The Student Editorial Board of Methods of Information in Medicine--an opportunity to educate tomorrow's peer reviewers.

OBJECTIVE: Peer review is a critical process in the publication of scientific findings; trainees and young investigators, however, have few opportunities to learn systematically how to review manuscripts. Journal editors have an opportunity to engage trainees and young investigators in the peer review process early during their career. METHODS: Methods of Information in Medicine, an official journal of the International Medical Informatics Association, is initiating a Student Editorial Board. The journal invites applications from international graduate and post-doctoral training programs that have a focus on health informatics, biomedical informatics, or a related field. RESULTS: Each year up to six trainees will be invited to join the Student Editorial Board. The trainees will go through a mentored training experience that includes an active involvement in the various aspects of peer review during their one to two-year term of appointment. CONCLUSIONS: The journal expects that the Student Editorial Board will benefit trainees and young investigators in becoming skilled reviewers and engaged peers who can offer professional, constructive, and informative feedback and enhance the process of scientific communication.

Newer developments in immunohistology.

The development of sensitive reagents and detection systems, together with the introduction of heat-induced antigen retrieval, has rapidly entrenched immunohistology as an indispensable adjunct to routine histological examination, contributing to diagnosis, prognosis and treatment. New antibodies continue to be produced and new applications for "old" antibodies are described. The production of antibodies enabling the detection of genetic abnormalities, including mutations, gene amplifications and specific chromosomal translocations associated with novel chimeric proteins, promises to yield further insights into the genesis and behaviour of tumours. The ability to stain for target molecules that regulate tumour growth and proliferation is essential for selecting tumours for treatment with monoclonal antibodies. The mechanism of antigen retrieval remains debated. The absence of optimal controls continues to hinder standardisation of immunostaining and invalidates current attempts at quantification of immunostaining.

Dynamic decision analysis in medicine: a data-driven approach.

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. Two major challenges in dynamic decision analysis are on proper formulation of a model for the problem and effective elicitation of the numerous time-dependent conditional probabilities for the model. Based on a new, general dynamic decision modeling framework called DynaMoL (Dynamic decision Modeling Language), we propose a data-driven approach to addressing these issues. Our approach uses available problem data from large medical databases, guides the decision modeling at a proper level of abstraction and establishes a Bayesian learning method for automatic extraction of the probabilistic parameters. We demonstrate the theoretical implications and practical promises of this new approach to dynamic decision analysis in medicine through a comprehensive case study in the optimal follow-up of patients after curative colorectal cancer surgery.

Nonparametric techniques to extract fuzzy rules for breast cancer diagnosis problem.

This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is constructed around each prototype. Finally, these clusters are interpreted as the fuzzy rules that relate the prognostic factors and the diagnosis results. The advantages of the proposed algorithm are, (a) there is no need to know the structure of the training data, (b) the number of fuzzy rules does not increase with the increase of the number of input dimensions, and (c) small number of fuzzy rules is generated. With the three generated fuzzy rules, 96.20% classification efficiency is achieved, which is comparable to other rule generation techniques.

Constructing influence views from data to support dynamic decision making in medicine.

A dynamic decision model can facilitate the complicated decision-making process in medicine, in which both time and uncertainty are explicitly considered. In this paper, we address the problem of automatic construction of a dynamic decision model from a large medical database. Within the DynaMoL (a dynamic decision modeling language) framework, a model can be represented in influence view. Thus, our proposed approach first learns the structures of the influence view based on the minimal description length (MDL) principle, and then obtains the conditional probabilities of the model by Bayesian method. The experiment results demonstrate that our system can efficiently construct the influence views from data with high fidelity.

Modelling medical decisions in DynaMoL: a new general framework of dynamic decision analysis.

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results indicate practical promise for the framework.

Consistency management in multiple perspective medical decision analysis.

Multiple perspective reasoning is often involved in real-world decision analysis. Different perspectives may be suitable for different stages of the decision modeling process. Multiple perspective modeling calls for consistency management which ensures that the different perspectives reflect the same information. This paper summarizes the graphical perspectives currently supported in DynaMoL, a new framework for dynamic decision analysis. We introduce a new perspective, the tree view, into the framework. We present the main ideas involved in consistency management in the framework. We also discuss the critical issues involved in multiple perspective modeling of a simplified case study in medicine.

Induction of diagnostic test strategies with multi-level information measures.

This paper presents a method for inducing clinical diagnostic test protocols or strategies from data. We represent testing strategies as a strategy tree. To support induction of strategy tree, we define three information measures: K-level information, K-level information gain, K-level gain ratio, and K-level cost index, for test selection during strategy building. These measures generalize Quinlan's information measures used in decision tree induction. We present theoretical and experimental results to show that the K-level cost index can be used to induce strategy trees in a practical domain.

Toward patient-centered, personalized and personal decision support and knowledge management: a survey.

OBJECTIVE: This paper summarizes the recent trends and highlights the challenges and opportunities in decision support and knowledge management for patient-centered, personalized, and personal health care. METHODS: The discussions are based on a broad survey of related references, focusing on the most recent publications. Major advances are examined in the areas of i) shared decision making paradigms, ii) continuity of care infrastructures and architectures, iii) human factors and system design approaches, iv) knowledge management innovations, and v) practical deployment and change considerations. RESULTS: Many important initiatives, projects, and plans with promising results have been identified. The common themes focus on supporting the individual patients who are playing an increasing central role in their own care decision processes. New collaborative decision making paradigms and information infrastructures are required to ensure effective continuity of care. Human factors and usability are crucial for the successful development and deployment of the relevant systems, tools, and aids. Advances in personalized medicine can be achieved through integrating genomic, phenotypic and other biological, individual, and population level information, and gaining useful insights from building and analyzing biological and other models at multiple levels of abstraction. Therefore, new Information and Communication Technologies and evaluation approaches are needed to effectively manage the scale and complexity of biomedical and health information, and adapt to the changing nature of clinical decision support. CONCLUSION: Recent research in decision support and knowledge management combines heterogeneous information and personal data to provide cost-effective, calibrated, personalized support in shared decision making at the point of care. Current and emerging efforts concentrate on developing or extending conventional paradigms, techniques, systems, and architectures for the new predictive, preemptive, and participatory health care model for patient-centered, personalized medicine. There is also an increasing emphasis on managing complexity with changing care models, processes, and settings.

Methods in year 50: preserving the past and preparing for the future.

BACKGROUND: Founded in 1962 and, therefore, the oldest international journal in medical informatics, Methods of Information in Medicine will publish its 50th volume in 2011. At the start of the journal's sixth decade, a discussion on the journal's profile seems appropriate. OBJECTIVES: To report on the new opportunities for online access to Methods publications as well as on the recent strategic decisions regarding the journal's aims and editorial policies. METHODS: Describing and analyzing the journal's aims and scope. Reflecting on recent publications and on the journal's development during the last decade. RESULTS: From 2011 forward all articles of Methods from 1962 until the present can be accessed online. Methods of Information in Medicine stresses the basic methodology and scientific fundamentals of processing data, information and knowledge in medicine and health care. Although the journal's major focus is on publications in medical informatics, it has never been restricted to publications only in this discipline. For example, articles in medical biometry, in or close to biomedical engineering, and, later, articles in bioinformatics continue to be a part of this journal. CONCLUSIONS: There is a continuous and, as it seems, ever growing overlap in the research methodology and application areas of the mentioned disciplines. As there is a continuing and even growing need for such a publication forum, Methods of Information in Medicine will keep its broad scope. As an organizational consequence, the journal's number of associate editors has increased accordingly.

The birth and evolution of a discipline devoted to information in biomedicine and health care. As reflected in its longest running journal.

BACKGROUND: The journal Methods of Information in Medicine, founded in 1962, has now completed its 50th volume. Its publications during the last five decades reflect the formation of a discipline that deals with information in biomedicine and health care. OBJECTIVES: To report about 1) the journal's origin, 2) the individuals who have significantly contributed to it, 3) trends in the journal's aims and scope, 4) influential papers and 5) major topics published in Methods over the years. METHODS: Methods included analysing the correspondence and journal issues in the archives of the editorial office and of the publisher, citation analysis using the ISI and Scopus databases, and analysing the articles' Medical Subject Headings (MeSH) in MEDLINE. RESULTS: In the journal's first 50 years 208 editorial board members and/or editors contributed to the journal's development, with most individuals coming from Europe and North America. The median time of service was 11 years. At the time of analysis 2,456 articles had been indexed with MeSH. Topics included computerized systems of various types, informatics methodologies, and topics related to a specific medical domain. Some MeSH topic entries were heavily and regularly represented in each of the journal's five decades (e.g. information systems and medical records), while others were important in a particular decade, but not in other decades (e.g. punched-card systems and systems integration). Seven papers were cited more than 100 times and these also covered a broad range of themes such as knowledge representation, analysis of biomedical data and knowledge, clinical decision support and electronic patient records. CONCLUSIONS: Methods of Information in Medicine is the oldest international journal in biomedical informatics. The journal's development over the last 50 years correlates with the formation of this new discipline. It has and continues to stress the basic methodology and scientific fundamentals of organizing, representing and analysing data, information and knowledge in biomedicine and health care. It has and continues to stimulate multidisciplinary communication on research that is devoted to high-quality, efficient health care, to quality of life and to the progress of biomedicine and the health sciences.

50 years of informatics research on decision support: what's next.

OBJECTIVES: To reflect on the history, status, and future trends of decision support in health and biomedical informatics. To highlight the new challenges posed by the complexity and diversity of genomic and clinical domains. To examine the emerging paradigms for supporting cost-effective, personalized decision making. METHODS: A group of international experts in health and biomedical informatics presented their views and discussed the challenges and issues on decision support at the Methods of Information in Medicine 50th anniversary symposium. The experts were invited to write short articles summarizing their thoughts and positions after the symposium. RESULTS AND CONCLUSIONS: The challenges posed by the complexity and diversity of the domain knowledge, system infrastructure, and usage pattern are highlighted. New requirements and computational paradigms for representing, using, and acquiring biomedical knowledge and healthcare protocols are proposed. The underlying common themes identified for developing next-generation decision support include incorporating lessons from history, uniform vocabularies, integrative interfaces, contextualized decisions, personalized recommendations, and adaptive solutions.

Apoptosis is a major cause of so-called "caseous necrosis" in mycobacterial granulomas in HIV-infected patients.

AIM: To demonstrate that so-called "caseous necrosis" is the result of apoptosis and investigate the association of B and T cells, and macrophages with the granulomas and their relationship to some apoptosis-related proteins. METHODS: Cervical lymph node biopsy specimens from 55 HIV-infected Thai patients with caseating granulomas, confluent caseating granulomas, sarcoid-like granulomas, foamy macrophage response, pseudo-inflammatory tumour response or non-specific lymphoid hyperplasia were examined histologically and for apoptosis by immunostaining for caspase 3 and TUNEL. Classic tuberculoid caseating granulomas in cervical lymph node and lungs from non-HIV-infected patients were also stained with caspase 3. RESULTS: All areas of caseous necrosis frequently displayed extensive apoptosis that readily accounted for the so-called "necrosis". Small foci of apoptosis were present in the other reaction patterns and fibrotic granulomas often showed residual apoptosis. The extent of apoptosis was inversely related to the numbers of identifiable acid-fast bacilli; all epithelioid macrophages revealed strong immunoexpression of the pro-apoptotic proteins Bax and Fas, whereas the anti-apoptotic protein Bcl-2 was not present. Apoptosis occurred in CD68+ macrophages and CD3+ CD8+ T cells; all nodes were deficient of CD4+ cells. CD8+ T cells were intimately related to the apoptotic foci, suggesting a role in the process, particularly in the absence of CD4+ cells. In non-HIV-infected cases, similar extensive apoptosis was confirmed with caspase 3. CONCLUSIONS: So-called "caseous necrosis" is shown for the first time to be the result of apoptosis. In the absence of CD4+ cells the findings negate many of the postulated mechanisms of apoptosis in the murine model and have implications for the treatment of mycobacterial infections.

Free and open source enabling technologies for patient-centric, guideline-based clinical decision support: a survey.

OBJECTIVES: Guideline-based clinical decision support is an emerging paradigm to help reduce error, lower cost, and improve quality in evidence-based medicine. The free and open source (FOS) approach is a promising alternative for delivering cost-effective information technology (IT) solutions in health care. In this paper, we survey the current FOS enabling technologies for patient-centric, guideline-based care, and discuss the current trends and future directions of their role in clinical decision support. METHODS: We searched PubMed, major biomedical informatics websites, and the web in general for papers and links related to FOS health care IT systems. We also relied on our background and knowledge for specific subtopics. We focused on the functionalities of guideline modeling tools, and briefly examined the supporting technologies for terminology, data exchange and electronic health record (EHR) standards. RESULTS: To effectively support patient-centric, guideline-based care, the computerized guidelines and protocols need to be integrated with existing clinical information systems or EHRs. Technologies that enable such integration should be accessible, interoperable, and scalable. A plethora of FOS tools and techniques for supporting different knowledge management and quality assurance tasks involved are available. Many challenges, however, remain in their implementation. CONCLUSIONS: There are active and growing trends of deploying FOS enabling technologies for integrating clinical guidelines, protocols, and pathways into the main care processes. The continuing development and maturation of such technologies are likely to make increasingly significant contributions to patient-centric, guideline-based clinical decision support.

Representation requirements for supporting knowledge-based construction of decision models in medicine.

This paper analyzes the medical knowledge required for formulating decision models in the domain of pulmonary infectious diseases (PIDs) with acquired immunodeficiency syndrome (AIDS). Aiming to support dynamic decision-modeling, the knowledge characterization focuses on the ontology of the clinical decision problem. Relevant inference patterns and knowledge types are identified.

Dynamic decision modeling in medicine: a critique of existing formalisms.

Dynamic decision models are frameworks for modeling and solving decision problems that take into explicit account the effects of time. These formalisms are based on structural and semantical extensions of conventional decision models, e.g., decision trees and influence diagrams, with the mathematical definitions of finite-state semi-Markov processes. This paper identifies the common theoretical basis of existing dynamic decision modeling formalisms, and compares and contrasts their applicability and efficiency. It also argues that a subclass of such dynamic decision problems can be formulated and solved more effectively with non-graphical techniques. Some insights gained from this exercise on automating the dynamic decision making process are summarized.