Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Predicting Bleeding and Thrombosis Complications in Patients with Continuous Flow Left Ventricular Assist Devices

Background: Left ventricular assist device (LVAD) therapy has been proven to relieve heart failure symptoms and improve survival, but is not devoid of bleeding and/or thrombotic complications. Risk stratification tools have been utilized in other cardiovascular disease populations to estimate the risk of bleeding and thrombosis with and without anticoagulation, including the HAS-BLED, HEMORR2HAGES, CHADS2 and CHA2DS2-VASc models. The study objective was to evaluate the predictive value of available risk models for bleeding and thrombotic complications in patients with an LVAD within one year of implantation. Methods: This was a retrospective, single-center analysis of patients implanted with the HeartMate II continuous-flow LVAD from July 2011 to June 2016. All patients who received an LVAD within the study period were eligible for inclusion. The primary endpoint was the first occurrence of bleeding or thrombosis within one year from implantation. Baseline risk model scores were calculated at the time of LVAD implantation. Chi-square and student’s t-test were used to measure baseline differences and compare mean risk model scores between patients who had an event. A receiver operator characteristic (ROC) curve analysis was performed to evaluate the accuracy of the risk models to predict an event. Results: A total of 129 patients underwent LVAD implantation within the study time period. Mean CHADS2, CHA2DS2-VASc, and HAS-BLED scores were not significantly different in patients with and without an event. The mean HEMORR2HAGES score was 3.09 and 2.51 in those with and without a bleeding event, respectively (p = 0.008). The ROC curve area for the HEMORR2HAGES model was the highest at 0.620. Conclusion: The HAS-BLED, HEMORR2HAGES, CHADS2and CHA2DS2-VASc risk stratification models did not accurately predict bleeding or thrombosis events in our population. The mean HEMORR2HAGES model score was higher in patients who experienced a bleeding event. However, this model did not have strong positive predictive value. Better risk models are needed to predict bleeding and thrombotic events in this patient population

The Effects of E. Coli 0157:H7, FMD and BSE on Japanese Retail Beef Prices: A Historical Decomposition

This study examines the time-varying Japanese price reactions to the 2001 Bovine Spongiform Encephalopathy (BSE) discovery, the 2000 outbreak of foot and mouth disease (FMD), and the 1996 E. coli food po isoning events. Historical decomposition of retail-level price-series aids in explaining the behavior of beef prices in a neighborhood (period-by-period time interval) of the three events. This is based on an application of directed acyclic graphs, constructing orthogonal innovations to determine causal patterns behind contemporaneous innovations. The results show the beef safety events had different negative impacts on Japanese retail beef prices, suggesting that consumers understood and differentiated among the health risks. The results provide incentives for beef producers and retailers to proactively inform consumers about ongoing beef safety measures. Understanding consumer reaction to BSE, FMD and E. coli helps the beef industry restore consumer confidence after future food safety crises, and provides policy makers a basis for countermeasures and compensations.Japan, beef prices, BSE, FMD, E. coli, historical decomposition., Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries, Q11, Q13,

Heuristics and biases in cardiovascular disease prevention:How can we improve communication about risk, benefits and harms?

Objective Cardiovascular disease (CVD) prevention guidelines recommend medication based on the probability of a heart attack/stroke in the next 5–10 years. However, heuristics and biases make risk communication challenging for doctors. This study explored how patients interpret personalised CVD risk results presented in varying formats and timeframes. Methods GPs recruited 25 patients with CVD risk factors and varying medication history. Participants were asked to ‘think aloud’ while using two CVD risk calculators that present probabilistic risk in different ways, within a semi-structured interview. Transcribed audio-recordings were coded using Framework Analysis. Results Key themes were: 1) numbers lack meaning without a reference point; 2) risk results need to be both credible and novel; 3) selective attention to intervention effects. Risk categories (low/moderate/high) provided meaningful context, but short-term risk results were not credible if they didn’t match expectations. Colour-coded icon arrays showing the effect of age and interventions were seen as novel and motivating. Those on medication focused on benefits, while others focused on harms. Conclusion CVD risk formats need to be tailored to patient expectations and experiences in order to counteract heuristics and biases. Practice implications Doctors need access to multiple CVD risk formats to communicate effectively about CVD prevention