Isabelle Bichindaritz

• PHD in Computer Science, CPHIMS
• Managing Director at State University of New York at Oswego

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased acc...

Survival analysis is a field of statistics specialized in making predictions about the survival length of patients, even though it can be applied to the prediction of any future event. It is routinely used in medical research to stratify patients in groups based on risk, such as high-risk groups and low-risk groups, and has paramount important in p...

Survival analysis has currently become an essential statistical research hotspot that models the time-to-event information with data censorship handling. Such technique yields extensive applications in carcinoma treatment and prediction. Deep neural networks (DNNs) also exhibit unusual appeal for survival analysis because of their non-linear nature...

This article proposes an adaptable hybrid model for recommending effective investments in different scenarios. Currently, a wide variety of methodologies are used for company valuation, especially those that take into account financial statements. However, for private held companies, there is no method that would be capable of predicting, with full...

Survival analysis has currently become an essential statistical research hotspot that models the time-to-event information with data censorship handling. Existing studies suggest that cancer patients with different prognoses can be better stratified using multi-omics fusion analysis than using a single signature. Despite the achievements that have...

Accurately predicting the interaction between G-protein-coupled receptors (GPCR) and drugs is of great significance for understanding protein functions and drug discovery and has become a hot spot in current research. To improve the accuracy of GPCR-drug interaction prediction, this paper proposes a new GPCR-Drug interaction prediction method based...

Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic routines, and availability of powerful computers. With this increased usage comes a responsibility for education, borne equally by data scientists plying their wares i...

Despite the current standard of care, breast cancer remains one of the leading causes of mortality in women worldwide, thus emphasizing the need for better predictive and therapeutic targets. ABI1 is associated with poor survival and an aggressive breast cancer phenotype, although its role in tumorigenesis, metastasis and the disease outcome remain...

There is usually a trade-off between predictive performance and transparency, where the reasoning process behind an algorithm is shielded behind a ”black-box.” In medical domains, experts being responsible for their decisions need to understand the reasons behind machine-generated recommendations. This paper presents a transparent case-based surviv...

Motivation Integrative multi-feature fusion analysis on biomedical data has gained much attention recently. In breast cancer, existing studies have demonstrated that combining genomic mRNA data and DNA methylation data can better stratify cancer patients with distinct prognosis than using single signature. However, those existing methods are simply...

In the current era of medicine where clinicians and researchers alike are seeking to personalize treatment plans to individuals, the integration of clinical data with microarray data is surprisingly absent. With this in mind, clinical covariate data was used to pre-select previously classified breast cancer tissue, and employ these classifications...

Artificial intelligence (AI) and machine learning (ML) are now almost everywhere. Yet, i) most of us do not have a formal training on this recent topic; ii) their concepts emerge from several different scientific communities. Thus, deciphering research articles, understanding their underlying assumptions and limits remains quite challenging. To thi...

Psychiatric disorder diagnoses are heavily reliant on observable symptoms and clinical traits, the skill level of the physician, and the patient’s ability to verbalize experienced events. Therefore, researchers have sought to identify biological markers that accurately differentiate mental disorder subtypes from psychiatrically normal comparison su...

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have at...

This book constitutes the refereed post-conference proceedings of the First International Workshop on Artificial Intelligence in Health, AIH 2018, in Stockholm, Sweden, in July 2018. This workshop consolidated the workshops CARE, KRH4C and AI4HC into a single event. The 18 revised full papers included in this volume were carefully selected from th...

This work presents a novel multifaceted approach for facilitating education in data analytics. This novel approach is necessary as this new and growing discipline warrants understanding within diverse organizational arenas while recognizing that students are likely non-traditional, usually already employed in various fields and having different lev...

Case-based reasoning (CBR) systems often refer to diverse machine learning functionalities and algorithms to augment their capabilities. In this article we review the concept of case based learning and define it as the use of case based reasoning for machine learning. We present some of its characteristics and situate it in the context of the major...

Breast cancer is the most commonly diagnosed non-cutaneous cancer in American women and is estimated to cause 40,000 deaths this year. Despite standard of care, breast cancer patients often relapse after a few years of treatment thus highlighting the need for new molecular targets for improved management of metastatic disease. Abelson interactor 1...

This paper demonstrates that electrocardiogram (ECG) signals can be used to detect and classify stress in a person using as low as 5 s data stream. The analysis focuses on determining the minimum ECG data required to classify stress levels. Time taken to detect level of stress can be crucial to prevent cardiac arrest and other abnormalities. The EC...

Physiological sensor analytics aims at monitoring health as the availability of sensor-enabled portable, wearable, and implantable devices become ubiquitous in the growing Internet of Things (IoT). Physiological multi-sensor studies have been conducted previously to detect stress. In this study, we focus on electrocardiography (ECG) monitoring that...

This study aims to elucidate the role of Abelson interactor 1 (Abi1), a key protein in the WAVE regulatory complex, in mammary carcinogenesis and metastasis. Breast cancer is the second-leading cause of mortality in women in the United States with an estimated ~200,000 new cases and over 40,000 deaths this year. Despite current treatment modalities...

In hospitals in China, the rapid development of intelligent knowledge-based systems has been accompanied by the widespread adoption of case-based health knowledge systems (CBHKS). Their implementation has provided a great opportunity for Management promotions in hospitals. However, the impact of the use of CBHKS on the improvement of hospital manag...

We would like to thank all the contributing authors for their participation in this special issue and the reviewers for their hard and very valuable work and support. We would also like to thank the journal’s editorial staff for their work and assistance.

Scientific literature has been quickly expanding as the availability of articles in electronic form has increased rapidly. For the scientific researcher and the practitioner alike, keeping track with the advancement of the research is an ongoing challenge, and for the most part, the mass of experience recorded in the scientific literature is largel...

This paper presents four synergistic systems that exemplify the approaches and benefits of case-based reasoning in medical domains. It then explores how these systems couple Artificial Intelligence (AI) research with medical research and practice, integrate multiple AI and computing methodologies, leverage small numbers of available cases, reason w...

Research in case-based reasoning (CBR) in the health sciences started more than 20 years ago and has been steadily expanding during these years. This paper describes the state of the research through an analysis of its mainstream, or core, literature. The methodology followed involves first the definition of a classification and indexing scheme for...

This article reports on the main track papers, speakers, satellite events, and other activities of the Eighteenth International Conference on Case-Based Reasoning (ICCBR), held 19-22 July 2010 in Alessandria, Italy. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

Safety assessment of thermal power plants (TPP) is an important means to ensure the safety of production in thermal power production enterprises. Modern information technology can play an important role in TPP safety assessment. The evaluation of power plant systems relies, to a large extent, on the knowledge and experience of the experts undertaki...

Bioinformatics offers an interesting challenge for data mining algorithms given the high dimensionality of its data and the comparatively small set of samples. Case-based classification algorithms have been successfully applied to classify bioinformatics data and often serve as a reference for other algorithms. Therefore this paper proposes to stud...

Bioinformatics datasets are often used to compare classification algorithms for highly dimensional data. Since genetic data are becoming more and more routinely used in medical settings, researchers and life scientists alike are interested in answering such questions as finding the gene signature of a disease, classifying data for diagnosis, or eva...

Security assessment of Thermal Power Plants (TPPs) is one of the important means to guarantee the safety of production in thermal power production enterprises. Essentially, the evaluation of power plant systems relies to a large extent on the knowledge and length of experience of the experts. Therefore in this domain Case-Based Reasoning (CBR) is i...

An abstract is not available.

Security assessment of Thermal Power Plants (TPP) is one of the important means to guarantee the safety of production in thermal power production enterprises. Modern information technology may play a more important role in TPP safety assessment. Essentially, the evaluation of power plant systems relies to a large extent on the knowledge and length...

This chapter presents an introduction to the computational intelligence in medicine as well as a sample of recent advances in the field. A very brief introduction of chapters included in the book is included.

Microarray technology enables the simultaneous measurement of thousands of gene expressions, while often providing a limited set of samples. These datasets require data mining methods for classification, prediction, and clustering to be tailored to the peculiarity of this domain, marked by the so called ‘curse of dimensionality’. One main character...

The field of bioinformatics shows a tremendous growth at the crossroads of biology, medicine, information science, and computer science. Figures clearly demonstrate that today bioinformatics research is as productive as data mining research as a whole. However most bioinformatics research deals with tasks of prediction, classification, and tree or...

Case-based reasoning (CBR) is an Artificial Intelligence (AI) approach with broad applicability to building intelligent systems in health sciences domains. It represents knowledge in the form of exemplary past experiences, or cases. It is especially well suited to health sciences domains, where experience plays a major role in acquiring knowledge a...

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge...

This paper presents a system implemented to evaluate the retrieval efficiency of images when they are semantically indexed using a combination of a web ontology language and the low-level features of the image. Finding a similarity measure algorithm to retrieve images based on the semantic metadata can be very challenging due to diverse image conte...

Computational intelligence researchers have often applied their systems and methods to health sciences domains. Some of the most famous expert systems were developed in these domains. This particular interest also holds for case-based reasoning (CBR). This article first discusses the motivations for applying CBR to health sciences domains and the c...

As the amount of information available to researchers grows at an increasing rate, it becomes much more difficult to find relevant resources. An approach taken by several authoritative bodies, such as the Association for Computing Machinery and the U.S. National Library of Medicine, is the introduction of a classification scheme. However, even the...

Biomedical domains have been an application domain of choice for artificial intelligence (AI) since its pioneering years in expert systems. Some simple explanations to this phenomenon are the intellectual complexity presented by this domain, as well as the dominant industry market share of healthcare. Following in AI’s tracks, case-based reasoning...

In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based ...

This article addresses the task of mining for cases from biomedical literature to automatically build an initial case base for a case-based reasoning (CBR) system. This research takes place within the Mémoire project, which has for goal to provide a framework to facilitate building CBR systems in biology and medicine. By analyzing medical literatur...

Case-based reasoning (CBR) methodology stems from research on building computational memories capable of analogical reasoning, and require for that purpose specific composition and organization. This main task in CBR has triggered very significant research work and findings, which are summarized and analyzed in this article. In particular, since me...

Representing biomedical knowledge is an essential task in biomedical informatics intelli- gent systems. Case-based reasoning (CBR) holds the promise of representing contextual knowledge in a way that was not possible before with traditional knowledge representation and knowledge-based methods. One main issue in biomedical CBR is dealing with the ra...

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise of representing contextual knowledge in a way that was not possible before with traditional knowledge representation and knowledge-based methods. A main issue in biomedical CBR has been dealing with mai...

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise of representing contextual knowledge in a way that was not possible before with traditional knowledge representation and knowledge-based methods. One main issue in biomedical CBR is dealing with the rate...

We begin this article with an introduction of associative concept spaces and their properties and reiterate an earlier proposal for a definition of Medical Informatics. We focus on dynamic representations of knowledge and contrast them with ontological approaches that imply strong commitments to particular conceptualizations of reality. This focus...

There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third...

Mémoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retr...

This article addresses the task of mining named relationships between concepts from biomedical literature for indexing purposes or for scientific discovery from medical literature. This research builds on previous work on concept mining from medical literature for indexing purposes and proposes to learn semantic relationships names between concepts...

This article addresses the task of mining concepts from biomedical literature to index and search through a documents base. This research takes place within the Telemakus project, which has for goal to support and facilitate the knowledge discovery process by providing retrieval, visual, and interaction tools to mine and map research findings from...

An abstract is not available.

Mémoire is a framework for sharing and distributing case bases and case-based reasoning (CBR) systems in biology and medicine. This paper first introduces the semantic Web approach to build a better Web where search engines, knowledge sources and servers, applications and services can live, work, and learn in cooperation. This semantic approach is...

This paper presents current work in case-based reasoning (CBR) in the health sciences, describes current trends and issues, and projects future directions for work in this field. It represents the contributions of researchers at two workshops on case-based reasoning in the health sciences. These workshops were held at the Fifth International Confer...

This commentary summarizes case-based reasoning research applied in the medical domain. In this commentary the term is used in an all-encompassing manner. It comprises all aspects of health, for example, from diagnosis to nutrition planning. This article provides references to researchers in the field, systems, workshops, and landmark publications.

This article addresses the task of mining concepts from biomedical literature to index and search through this documents base. This research takes place within the Telemakus project, which has for goal to support and facilitate the knowledge discovery process by providing retrieval, visual, and interaction tools to mine and map research findings fr...

Mémoire is a framework for the sharing and distribution of case bases and case based reasoning in biology and medicine. Based on the fact that semantics account for the success of biomedical case based reasoning systems, this paper defends the suitability of a semantic approach similar to the semantic Web for sharing and distributing case bases and...

Phylsyst is an intelligent system that mines phylogenetic classifications. Its idea stems from the work of phylogeneticists of the Société Française de Systématique and proposes to test an innovative method for inferring phylogenetic classifications. The main idea in Phylsyst is to represent the reasoning of an expert phylogeneticist constructing a...

This article presents a framework called Mémoire for the interopera-bility of case bases and case based reasoning in biology and medicine. It ex-plains the motivation for such as formalization effort, based on the importance of semantics in the success of biomedical case based reasoning systems. This paper defends the suitability of a semantic appr...

Case-based reasoning systems applied to safety-critical environments justify specific measures to ensure that the assistance provided is not dangerous to human life. This article presents a case-based reasoning system developed for medical decision-support in a safety-critical environment, the CARE- PARTNER system. Based on the evaluation of the re...

Case-based reasoning systems applied to safety-critical environments justify specific measures to ensure that the assistance provided is not dangerous to human life. Taking example on a case-based reasoning system developed for medical decision-support in stem cell post-transplant long-term follow-up, this article stresses the importance to differe...

The authors randomly selected 400 physicians from a population of 1,545 practicing physicians providing follow-up care to patients who received bone marrow or blood stem cell transplants at the Fred Hutchinson Cancer Research Center to determine interest in receiving Internet-based transplant information. In a two-factor completely randomized facto...

Our project is to build and evaluate a knowledge-based computerized decision-support system for bonemarrow post-transplant care on the WWW. Its main reasoning methodologies are case-based reasoning and rule-based reasoning. Data mining is used in this project in synergy with machine learning to automatically update the knowledge-base (learn new rul...

This paper presents the CARE-PARTNER system. Functionally, it offers via the WWW knowledge-support assistance to clinicians responsible for the long-term follow-up of stem-cell post-transplant patient care. CARE- PARTNER aims at implementing the concept of evidence-based medical practice, which recommends the practice of medicine based on proven an...

Evidence-based practice in medicine promotes the performance of medicine based upon proven and validated practice. The CARE-PARTNER system presented here is a computerized knowledge-support system for stem-cell post-transplant long-term follow-up (LTFU) care on the WWW, which means that it monitors the quality of the knowledge both of its own knowl...

The system presented in this paper proposes to implement the concept of evidence-based medical practice. This reasoning framework permits physicians to associate preferences to the kind of knowledge they reuse during their care, favoring knowledge based upon more evidence. This system is a computerized decision-support assistant for the long-term f...

This paper proposes a multimodal reasoning framework for the cooperation of case-based reasoning, rule-based reasoning and information retrieval to solve problems. Functionally, it offers via the WWW knowledge-support assistance to clinicians responsible for the long-term follow- up of stem-cell post-transplant patient care. In this domain, no sing...

ion in Case-Based Reasoning. Isabelle BICHINDARITZ LIAP-5, UFR de Math'ematiques et Informatique 45 rue des Saints-P`eres 75006 Paris FRANCE tel : (+33) 1 44 55 35 63 fax : (+33) 1 44 55 35 36 email : bici@math-info.univ-paris5.fr Introduction Object-oriented methodology (OOM) and case-based reasoning methodology (CBR) have close roots in the 70's:...

A French adaptation of the Stroop colour-naming task was used to investigate selective processing of information related to eating and the body in 92 female subjects: 18 with restricting-type anorexia nervosa (RAs), 25 with binge-eating-type anorexia (BAs), 20 with bulimia nervosa (BNs), and 29 controls (Cs). All participants were significantly slo...

The article presents the temporal knowledge representation and its organization in a case-based reasoning system called MNAOMIA. The general case-based reasoning methodology is grounded on a model of reasoning such that memory, learning and reasoning are inseparable. This particular focus forces pertinent knowledge representation and organization i...

Originally, case-based reasoning emerged from Schank's theory of dynamic memory. It has then been presented as an artificial intelligence methodology for processing empirical knowledge. Nevertheless, more recent case-based reasoning systems study show how to take advantage from theoretical knowledge to process empirical knowledge more effectively....

Case-based reasoning systems are generally devoted to the realization of a single cognitive task. The need for such systems to perform various cognitive tasks questions how to organize their memory to permit them to be task-adaptive. The case-based reasoning system adaptive to cognitive tasks presented here is capable to adapt to analysis tasks as...

Originally, case-based reasoning emerged from Schank's theory of dynamic memory. It has then been presented as an artificial intelligence methodology for processing empirical knowledge. Nevertheless, more recent case-based reasoning systems study how to take advantage from theoretical knowledge to process empirical knowledge more effectively. In or...

In many domains, automatic concept learning from real-world input data is a very useful, as well as complex, process. The use of a case-based concept learning system is presented in this paper. This system uses domain knowledge to guide the different steps of the construction and the utilization of the cases memory. Its originality is that the doma...