Towards a smart cancer registry

Aims Pathology notification for a Cancer Registry is regarded as the most valid information for the confirmation of a diagnosis of cancer. In view of the importance of pathology data, an automatic medical text analysis system (Medtex) is being developed to perform electronic Cancer Registry data extraction and coding of important clinical information embedded within pathology reports. Methods The system automatically scans HL7 messages received from a Queensland pathology information system and analyses the reports for terms and concepts relevant to a cancer notification. A multitude of data items for cancer notification such as primary site, histological type, stage, and other synoptic data are classified by the system. The underlying extraction and classification technology is based on SNOMED CT1 2. The Queensland Cancer Registry business rules3 and International Classification of Diseases – Oncology – Version 34 have been incorporated. Results The cancer notification services show that the classification of notifiable reports can be achieved with sensitivities of 98% and specificities of 96%5, while the coding of cancer notification items such as basis of diagnosis, histological type and grade, primary site and laterality can be extracted with an overall accuracy of 80%6. In the case of lung cancer staging, the automated stages produced were accurate enough for the purposes of population level research and indicative staging prior to multi-disciplinary team meetings2 7. Medtex also allows for detailed tumour stream synoptic reporting8. Conclusions Medtex demonstrates how medical free-text processing could enable the automation of some Cancer Registry processes. Over 70% of Cancer Registry coding resources are devoted to information acquisition. The development of a clinical decision support system to unlock information from medical free-text could significantly reduce costs arising from duplicated processes and enable improved decision support, enhancing efficiency and timeliness of cancer information for Cancer Registries.

Towards a visually enhanced medical search engine

This paper presents the prototype of an information retrieval system for medical records that utilises visualisation techniques, namely word clouds and timelines. The system simplifies and assists information seeking tasks within the medical domain. Access to patient medical information can be time consuming as it requires practitioners to review a large number of electronic medical records to find relevant information. Presenting a summary of the content of a medical document by means of a word cloud may permit information seekers to decide upon the relevance of a document to their information need in a simple and time effective manner. We extend this intuition, by mapping word clouds of electronic medical records onto a timeline, to provide temporal information to the user. This allows exploring word clouds in the context of a patient’s medical history. To enhance the presentation of word clouds, we also provide the means for calculating aggregations and differences between patient’s word clouds.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Reversible and blind database watermarking using difference expansion

There has been significant research in the field of database watermarking recently. However, there has not been sufficient attention given to the requirement of providing reversibility (the ability to revert back to original relation from watermarked relation) and blindness (not needing the original relation for detection purpose) at the same time. This model has several disadvantages over reversible and blind watermarking (requiring only the watermarked relation and secret key from which the watermark is detected and the original relation is restored) including the inability to identify the rightful owner in case of successful secondary watermarking, the inability to revert the relation to the original data set (required in high precision industries) and the requirement to store the unmarked relation at a secure secondary storage. To overcome these problems, we propose a watermarking scheme that is reversible as well as blind. We utilize difference expansion on integers to achieve reversibility. The major advantages provided by our scheme are reversibility to a high quality original data set, rightful owner identification, resistance against secondary watermarking attacks, and no need to store the original database at a secure secondary storage. We have implemented our scheme and results show the success rate is limited to 11% even when 48% tuples are modified.

The relationship between BCOM1 gene variants and macular pigment optical density in persons with and without age-related macular degeneration

Background: Recent evidence indicates that gene variants related to carotenoid metabolism play a role in the uptake of macular pigments lutein (L) and zeaxanthine (Z). Moreover, these pigments are proposed to reduce the risk for advanced age-related macular degeneration (AMD). This study provides the initial examination of the relationship between the gene variants related to carotenoid metabolism, macular pigment optical density (MPOD) and their combined expression in healthy humans and patients with AMD. Participants and Methods: Forty-four participants were enrolled from a general population and a private practice including 20 healthy participants and 24 patients with advanced (neovascular) AMD. Participants were genotyped for the three single nucleotide polymorphisms (SNPs) upstream from BCMO1, rs11645428, rs6420424 and rs6564851 that have been shown to either up or down regulate beta-carotene conversion efficiency in the plasma. MPOD was determined by heterochromatic flicker photometry. Results: Healthy participants with the rs11645428 GG genotype, rs6420424 AA genotype and rs6564851 GG genotype all had on average significantly lower MPOD compared to those with the other genotypes (p < 0.01 for all three comparisons). When combining BCMO1 genotypes reported to have “high” (rs11645428 AA/rs6420424 GG/rs6564851 TT) and “low” (rs11645428 GG/rs6420424 AA/rs6564851 GG) beta-carotene conversion efficiency, we demonstrate clear differences in MPOD values (p<0.01). In patients with AMD there were no significant differences in MPOD for any of the three BCMO1 gene variants. Conclusion: In healthy participants MPOD levels can be related to high and low beta-carotene conversion BCMO1 genotypes. Such relationships were not found in patients with advanced neovascular AMD, indicative of additional processes influencing carotenoid uptake, possibly related to other AMD susceptibility genes. Our findings indicate that specific BCMO1 SNPs should be determined when assessing the effects of carotenoid supplementation on macular pigment and that their expression may be influenced by retinal disease.

De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology

Background Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. Aims The aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach. Results The automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80. Conclusion While the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Design of a traumatic injury simulator for assessing lower limb response to high loading rates

Current military conflicts are characterized by the use of the improvised explosive device. Improvements in personal protection, medical care, and evacuation logistics have resulted in increasing numbers of casualties surviving with complex musculoskeletal injuries, often leading to lifelong disability. Thus, there exists an urgent requirement to investigate the mechanism of extremity injury caused by these devices in order to develop mitigation strategies. In addition, the wounds of war are no longer restricted to the battlefield; similar injuries can be witnessed in civilian centers following a terrorist attack. Key to understanding such mechanisms of injury is the ability to deconstruct the complexities of an explosive event into a controlled, laboratory-based environment. In this article, a traumatic injury simulator, designed to recreate in the laboratory the impulse that is transferred to the lower extremity from an anti-vehicle explosion, is presented and characterized experimentally and numerically. Tests with instrumented cadaveric limbs were then conducted to assess the simulator’s ability to interact with the human in two mounting conditions, simulating typical seated and standing vehicle passengers. This experimental device will now allow us to (a) gain comprehensive understanding of the load-transfer mechanisms through the lower limb, (b) characterize the dissipating capacity of mitigation technologies, and (c) assess the bio-fidelity of surrogates.

The comparative behaviour of two combat boots under impact

Background Improvised explosive devices have become the characteristic weapon of conflicts in Iraq and Afghanistan. While little can be done to mitigate against the effects of blast in free-field explosions, scaled blast simulations have shown that the combat boot can attenuate the effects on the vehicle occupants of anti-vehicular mine blasts. Although the combat boot offers some protection to the lower limb, its behaviour at the energies seen in anti-vehicular mine blast has not been documented previously. Methods The sole of eight same-size combat boots from two brands currently used by UK troops deployed to Iraq and Afghanistan were impacted at energies of up to 518 J, using a spring-assisted drop rig. Results The results showed that the Meindl Desert Fox combat boot consistently experienced a lower peak force at lower impact energies and a longer time-to-peak force at higher impact energies when compared with the Lowa Desert Fox combat boot. Discussion This reduction in the peak force and extended rise time, resulting in a lower energy transfer rate, is a potentially positive mitigating effect in terms of the trauma experienced by the lower limb. Conclusion Currently, combat boots are tested under impact at the energies seen during heel strike in running. Through the identification of significantly different behaviours at high loading, this study has shown that there is rationale in adding the performance of combat boots under impact at energies above those set out in international standards to the list of criteria for the selection of a combat boot.

In-vehicle extremity injuries from improvised explosive devices : current and future foci

The conflicts in Iraq and Afghanistan have been epitomized by the insurgents’ use of the improvised explosive device against vehicle-borne security forces. These weapons, capable of causing multiple severely injured casualties in a single incident, pose the most prevalent single threat to Coalition troops operating in the region. Improvements in personal protection and medical care have resulted in increasing numbers of casualties surviving with complex lower limb injuries, often leading to long-term disability. Thus, there exists an urgent requirement to investigate and mitigate against the mechanism of extremity injury caused by these devices. This will necessitate an ontological approach, linking molecular, cellular and tissue interaction to physiological dysfunction. This can only be achieved via a collaborative approach between clinicians, natural scientists and engineers, combining physical and numerical modelling tools with clinical data from the battlefield. In this article, we compile existing knowledge on the effects of explosions on skeletal injury, review and critique relevant experimental and computational research related to lower limb injury and damage and propose research foci required to drive the development of future mitigation technologies.

A comparison of MiL‐Lx and Hybrid‐III responses in seated and standing postures with blast mats in simulated under‐vehicle explosions

Blast mats that can be retrofitted to the floor of military vehicles are considered to reduce the risk of injury from under‐vehicle explosions. Anthropometric test devices (ATDs) are validated for use only in the seated position. The aim of this study was to use a traumatic injury simulator fitted with 3 different blast mats in order to assess the ability of 2 ATD designs to evaluate the protective capacity of the mats in 2 occupant postures under 2 severities. Tests were performed for each combination of mat design, ATD, severity and posture using an antivehicle under‐belly injury simulator. The differences between mitigation systems were larger under the H‐III compared to the MiL‐Lx. There was little difference in how the 2 ATDs and how posture ranked the mitigation systems. Results from this study suggest that conclusions obtained by testing in the seated position can be extrapolated to the standing. However, the different percentage reductions observed in the 2 ATDs suggests different levels of protection. It is therefore unclear which ATD should be used to assess such mitigation systems. A correlation between cadavers and ATDs on the protection offered by blast mats is required in order to elucidate this issue.

A standing vehicle occupant is likely to sustain a more severe injury than one who has flexed knees in an under‐vehicle explosion : a cadaveric study

The lower limb of military vehicle occupants has been the most injured body part due to undervehicle explosions in recent conflicts. Understanding the injury mechanism and causality of injury severity could aid in developing better protection. Therefore, we tested 4 different occupant postures (seated, brace, standing, standing with knee locked in hyper‐extension) in a simulated under‐vehicle explosion (solid blast) using our traumatic injury simulator in the laboratory; we hypothesised that occupant posture would affect injury severity. No skeletal injury was observed in the specimens in seated and braced postures. Severe, impairing injuries were observed in the foot of standing and hyper‐extended specimens. These results demonstrate that a vehicle occupant whose posture at the time of the attack incorporates knee flexion is more likely to be protected against severe skeletal injury to the lower leg.

Use of cadavers and anthropometric test devices (ATDs) for assessing lower limb injury outcome from under‐vehicle explosions

Lower extremities are particularly susceptible to injury in an under‐vehicle explosion. Operational fitness of military vehicles is assessed through anthropometric test devices (ATDs) in full‐scale blast tests. The aim of this study was to compare the response between the Hybrid‐III ATD, the MiL‐Lx ATD and cadavers in our traumatic injury simulator, which is able to replicate the response of the vehicle floor in an under‐vehicle explosion. All specimens were fitted with a combat boot and tested on our traumatic injury simulator in a seated position. The load recorded in the ATDs was above the tolerance levels recommended by NATO in all tests; no injuries were observed in any of the 3 cadaveric specimens. The Hybrid‐III produced higher peak forces than the MiL‐Lx. The time to peak strain in the calcaneus of the cadavers was similar to the time to peak force in the ATDs. Maximum compression of the sole of the combat boot was similar for cadavers and MiL‐Lx, but significantly greater for the Hybrid‐III. These results suggest that the MiL‐Lx has a more biofidelic response to under‐vehicle explosive events compared to the Hybrid‐III. Therefore, it is recommended that mitigation strategies are assessed using the MiL‐Lx surrogate and not the Hybrid‐III.

Prospects for studying how high-intensity compression waves cause damage in human blast injuries

Since World War I, explosions have accounted for over 70% of all injuries in conflict. With the development of improved personnel protection of the torso, improved medical care and faster aeromedical evacuation, casualties are surviving with more severe injuries to the extremities. Understanding the processes involved in the transfer of blast-induced shock waves through biological tissues is essential for supporting efforts aimed at mitigating and treating blast injury. Given the inherent heterogeneities in the human body, we argue that studying these processes demands a highly integrated approach requiring expertise in shock physics, biomechanics and fundamental biological processes. This multidisciplinary systems approach enables one to develop the experimental framework for investigating the material properties of human tissues that are subjected to high compression waves in blast conditions and the fundamental cellular processes altered by this type of stimuli. Ultimately, we hope to use the information gained from these studies in translational research aimed at developing improved protection for those at risk and improved clinical outcomes for those who have been injured from a blast wave.