Medical Informatics

Controller-Free Exploration of Medical Image Data: Experiencing the Kinect

by Mario Ciampi

L. Gallo, A. P. Placitelli, and M. Ciampi, in IEEE CBMS 2011: Proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, pp. 1-6, 2011, IEEE Press, Los Alamitos, CA, USA

Health information Technology

by Molly King

Dorr, David A., Molly M. King. 2011. Chapter in Comprehensive Care Coordination for Chronically Ill Adults, edited by Cheryl Schraeder and Paul S. Shelton. Ames, Iowa: Wiley-Blackwell.

Técnicas para el análisis de datos clínicos

by David Nettleton

Evaluating IAIMS at Yale

by Pascal Calarco

With SE Grajek, SJ Frawley, J McKay, PL Miller, JA Paton, NK Roderer, JE Sullivan.  Journal of the American Medical Informatics Association. 4:2 (Mar/Apr. ‘97): 138-149.

Abstract Objective: To evaluate use of information resources during the first year of IAIMS implementation at the... more Abstract Objective: To evaluate use of information resources during the first year of IAIMS implementation at the Yale—New Haven Medical Center. The evaluation asked: (1) Which information resources are being used? (2) Who uses information resources? (3) Where are information resources used? (4) Are multiple sources of information being integrated?

Design: Measures included monthly usage data for resources delivered network-wide, in the Medical Library, and in the Hospital; online surveys of library workstation users; an annual survey of a random, stratified sample of Medical Center faculty, postdoctoral trainees, students, nurses, residents, and managerial and professional staff; and user comments.

Results: Eighty-three percent of the Medical Center community use networked information resources, and use of resources is increasing. Both status (faculty, student, nurse, etc.) and mission (teaching, research, patient care) affect use of individual resources. Eighty-eight percent of people use computers in more than one location, and increases in usage of traditional library resources such as MEDLINE are due to increased access from outside the Library. Both survey and usage data suggest that people are using multiple resources during the same information seeking session.

Conclusions: Almost all of the Medical Center community is using networked information resources in more settings. It is necessary to support increased demand for information access from remote locations and to specific populations, such as nurses. People are integrating information from multiple sources, but true integration within information systems is just beginning. Other institutions are advised to incorporate pragmatic evaluation into their IAIMS activities and to share evaluation results with decision-makers.

Development and validation of filters for the retrieval of studies of clinical examination from Medline

by Andrea Ketchum

Co-authored with Nader Shaikh, MD MPH,corresponding author1 Robert G Badgett, MD,2 Mina Pi, BS,3 Nancy L Wilczynski, PhD,4 K. Ann McKibbon, PhD,4 and R. Brian Haynes, MD PhD4.  Originally published in the Journal of Medical Internet Research.

Background
Efficiently finding clinical examination studies—studies that quantify the value of symptoms and signs... more Background
Efficiently finding clinical examination studies—studies that quantify the value of symptoms and signs in the diagnosis of disease—is becoming increasingly difficult. Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests.
Objective
The objective was to develop filters for retrieving clinical examination studies from Medline.
Methods
We developed filters in a training dataset and validated them in a testing database. We created the training database by hand searching 161 journals (n = 52,636 studies). We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. To identify best combinations of these search terms, we used recursive partitioning. The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). We also examined the impact of examining reference lists of included articles on recall.
Results
In the training database, the single-term filters with the highest recall (95%) and the highest precision (8.4%) were diagnosis[subheading] and “medical history taking”[MeSH], respectively. The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100% and a precision of 89% in the training database. In the testing database, the Haynes-2004-Sensitive filter (recall 98%, precision 0.13%) and the RP filter (recall 89%, precision 0.52%) showed the best performance. The recall of these two filters increased to 99% and 94% respectively with review of the reference lists of the included articles.
Conclusions
Recursive partitioning appears to be a useful method of developing search filters. The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine.

Development and validation of filters for the retrieval of studies of clinical examination from Medline

by Andrea Ketchum

Co-authored with Nader Shaikh, MD MPH,corresponding author1 Robert G Badgett, MD,2 Mina Pi, BS,3 Nancy L Wilczynski, PhD,4 K. Ann McKibbon, PhD,4 and R. Brian Haynes, MD PhD4.  Originally published in the Journal of Medical Internet Research.

Background
Efficiently finding clinical examination studies—studies that quantify the value of symptoms and signs... more Background
Efficiently finding clinical examination studies—studies that quantify the value of symptoms and signs in the diagnosis of disease—is becoming increasingly difficult. Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests.
Objective
The objective was to develop filters for retrieving clinical examination studies from Medline.
Methods
We developed filters in a training dataset and validated them in a testing database. We created the training database by hand searching 161 journals (n = 52,636 studies). We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. To identify best combinations of these search terms, we used recursive partitioning. The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). We also examined the impact of examining reference lists of included articles on recall.
Results
In the training database, the single-term filters with the highest recall (95%) and the highest precision (8.4%) were diagnosis[subheading] and “medical history taking”[MeSH], respectively. The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100% and a precision of 89% in the training database. In the testing database, the Haynes-2004-Sensitive filter (recall 98%, precision 0.13%) and the RP filter (recall 89%, precision 0.52%) showed the best performance. The recall of these two filters increased to 99% and 94% respectively with review of the reference lists of the included articles.
Conclusions
Recursive partitioning appears to be a useful method of developing search filters. The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine.

Development and validation of filters for the retrieval of studies of clinical examination from Medline

by Andrea Ketchum

Co-authored with Nader Shaikh, MD MPH,corresponding author1 Robert G Badgett, MD,2 Mina Pi, BS,3 Nancy L Wilczynski, PhD,4 K. Ann McKibbon, PhD,4 and R. Brian Haynes, MD PhD4.  Originally published in the Journal of Medical Internet Research.

Background
Efficiently finding clinical examination studies—studies that quantify the value of symptoms and signs... more Background
Efficiently finding clinical examination studies—studies that quantify the value of symptoms and signs in the diagnosis of disease—is becoming increasingly difficult. Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests.
Objective
The objective was to develop filters for retrieving clinical examination studies from Medline.
Methods
We developed filters in a training dataset and validated them in a testing database. We created the training database by hand searching 161 journals (n = 52,636 studies). We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. To identify best combinations of these search terms, we used recursive partitioning. The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). We also examined the impact of examining reference lists of included articles on recall.
Results
In the training database, the single-term filters with the highest recall (95%) and the highest precision (8.4%) were diagnosis[subheading] and “medical history taking”[MeSH], respectively. The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100% and a precision of 89% in the training database. In the testing database, the Haynes-2004-Sensitive filter (recall 98%, precision 0.13%) and the RP filter (recall 89%, precision 0.52%) showed the best performance. The recall of these two filters increased to 99% and 94% respectively with review of the reference lists of the included articles.
Conclusions
Recursive partitioning appears to be a useful method of developing search filters. The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine.

Maintaining redundancy in the coordination of medical emergencies

by Aksel Tjora

Declerck, G., Charlet, J. (2011). Intelligence Artificielle, ontologies et connaissances en médecine. Les limites de la mécanisation de la pensée

by Gunnar Declerck

Prepublication version
Complete reference :
Declerck, G., Charlet, J. (2011). Intelligence Artificielle, ontologies et connaissances en médecine. Les limites de la mécanisation de la pensée. Revue d’Intelligence Artificielle (RIA), vol. 25, n°4, pp. 445-472, n° spécial « Intelligence artificielle et santé »

This theoretical article aims to draw up an inventory of the latest advances in medical knowledge engineering in the... more This theoretical article aims to draw up an inventory of the latest advances in medical knowledge engineering in the specific area of ontologies and knowledge based systems design. Echoing the debates that animated the landscape of Artificial Intelligence (AI) from the 1970s under the impetus of Dreyfus HL, it aims to show that most of the difficulties currently faced by medical knowledge engineering are inherent in the nature of AI, whose project is the mechanization of cognitive activity. As such it promotes the idea that only a fair understanding of what machines can do, given their machinic character itself, and remains, despite its cognitive finitude, a property of human being, may offer to balancing the scales between tasks that can be allocated to machines and those that have to be left in charge of humans.

Using NLG to Manage Information in Medical Emergencies

by Alasdair Mort

Authors; Nguyen H, Mellish C, Mort A, Kindness P, Knight J and Reiter E. Paper presented at Digital Engagement 11; 15-17th November 2011, Newcastle, UK

When a medical emergency occurs in a rural area of the UK, the first person on scene (FPOS) may be a layperson with... more When a medical emergency occurs in a rural area of the UK, the first person on scene (FPOS) may be a layperson with some basic first aid training and limited equipment. That person must assess the patients and their situation, carry out basic treatments and seek additional help as appropriate. This paper describes the potential use of Natural Language Generation (NLG) to generate advice to the FPOS at a road traffic collision and more effective handover reports between the FPOS and the subsequent medical staff in the patients’ chain of care.

Investigating Socio-technical Requirements for an Inclusive Medical Classification

by Fernando Figueira Filho

Federating learning management systems for medical education: A persuasive technologies perspective

by Bratsas Charalampos

Overview and Analysis of Electronic Health Record Standards

by Spyros Kitsiou

Modeling medical interventions using the semantic MediaWiki for use in healthcare practice and education

by Bratsas Charalampos

An Ontology-Based Approach to Constructing Medical Computational Problems for Use In Electronic Medical Education

by Bratsas Charalampos

A Semantic Wiki Within Moodle for Greek Medical Education

by Bratsas Charalampos

KnowBaSICS-M: An Ontology-Based System for Semantic Management of Medical Problems and Computerised Algorithmic Solutions

by Bratsas Charalampos

Ontology-Based Vector Space Model and Fuzzy Query Expansion to Retrieve Knowledge on Medical Computational Problem Solutions

by Bratsas Charalampos

Engaging Clinicians in the Visualization Design Process – Is It Possible?

by Kostas Pantazos

Proc. Workshop on Visual Analytics in Healthcare (VAHC) in conjunction with IEEE VisWeek 2011, Providence, RI, USA, 23 October, 2011.

Applying Artificial Intelligence to clinical guidelines: the GLARE approach

by Gianluca Correndo