Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform

Internal drainage of liver hydatid - concerns and solutions

There are a number of procedures for the surgical management of liver hydatid and controversies still persist regarding the best technique Forty-three patients with hydatid disease of the liver were managed surgically between 1991 and 1998. Internal drainage (Roux-en-Y cysto-jejunostomy) was performed in eight cases of liver hydatid with biliary communications. Internal drainage was associated with a high incidence of the infection of the residual cavity with abscess formation (n=3/8, 38%). In all the three patients the cyst was located in the superior segments of the liver (VII, VIII, IVa). In two of the three patients the cyst was larger than 10 cm. Dependent siting of stoma is a key for the successful outcome of internal drainage in liver hydatid. This procedure is best avoided in large cysts, especially those located in the superior segments and with pericyst calcification

Computational Intelligence in Medical Decisions Making

Computation intelligence paradigms including artificial neural networks, fuzzy systems, evolutionary computing techniques, intelligent agents and so on provide a basis for human like reasoning in medical systems. Approximate reasoning is one of the most effective fuzzy systems. The compositional rule of inference founded on the logical law modus ponens is furnished with a true conclusion, provided that the premises of the rule are true as well. Even though there exist different approaches to an implication, being the crucial part of the rule, we modify the early implication proposed in our practical model concerning a medical application. The approximate reasoning system presented in this work considers evaluation of a risk in the situation when physicians weigh necessity of the operation on a patient. The patient’s clinical symptom levels, pathologically heightened, indicate the presence of a disease possible to recover by surgery. We wish to evaluate the extension of the operation danger by involving particularly designed fuzzy sets in the algorithm of approximate reasoning

A knowledge-based diagnostic clinical decision support system for musculoskeletal disorders of the shoulder for use in a primary care setting

Background Twenty percent of cases seen by primary care clinicians (general practitioners; GPs) are musculoskeletal in nature, and approximately one-quarter of these are shoulder complaints. GPs are increasingly overloaded with clinical information and unfamiliarity with current research can easily lead to misdiagnosis and, in turn, to unnecessary test requests or onward specialist referrals. Well-designed diagnostic clinical decision support systems (CDSS) have been shown to facilitate clinical decision-making and reduce diagnostic errors. However, no CDSS have been developed or tested for musculoskeletal disorders.Methods We have developed a prototype knowledge-based diagnostic CDSS for musculoskeletal shoulder conditions. The CDSS uses Bayesian reasoning to diagnose six common musculoskeletal shoulder pathologies, based on current evidence and expert opinion. The CDSS was tested by comparing its diagnostic outcome against 50 case studies with known diagnosis by radiological imaging.Results The CDSS diagnostic validity and reliability was shown to be 88% with a Kappa value of 0.85 to a confidence level of 99% compared to known diagnosis by radiological imaging.Conclusions The results suggest that a Bayesian network-based CDSS is a promising instrument in the diagnosis of musculoskeletal shoulder conditions, having been shown to be valid and reliable for 50 case studies.Peer reviewe