A medical informatics perspective on decision support systems. Findings from the yearbook 2012 section on decision support.

OBJECTIVES: To summarize current excellent research in the field of computer-based decision support systems in health and healthcare. METHODS: We provide a synopsis of the articles selected for the IMIA Yearbook 2012, from which we attempt to draft a synthetic overview of the activity and new trends in the field. RESULTS: While the state of the research in the field of medical decision support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify fundamental aspects of the fields, e.g. Decision Support Systems for Computerized Provider Order Entry, both for physicians and pharmacists, as well as more specific developments such as instruments to improve processing of data related to Clinical Trials and applications to capture family health history. CONCLUSION: The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. This selected set of scientific investigations clearly question the way decision support systems are deployed in clinical environments as these systems seem to have little impact on patient safety and even could harm the patient. Furthermore, while significant research efforts are invested into translational & "omics" medicine, it is interesting to observe that simple data capture applications can reasonably lead to positive changes in healthcare.

A medical informatics perspective on health informatics 3.0. Findings from the Yearbook 2011 section on health informatics 3.0.

OBJECTIVES: To summarize current advances of the so-called Web 3.0 and emerging trends of the semantic web. METHODS: We provide a synopsis of the articles selected for the IMIA Yearbook 2011, from which we attempt to derive a synthetic overview of the today's and future activities in the field. RESULTS: while the state of the research in the field is illustrated by a set of fairly heterogeneous studies, it is possible to identify significant clusters. While the most salient challenge and obsessional target of the semantic web remains its ambition to simply interconnect all available information, it is interesting to observe the developments of complementary research fields such as information sciences and text analytics. The combined expression power and virtually unlimited data aggregation skills of Web 3.0 technologies make it a disruptive instrument to discover new biomedical knowledge. In parallel, such an unprecedented situation creates new threats for patients participating in large-scale genetic studies as Wjst demonstrate how various data set can be coupled to re-identify anonymous genetic information. CONCLUSIONS: The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development of core semantic web techniques as well as transdisciplinary achievements as exemplified with literature-based analytics. This selected set of scientific investigations also demonstrates the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge with potential threats for confidentiality directly associated with such advances. Altogether these papers support the idea that more elaborated computer tools, likely to combine heterogeneous text and data contents should soon emerge for the benefit of both experimentalists and hopefully clinicians.

Findings from the yearbook 2010 section on decision support systems.

OBJECTIVE: To summarize current excellent research in the field of computer-based decision support systems in health and healthcare. METHODS: We provide a synopsis of the articles selected for the IMIA Yearbook 2010, from which we attempt to derive a synthetic overview of the activity and new trends in the field. RESULTS: while the state of the research in the field of medical decision support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, clearly, the importance of studies related to computerized prescription order entry (CPOE) systems and guidelines management systems for both medical decision making and care providers, occupies a central role in the field, with application affecting also EHR vendors. In parallel, we observe translational interests for developing bridges with results generated by molecular biology, where the mass of data generated by high/throughput experiments and large-scale genome analysis projects, raises specific processing challenges. CONCLUSIONS: The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. This selected set of scientific investigations demonstrates the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge. Altogether these papers support the idea that more elaborated computer tools, likely to combine heterogeneous contextual contents, are needed.

A medical informatics perspective on decision support: toward a unified research paradigm combining biological vs. clinical, empirical vs. legacy, and structured vs. unstructured data.

OBJECTIVES: To summarize current excellent research in the field of decision-support systems. METHODS: We provide a synopsis of the articles selected for the IMIA Yearbook 2009, from which we attempt to derive a synthetic overview of the activity and new trends in the field. RESULTS: Five papers from international peer reviewed journals have been selected for the section on decision support. While the state of the research in the field of decision-support systems is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, issues related to guidelines processing implementation occupies a central role in today's field with two alternative directions: 1. broad medical applications, which attempts to assist decision-makers to process large patient sets; 2. narrow clinical applications focused on in-depth real-time signal processing for a specific population or medical specialty. CONCLUSIONS: The best paper selection of articles on decision-supports shows examples of excellent research on methods concerning original development as well as quality assurance of reported studies. It is also observed that this year's selection point directly to more original research areas such as temporal signal processing, although more traditionally related areas, such as information retrieval and/or natural language processing, remain fairly active in the field. Altogether these papers support the idea that more elaborated computer tools, likely to combine together textual and highly structured data, including real-time data contents, are needed.

A bio-medical informatics perspective on human factors. Findings from the Yearbook 2008 Section on Human Factors.

OBJECTIVES: To summarize current excellent research in the field of human factors. METHODS: We provide a synopsis of the articles selected for the IMIA Yearbook 2008, from which we attempt to derive a synthetic overview of the activity and new trends in the field. RESULTS: while the state of the research in the field of human factors is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, clearly, the importance of issues related to medical order entry, which also founded human factors studies in medical informatics, still occupies a central role in the field. In parallel, we observe an emerging interest for human factors from the field of bioinformatics, where the mass of data generated by high/throughput experiments and large-scale genome analysis projects, raises specific processing challenges. Such challenges will have to be addressed to achieve post-genomics era medicine. CONCLUSIONS: The best paper selection of articles on human factors shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of preserving interpersonal communication among healthcare staff in computerized working environments is complemented by more original scientific investigations, which demonstrate the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge. Altogether these papers support the idea that more elaborated computer tools, likely to combine contextual contents, are needed.

Controlling the vocabulary for anatomy.

When confronted with the representation of human anatomy, natural language processing (NLP) system designers are facing an unsolved and frequent problem: the lack of a suitable global reference. The available sources in electronic format are numerous, but none fits adequately all the constraints and needs of language analysis. These sources are usually incomplete, difficult to use or tailored to specific needs. The anatomist's or ontologist's view does not necessarily match that of the linguist. The purpose of this paper is to review most recognized sources of knowledge in anatomy usable for linguistic analysis. Their potential and limits are emphasized according to this point of view. Focus is given on the role of the consensus work of the International Federation of Associations of Anatomists (IFAA) giving the Terminologia Anatomica.

Evaluating and reducing the effect of data corruption when applying bag of words approaches to medical records.

Unlike journal corpora, which are supposed to be carefully reviewed before being published, the quality of documents in a patient record are often corrupted by mispelled words and conventional graphies or abbreviations. After a survey of the domain, the paper focuses on evaluating the effect of such corruption on an information retrieval (IR) engine. The IR system uses a classical bag of words approach, with stems as representation items and term frequency-inverse document frequency (tf-idf) as weighting schema; we pay special attention to the normalization factor. First results shows that even low corruption levels (3%) do affect retrieval effectiveness (4-7%), whereas higher corruption levels can affect retrieval effectiveness by 25%. Then, we show that the use of an improved automatic spelling correction system, applied on the corrupted collection, can almost restore the retrieval effectiveness of the engine.

Conceptual search in electronic patient record.

Search by content in a large corpus of free texts in the medical domain is, today, only partially solved. The so-called GREP approach (Get Regular Expression and Print), based on highly efficient string matching techniques, is subject to inherent limitations, especially its inability to recognize domain specific knowledge. Such methods oblige the user to formulate his or her query in a logical Boolean style; if this constraint is not fulfilled, the results are poor. The authors present an enhancement to string matching search by the addition of a light conceptual model behind the word lexicon. The new system accepts any sentence as a query and radically improves the quality of results. Efficiency regarding execution time is obtained at the expense of implementing advanced indexing algorithms in a pre-processing phase. The method is described and commented and a brief account of the results illustrates this paper.

Comparing general and medical texts for information retrieval based on natural language processing: an inquiry into lexical disambiguation.

In this paper we compare two types of corpus, focusing on the lexical ambiguity of each of them. The first corpus consists mainly of general newspaper articles and literature excerpts, while the second belongs to the medical domain. To conduct the study, we have used two different disambiguation tools. First, each tool was validated in its respective application area. We then use these systems in order to assess and compare both the general ambiguity rate and the particularities of each domain. Quantitative results show that medical documents are lexically less ambiguous than unrestricted documents. Our conclusions emphasize the importance of the application area in the design of NLP tools.

A light knowledge model for linguistic applications.

Content extraction from medical texts is achievable today by linguistic applications, in so far as sufficient domain knowledge is available. Such knowledge represents a model of the domain and is hard to collect with sufficient depth and good coverage, despite numerous attempts. To leverage this task is a priority in order to benefit from the awaited linguistic tools. The light model is designed with this goal in mind. Syntactic and lexical information are generally available with large lexicons. A domain model should add the necessary semantic information. The authors have designed a light knowledge model for the collection of semantic information on the basis of the recognized syntactical and lexical attributes. It has been tailored for the acquisition of enough semantic information in order to retrieve terms of a controlled vocabulary from free texts, as for example, to retrieve Mesh terms from patient records.

Looking back or looking all around: comparing two spell checking strategies for documents edition in an electronic patient record.

We report on the comparison of two systems for correcting spelling errors resulting in non-existent words (i.e. not listed in any lexicon). Both systems aim at improving edition of medical reports. Unlike traditional systems, based on word language models, both semantic and syntactic contexts are considered here. Both systems share the same string-to-string edit distance module, and the same contextual disambiguation principles. The differences between the two systems are located at the user interaction level: while the first system is using exclusively the left context, simulating the underlining of every mis-spelling at the end of every word typing, the second system uses the left as well as the right context and simulate a post-edition correction, when asked by the author. Our conclusion shows the improvements brought by the second approach.

Semantic handling of medical compound words through sound analysis and generation processes.

Compound words are frequently encountered in the medical domain. Their conciseness complies with the telegraphic style usually adopted by clinicians in daily practice. This amplifies the need for clarifying their semantic interpretation and representation through respectively the analysis and generation processes. While highlighting the peculiarities of medical compound words, this paper shows how model-driven linguistic tools accurately deal with the compositionality of medical language. These statements are illustrated by means of examples, stemming from the handling of surgical procedures as part of the GALEN-IN-USE project.

Medical document anonymization with a semantic lexicon.

We present an original system for locating and removing personally-identifying information in patient records. In this experiment, anonymization is seen as a particular case of knowledge extraction. We use natural language processing tools provided by the MEDTAG framework: a semantic lexicon specialized in medicine, and a toolkit for word-sense and morpho-syntactic tagging. The system finds 98-99% of all personally-identifying information.

Tagging medical texts: a rule-based experiment.

In this paper we describe the construction of a part-of-speech tagger for medical document retrieval purposes, therefore we have designed a specific architecture called minimal commitment. The system uses local grammatical rules for conducting the disambiguation task. Four evaluations are conducted, with and without taking unknown words into account. In between each evaluation the modules (lexicon, guesser, rules) of the system are incrementally improved.

A toolset for medical text processing.

The processing of medical texts is a burden in the absence of a toolset designed for simple operations such as recognizing morphological variants, updating and accessing a word dictionary of the domain and segmenting words with multiple morpho-semantems. The apparent simplicity of these basic operations is an illusion because it soon becomes clear that quality implementation is a long-term task. Coherency between subtasks may be lacking unless strict rules are enforced. In fact, good tools are rarely available or have not been tailored for the medical profession. This paper aims at defining a complete toolset for medical word processing. In addition, it provides relevant examples of the inherent difficulties of this task. It reports on typical results that can be expected from an industry-standard implementation.

The power and limits of a rule-based morpho-semantic parser.

The venue of Electronic Patient Record (EPR) implies an increasing amount of medical texts readily available for processing, as soon as convenient tools are made available. The chief application is text analysis, from which one can drive other disciplines like indexing for retrieval, knowledge representation, translation and inferencing for medical intelligent systems. Prerequisites for a convenient analyzer of medical texts are: building the lexicon, developing semantic representation of the domain, having a large corpus of texts available for statistical analysis, and finally mastering robust and powerful parsing techniques in order to satisfy the constraints of the medical domain. This article aims at presenting an easy-to-use parser ready to be adapted in different settings. It describes its power together with its practical limitations as experienced by the authors.

Model-based semantic dictionaries for medical language understanding.

Semantic dictionaries are emerging as a major cornerstone towards achieving sound natural language understanding. Indeed, they constitute the main bridge between words and conceptual entities that reflect their meanings. Nowadays, more and more wide-coverage lexical dictionaries are electronically available in the public domain. However, associating a semantic content with lexical entries is not a straightforward task as it is subordinate to the existence of a fine-grained concept model of the treated domain. This paper presents the benefits and pitfalls in building and maintaining multilingual dictionaries, the semantics of which is directly established on an existing concept model. Concrete cases, handled through the GALEN-IN-USE project, illustrate the use of such semantic dictionaries for the analysis and generation of multilingual surgical procedures.

MEDTAG: tag-like semantics for medical document indexing.

Medical documentation is central in health care, as it constitutes the main means of communication between care providers. However, there is a gap to bridge between storing information and extracting the relevant underlying knowledge. We believe natural language processing (NLP) is the best solution to handle such a large amount of textual information. In this paper we describe the construction of a semantic tagset for medical document indexing purposes. Rather than attempting to produce a home-made tagset, we decided to use, as far as possible, standard medicine resources. This step has led us to choose UMLS hierarchical classes as a basis for our tagset. We also show that semantic tagging is not only providing bases for disambiguisation between senses, but is also useful in the query expansion process of the retrieval system. We finally focus on assessing the results of the semantic tagger.