Conceptual graph operations for formal visual reasoning in the medical domain

Matching diagnostics development to clinical need: Target product profile development for a point of care test for community-acquired lower respiratory tract infection

Background

Point of care tests (POCTs) are increasingly being promoted for guiding the primary medical care of community acquired lower respiratory tract infections (CA-LRTI). POCT development has seldom been guided by explicitly identified clinical need and requirements of the intended users. Approaches for identifying POCT priorities and developing target product profiles (TPPs) for POCTs in primary medical care are not well developed, and there is no published TPP for a CA-LRTI POCT aimed at developed countries.

Methods

We conducted workshops with expert stakeholders and a survey with primary care clinicians to produce a target product profile (TPP) to guide the development of a clinically relevant and technologically feasible POCT for CA-LRTI.

Results

Participants with clinical, academic, industrial, technological and basic scientific backgrounds contributed to four expert workshops, and 45 practicing primary care clinicians responded to an online survey and prioritised community-acquired pneumonia (CAP) as the CA-LRTI where a new POCT was most urgently needed. Consensus was reached on a TPP document that included information on the intended niche in the clinical pathway in primary medical care; diagnostic product specification (intended use statement and test concept), and minimum and ideal user specifications. Clinicians minimum requirements of a CA-LRTI POCT included the use of minimally invasive samples, a result in less than 30 minutes, no more than a single preparation step, minimum operational requirements, and detection of common respiratory pathogens and their resistance to commonly prescribed antibiotics.

Conclusions

This multidisciplinary, multistage partnership approach generated a clinically-driven TPP for guiding the development of a new POCT, and this approach as well as the TPP itself may be useful to others developing a new POCT.

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

BACKGROUND AND OBJECTIVE

We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic health records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives.

METHODS

This review surveys the methods used in three important area: modeling and representing of time, medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations.

RESULTS

The main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations.

CONCLUSIONS

Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

Telemedicine framework using case-based reasoning with evidences

Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services. This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity. In addition to being a precious support in the procedure of medical decision-making, this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications. Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health. In this paper we enrich the case-based reasoning process by taking into account former evidence-based knowledge. We use the regular four steps approach and implement an additional (iii) step: (i) establish diagnosis, (ii) retrieve treatment, (iii) apply evidence, (iv) adaptation, (v) retain. Each step is performed using tools from knowledge engineering and information processing (natural language processing, ontology, indexation, algorithm, etc.). The case representation is done by the taxonomy component of a medical ontology model. The proposed approach is illustrated with an example from the oncology domain. Medical ontology allows a good and efficient modeling of the patient and his treatment. We are pointing up the role of evidences and specialist's opinions in effectiveness and safety of care.

Combining conceptual graphs and argumentation for aiding in the teleexpertise

Current medical information systems are too complex to be meaningfully exploited. Hence there is a need to develop new strategies for maximising the exploitation of medical data to the benefit of medical professionals. It is against this backdrop that we want to propose a tangible contribution by providing a tool which combines conceptual graphs and Dung׳s argumentation system in order to assist medical professionals in their decision making process. The proposed tool allows medical professionals to easily manipulate and visualise queries and answers for making decisions during the practice of teleexpertise. The knowledge modelling is made using an open application programming interface (API) called CoGui, which offers the means for building structured knowledge bases with the dedicated functionalities of graph-based reasoning via retrieved data from different institutions (hospitals, national security centre, and nursing homes). The tool that we have described in this study supports a formal traceable structure of the reasoning with acceptable arguments to elucidate some ethical problems that occur very often in the telemedicine domain.