Supporting medical decisions for treating rare diseases through genetic programming

Bakurov, I., Castelli, M., Vanneschi, L., & Freitas, M. J. (2019). Supporting medical decisions for treating rare diseases through genetic programming. In P. Kaufmann, & P. A. Castillo (Eds.), Applications of Evolutionary Computation: 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings (pp. 187-203). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11454 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-16692-2_13. ISBN: 978-3-030-16691-5; Online ISBN: 978-3-030-16692-2Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.authorsversionpublishe

The Status of Health Information Delivery in the United States: The Role of Libraries in the Complex Health Care Environment

published or submitted for publicatio

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

The Edna McConnell Clark Foundation's Tropical Disease Research Program: A 25-Year Retrospective Review 1976-1999

Documents and details the foundation's commitment to the program from its inception, and provides an analysis of its successes until the completion of the program in 1999

Outlook Magazine, Autumn 2011

https://digitalcommons.wustl.edu/outlook/1184/thumbnail.jp

Custom Made Versus Ready to Wear Treatments; Behavioral Propensities in Physician's Choices

To customize treatments to individual patients entails costs of coordination and cognition. Thus, providers sometimes choose treatments based on norms for broad classes of patients. We develop behavioral hypotheses explaining when and why doctors customize to the particular patient, and when instead they employ "ready-to-wear" treatments. Our empirical studies examining length of office visits and physician prescribing behavior find evidence of norm-following behavior. Some such behavior, from our studies and from the literature, proves sensible; but other behavior seems far from optimal.

The Value Proposition for Pathologists: A Population Health Approach

© The Author(s) 2020. The transition to a value-based payment system offers pathologists the opportunity to play an increased role in population health by improving outcomes and safety as well as reducing costs. Although laboratory testing itself accounts for a small portion of health-care spending, laboratory data have significant downstream effects in patient management as well as diagnosis. Pathologists currently are heavily engaged in precision medicine, use of laboratory and pathology test results (including autopsy data) to reduce diagnostic errors, and play leading roles in diagnostic management teams. Additionally, pathologists can use aggregate laboratory data to monitor the health of populations and improve health-care outcomes for both individual patients and populations. For the profession to thrive, pathologists will need to focus on extending their roles outside the laboratory beyond the traditional role in the analytic phase of testing. This should include leadership in ensuring correct ordering and interpretation of laboratory testing and leadership in population health programs. Pathologists in training will need to learn key concepts in informatics and data analytics, health-care economics, public health, implementation science, and health systems science. While these changes may reduce reimbursement for the traditional activities of pathologists, new opportunities arise for value creation and new compensation models. This report reviews these opportunities for pathologist leadership in utilization management, precision medicine, reducing diagnostic errors, and improving health-care outcomes

The Drug Repurposing Ecosystem: Intellectual Property Incentives, Market Exclusivity, and the Future of New Medicines

The pharmaceutical industry is in a state of fundamental transition. New drug approvals have slowed, patents on blockbuster drugs are expiring, and costs associated with developing new drugs are escalating and yielding fewer viable drug candidates. As a result, pharmaceutical firms have turned to a number of alternative strategies for growth. One of these strategies is drug repurposing -finding new ways to deploy approved drugs or abandoned clinical candidates in new disease areas. Despite the efficiency advantages of repurposing drugs, there is broad agreement that there is insufficient repurposing activity because of numerous intellectual property protection and market failures. This Article examines the system that surrounds drug repurposing, including serendipitous discovery, the application of big data methods to prioritize promising repurposing candidates, the unorthodoxly regulated off-label prescription practices of providers, and related prohibitions on pharmaceutical firms\u27 off-label marketing. The Article argues that there is a complex ecosystem in place and that additional or disruptive IP or market exclusivity incentives may harm as much as help in promoting repurposing activity. To illustrate this threat, the Article traces the trajectory of metformin, a common diabetes drug that shows promise for conditions ranging from polycystic ovary syndrome to breast cancer. From the initial reasons for Bristol-Myers Squibb to refuse to invest in promising alternative uses, to the institutions, researchers, and regulators who identified possibilities for metformin treatment, this Article aims to map the role of intellectual property protection, market exclusivity, and search for capital that led to metformin\u27s ascent as a repurposed drug. The Article contributes a concrete understanding to an important problem in pharmaceutical law and policy, one for which scholars have quickly suggested more powerful patent and market exclusivity protection when doing so may undermine the very processes now leading to effective alternative uses for existing drugs

Clinical Utility of Pharmacogenomic Testing to Support Prescriptive Decision Making Among Anesthesia Providers: A Mixed-Method Study

Anesthesia care is delivered world wide on a daily basis. Provision of anesthesia cares for surgical, obstetrical, or pain management procedures mandate a thorough understanding of physiology, pathophysiology, and pharmacology. Nearly 4 million anesthetics are delivered in the United States each year and the impact of genetics on anesthesia care is becoming greater. Anesthesia providers make prescriptive decisions based on an individual patient\u27s disease processes, proposed surgical or therapeutic procedure, and a thorough clinical history. The age of personalized medicine is upon us and the ability to use genetic testing to help predict how a patient will respond to various medications is here. By using genetically coded single nucleotide polymorphism programming of the metabolic pathways in the liver, drugs responsiveness can be more precisely predicted and explained. This dissertation focuses on the clinical utility of genetic testing to predict drug responsiveness (pharmacogenomics) among anesthesia providers with a focus on treating acute pain. Specifically, the following research question is addressed: What is the clinical utility of pharmacogenomic testing to support prescriptive decision making among anesthesia providers. To answer this research question, a mixed-method sequential qualitative quantitative study was carried out. The conclusions of this research are (a) anesthesia providers lack knowledge concerning pharmacogenomic testing, (b) anesthesia providers are concerned about potential ethical and economic issues surrounding genetic testing, and (c) anesthesia providers perceive a potential benefit to using pharmacogenomic testing as it relates to making prescriptive decisions. Further work is necessary to more carefully refine the instrument used to measure clinical utility as well as future intervention work aimed at increasing anesthesia provider knowledge about pharmacogenomic testing