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)

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Addicting via hashtags: How is Twitter making addiction?

Persons, substances, bodies, consumption: an ever widening process of ‘‘addicting’’ is underway in Western societies. In this article, we turn our attention to the production of addiction on the microblogging social media platform, Twitter, as an important emerging site in which the addicting of contemporary societies is also occurring. Our analysis explores two questions. First, we investigate the ways in which addiction is enacted via Twitter. How is addiction being made on Twitter? Second, we ask how the technology of Twitter itself is shaping meaning: how do the technological ‘‘affordances’’ of Twitter help constitute the kinds of addiction being materialized? While we find a multiplicity of meanings in the 140-character messages, we also find a pattern: a tendency toward extremes—addiction riven between pain and pleasure. In addition, we find significant areas of commonality between approaches and notable silences around alternatives to common understandings of addiction. We argue that the constraints on communication imposed by Twitter technology afford a ‘‘shorthand’’ of addiction that is both revealing and productive. Illuminated is the importance of addiction as a piece of cultural shorthand that draws on and simultaneously reproduces simplistic, reductive addiction objects. In concluding, we consider what these realities of addiction being enacted through Twitter can tell us about contemporary conditions of possibility for drug use in society and for individual subjectivities and experiences

Estimating the heterogeneous relationship between peer drinking and youth alcohol consumption in Chile using propensity score stratification

When estimating the association between peer and youth alcohol consumption, it is critical to account for possible differential levels of response to peer socialization processes across youth, in addition to variability in individual, family, and social factors. Failure to account for intrinsic differences in youth's response to peers may pose a threat of selection bias. To address this issue, we used a propensity score stratification method to examine whether the size of the association between peer and youth drinking is contingent upon differential predicted probabilities of associating with alcohol-consuming friends. Analyzing a Chilean youth sample (N = 914) of substance use, we found that youths are susceptible to the detrimental role of peer drinkers, but the harmful relationship with one's own drinking behavior may be exacerbated among youth who already have a high probability of socializing with peers who drink. In other words, computing a single weighted-average estimate for peer drinking would have underestimated the detrimental role of peers, particularly among at-risk youths, and overestimated the role of drinking peers among youths who are less susceptible to peer socialization processes. Heterogeneous patterns in the association between peer and youth drinking may shed light on social policies that target at-risk youths.We are extremely grateful to the youth and their families in Santiago, Chile for taking the time to participate in this study. This study received support from U.S. National Institute on Drug Abuse (R01 DAD21181), National Institute of Child Health and Human Development (R01-HD-074603-01), and the Vivian A. and James L. Curtis School of Social Work Research and Training Center, University of Michigan. (R01 DAD21181 - U.S. National Institute on Drug Abuse; R01-HD-074603-01 - National Institute of Child Health and Human Development; Vivian A. and James L. Curtis School of Social Work Research and Training Center, University of Michigan

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference

Dialogue games for explaining medication choices

SMT solvers can be used efficiently to search for optimal paths across multiple graphs when optimising for certain resources. In the medical context, these graphs can represent treatment plans for chronic conditions where the optimal paths across all plans under consideration are the ones which minimize adverse drug interactions. The SMT solvers, however, work as a black-box model and there is a need to justify the optimal plans in a human-friendly way. We aim to fulfill this need by proposing explanatory dialogue protocols based on computational argumentation to increase the understanding and trust of humans interacting with the system. The protocols provide supporting reasons for nodes in a path and also allow counter reasons for the nodes not in the graph, highlighting any potential adverse interactions during the dialogue.Postprin

Practical Strategies for Pharmacist Integration with Primary Care: A Workbook.

This workbook is a practical set of tips and resources to assist pharmacists in providing clinical pharmacy services to primary care providers and their patients. The content was written based on experiences in Vermont in 2014, however the topics should generalize to pharmacists in other areas

Trials, Tricks and Transparency: How Disclosure Rules Affect Clinical Knowledge

Scandals of selective reporting of clinical trial results by pharmaceutical firms have underlined the need for more transparency in clinical trials. We provide a theoretical framework which reproduces incentives for selective reporting and yields three key implications concerning regulation. First, a compulsory clinical trial registry complemented through a voluntary clinical trial results database can implement full transparency (the existence of all trials as well as their results is known). Second, full transparency comes at a price. It has a deterrence effect on the incentives to conduct clinical trials, as it reduces the firms' gains from trials. Third, in principle, a voluntary clinical trial results database without a compulsory registry is a superior regulatory tool; but we provide some qualified support for additional compulsory registries when medical decision-makers cannot anticipate correctly the drug companies' decisions whether to conduct trials.pharmaceutical firms, strategic information transmission, clinical trials, registries, results databases, scientific knowledge.