledge-assisted visualization paradigm, leverages the domain knowledge acquired by clinical experts and formalized into computer-interpretable guidelines (CIGs), in order to improve the automated analysis, the visualization, and the interactive exploration of EHRs of patient cohorts. In this way, the analyst can get insights about the clinical history of multiple patients and assess the effectiveness of their health care treatments.