Automated concept-level information extraction to reduce the need for custom software and rules development

Objective

Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval.

Materials and methods

A ‘learn by example’ approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge''s concept extraction task provided the data sets and metrics used to evaluate performance.

Results

Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks.

Discussion

With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation.

Conclusion

Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.     

Linguistic approach for identification of medication names and related information in clinical narratives

Background

Pharmacotherapy is an integral part of any medical care process and plays an important role in the medical history of most patients. Information on medication is crucial for several tasks such as pharmacovigilance, medical decision or biomedical research.

Objectives

Within a narrative text, medication-related information can be buried within other non-relevant data. Specific methods, such as those provided by text mining, must be designed for accessing them, and this is the objective of this study.

Methods

The authors designed a system for analyzing narrative clinical documents to extract from them medication occurrences and medication-related information. The system also attempts to deduce medications not covered by the dictionaries used.

Results

Results provided by the system were evaluated within the framework of the I2B2 NLP challenge held in 2009. The system achieved an F-measure of 0.78 and ranked 7th out of 20 participating teams (the highest F-measure was 0.86). The system provided good results for the annotation and extraction of medication names, their frequency, dosage and mode of administration (F-measure over 0.81), while information on duration and reasons is poorly annotated and extracted (F-measure 0.36 and 0.29, respectively). The performance of the system was stable between the training and test sets.     

Automatic extraction of relations between medical concepts in clinical texts

Objective

A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records.

Materials and methods

A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier.

Results

The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2*Precision*Recall/(Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7.

Discussion

Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction.

Conclusion

Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7%. In addition, features based on similarity contribute to a decrease of 1.1% when they are not available.     

Hybrid methods for improving information access in clinical documents: concept,assertion, and relation identification

Objective

This paper describes the approaches the authors developed while participating in the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate assertions on concepts and relations between concepts.

Design

The authors''approaches rely on both rule-based and machine-learning methods. Natural language processing is used to extract features from the input texts; these features are then used in the authors'' machine-learning approaches. The authors used Conditional Random Fields for concept extraction, and Support Vector Machines for assertion and relation annotation. Depending on the task, the authors tested various combinations of rule-based and machine-learning methods.

Results

The authors''assertion annotation system obtained an F-measure of 0.931, ranking fifth out of 21 participants at the i2b2/VA 2010 challenge. The authors'' relation annotation system ranked third out of 16 participants with a 0.709 F-measure. The 0.773 F-measure the authors obtained on concept extraction did not make it to the top 10.

Conclusion

On the one hand, the authors confirm that the use of only machine-learning methods is highly dependent on the annotated training data, and thus obtained better results for well-represented classes. On the other hand, the use of only a rule-based method was not sufficient to deal with new types of data. Finally, the use of hybrid approaches combining machine-learning and rule-based approaches yielded higher scores.     

Vaccine adverse event text mining system for extracting features from vaccine safety reports

Objective

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports.

Design

Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool.

Measurements

The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches.

Results

VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively.

Conclusion

Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.     

A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction

Objective

The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication–attribute linkage detection in two clinical corpora.

Data and methods

We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes. We evaluated the system''s performance against the human-generated gold standard.

Results

The experiments showed that the two machine learning approaches performed statistically significantly better than the baseline rule-based approach. The binary SVM classification achieved 0.94 F-measure with individual tokens as features. The SVM model trained on a parsimonious feature set achieved 0.81 F-measure for CN and 0.87 for CTA. The CRF MLSL method achieved 0.80 F-measure on both corpora.

Discussion and conclusions

We compared the novel MLSL method with a binary classification and a rule-based method. The MLSL method performed statistically significantly better than the rule-based method. However, the SVM-based binary classification method was statistically significantly better than the MLSL method for both the CTA and CN corpora. Using parsimonious feature sets both the SVM-based binary classification and CRF-based MLSL methods achieved high performance in detecting medication name and attribute linkages in CTA and CN.     

Effects of personal identifier resynthesis on clinical text de-identification

Objective

De-identified medical records are critical to biomedical research. Text de-identification software exists, including “resynthesis” components that replace real identifiers with synthetic identifiers. The goal of this research is to evaluate the effectiveness and examine possible bias introduced by resynthesis on de-identification software.

Design

We evaluated the open-source MITRE Identification Scrubber Toolkit, which includes a resynthesis capability, with clinical text from Vanderbilt University Medical Center patient records. We investigated four record classes from over 500 patients'' files, including laboratory reports, medication orders, discharge summaries and clinical notes. We trained and tested the de-identification tool on real and resynthesized records.

Measurements

We measured performance in terms of precision, recall, F-measure and accuracy for the detection of protected health identifiers as designated by the HIPAA Safe Harbor Rule.

Results

The de-identification tool was trained and tested on a collection of real and resynthesized Vanderbilt records. Results for training and testing on the real records were 0.990 accuracy and 0.960 F-measure. The results improved when trained and tested on resynthesized records with 0.998 accuracy and 0.980 F-measure but deteriorated moderately when trained on real records and tested on resynthesized records with 0.989 accuracy 0.862 F-measure. Moreover, the results declined significantly when trained on resynthesized records and tested on real records with 0.942 accuracy and 0.728 F-measure.

Conclusion

The de-identification tool achieves high accuracy when training and test sets are homogeneous (ie, both real or resynthesized records). The resynthesis component regularizes the data to make them less “realistic,” resulting in loss of performance particularly when training on resynthesized data and testing on real data.     

A classification approach to coreference in discharge summaries: 2011 i2b2 challenge

Objective

To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.

Design

An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia.

Measurements

Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results.

Results

The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference.

Conclusions

This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.     

Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries

Objective

A system that translates narrative text in the medical domain into structured representation is in great demand. The system performs three sub-tasks: concept extraction, assertion classification, and relation identification.

Design

The overall system consists of five steps: (1) pre-processing sentences, (2) marking noun phrases (NPs) and adjective phrases (APs), (3) extracting concepts that use a dosage-unit dictionary to dynamically switch two models based on Conditional Random Fields (CRF), (4) classifying assertions based on voting of five classifiers, and (5) identifying relations using normalized sentences with a set of effective discriminating features.

Measurements

Macro-averaged and micro-averaged precision, recall and F-measure were used to evaluate results.

Results

The performance is competitive with the state-of-the-art systems with micro-averaged F-measure of 0.8489 for concept extraction, 0.9392 for assertion classification and 0.7326 for relation identification.

Conclusions

The system exploits an array of common features and achieves state-of-the-art performance. Prudent feature engineering sets the foundation of our systems. In concept extraction, we demonstrated that switching models, one of which is especially designed for telegraphic sentences, improved extraction of the treatment concept significantly. In assertion classification, a set of features derived from a rule-based classifier were proven to be effective for the classes such as conditional and possible. These classes would suffer from data scarcity in conventional machine-learning methods. In relation identification, we use two-staged architecture, the second of which applies pairwise classifiers to possible candidate classes. This architecture significantly improves performance.     

Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010

Objective

As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge.

Design

The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline.

Measurements

Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set.

Results

The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second).

Conclusion

For all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks.     

Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

Objective

The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.

Design

We selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N_pos=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations.

Measurements

Classifiers'' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman''s test; misclassification error rate analysis was also performed.

Results

Rule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).

Conclusion

Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.     

Comprehensive temporal information detection from clinical text: medical events,time, and TLINK identification

Background

Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.

Objective

To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text.

Materials and methods

The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework.

Results

Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537.

Conclusions

Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance.     

Automatic extraction of medication information from medical discharge summaries

Objective

This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries.

Design

The system explored several linguistic natural language processing techniques (eg, term-based and token-based rule matching) to identify medication-related information in the narrative text. A number of lexical resources was constructed to profile lexical or morphological features for different categories of medication constituents.

Measurements

Performance was evaluated in terms of the micro-averaged F-measure at the horizontal system level.

Results

The automated system performed well, and achieved an F-micro of 80% for the term-level results and 81% for the token-level results, placing it sixth in exact matches and fourth in inexact matches in the i2b2 competition.

Conclusion

The overall results show that this relatively simple rule-based approach is capable of tackling multiple entity identification tasks such as medication extraction under situations in which few training documents are annotated for machine learning approaches, and the entity information can be characterized with a set of feature tokens.     

High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge

Objective

Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records.

Design

Authors developed a novel supervised learning model that incorporates two machine learning algorithms and several rule-based engines.

Measurements

Evaluation of each step included precision, recall and F-measure metrics. The final outputs of the system were scored using the i2b2 workshop evaluation metrics, including strict and relaxed matching with a gold standard.

Results

Evaluation results showed greater than 90% accuracy on five out of seven entities in the name entity recognition task, and an F-measure greater than 95% on the relationship classification task. The strict micro averaged F-measure for the system output achieved best submitted performance of the competition, at 85.65%.

Limitations

Clinical staff will only use practical processing systems if they have confidence in their reliability. Authors estimate that an acceptable accuracy for a such a working system should be approximately 95%. This leaves a significant performance gap of 5 to 10% from the current processing capabilities.

Conclusion

A multistage method with mixed computational strategies using a combination of rule-based classifiers and statistical classifiers seems to provide a near-optimal strategy for automated extraction of medication information from clinical records.Many of the potential benefits of the electronic medical record (EMR) rely significantly on our ability to automatically process the free-text content in the EMR. To understand the limitations and difficulties of exploiting the EMR we have designed an information extraction engine to identify medication events within patient discharge summaries, as specified by the i2b2 medication extraction shared task.     

MITRE system for clinical assertion status classification

Objective

To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation ‘Challenges in natural language processing for clinical data’ for the task of classifying assertions associated with problem concepts extracted from patient records.

Materials and methods

A combination of machine learning (conditional random field and maximum entropy) and rule-based (pattern matching) techniques was used to detect negation, speculation, and hypothetical and conditional information, as well as information associated with persons other than the patient.

Results

The best submission obtained an overall micro-averaged F-score of 0.9343.

Conclusions

Using semantic attributes of concepts and information about document structure as features for statistical classification of assertions is a good way to leverage rule-based and statistical techniques. In this task, the choice of features may be more important than the choice of classifier algorithm.     

A supervised framework for resolving coreference in clinical records

Objective

A method for the automatic resolution of coreference between medical concepts in clinical records.

Materials and methods

A multiple pass sieve approach utilizing support vector machines (SVMs) at each pass was used to resolve coreference. Information such as lexical similarity, recency of a concept mention, synonymy based on Wikipedia redirects, and local lexical context were used to inform the method. Results were evaluated using an unweighted average of MUC, CEAF, and B³ coreference evaluation metrics. The datasets used in these research experiments were made available through the 2011 i2b2/VA Shared Task on Coreference.

Results

The method achieved an average F score of 0.821 on the ODIE dataset, with a precision of 0.802 and a recall of 0.845. These results compare favorably to the best-performing system with a reported F score of 0.827 on the dataset and the median system F score of 0.800 among the eight teams that participated in the 2011 i2b2/VA Shared Task on Coreference. On the i2b2 dataset, the method achieved an average F score of 0.906, with a precision of 0.895 and a recall of 0.918 compared to the best F score of 0.915 and the median of 0.859 among the 16 participating teams.

Discussion

Post hoc analysis revealed significant performance degradation on pathology reports. The pathology reports were characterized by complex synonymy and very few patient mentions.

Conclusion

The use of several simple lexical matching methods had the most impact on achieving competitive performance on the task of coreference resolution. Moreover, the ability to detect patients in electronic medical records helped to improve coreference resolution more than other linguistic analysis.     

Intensive care unit nurses' information needs and recommendations for integrated displays to improve nurses' situation awareness

Objective

Fatal errors can occur in intensive care units (ICUs). Researchers claim that information integration at the bedside may improve nurses'' situation awareness (SA) of patients and decrease errors. However, it is unclear which information should be integrated and in what form. Our research uses the theory of SA to analyze the type of tasks, and their associated information gaps. We aimed to provide recommendations for integrated, consolidated information displays to improve nurses'' SA.

Materials and Methods

Systematic observations methods were used to follow 19 ICU nurses for 38 hours in 3 clinical practice settings. Storyboard methods and concept mapping helped to categorize the observed tasks, the associated information needs, and the information gaps of the most frequent tasks by SA level. Consensus and discussion of the research team was used to propose recommendations to improve information displays at the bedside based on information deficits.

Results

Nurses performed 46 different tasks at a rate of 23.4 tasks per hour. The information needed to perform the most common tasks was often inaccessible, difficult to see at a distance or located on multiple monitoring devices. Current devices at the ICU bedside do not adequately support a nurse''s information-gathering activities. Medication management was the most frequent category of tasks.

Discussion

Information gaps were present at all levels of SA and across most of the tasks. Using a theoretical model to understand information gaps can aid in designing functional requirements.

Conclusion

Integrated information that enhances nurses'' Situation Awareness may decrease errors and improve patient safety in the future.     

Research and applications: Leveraging biodiversity knowledge for potential phyto-therapeutic applications

Objective

To identify and highlight the feasibility, challenges, and advantages of providing a cross-domain pipeline that can link relevant biodiversity information for phyto-therapeutic assessment.

Materials and methods

A public repository of clinical trials information (ClinicalTrials.gov) was explored to determine the state of plant-based interventions under investigation.

Results

The results showed that ∼15% of drug interventions in ClinicalTrials.gov were potentially plant related, with about 60% of them clustered within 10 taxonomic families. Further analysis of these plant-based interventions identified ∼3.7% of associated plant species as endangered as determined from the International Union for the Conservation of Nature Red List.

Discussion

The diversity of the plant kingdom has provided human civilization with life-sustaining food and medicine for centuries. There has been renewed interest in the investigation of botanicals as sources of new drugs, building on traditional knowledge about plant-based medicines. However, data about the plant-based biodiversity potential for therapeutics (eg, based on genetic or chemical information) are generally scattered across a range of sources and isolated from contemporary pharmacological resources. This study explored the potential to bridge biodiversity and biomedical knowledge sources.

Conclusions

The findings from this feasibility study suggest that there is an opportunity for developing plant-based drugs and further highlight taxonomic relationships between plants that may be rich sources for bioprospecting.     

Research and applications: Automatic lymphoma classification with sentence subgraph mining from pathology reports

Objective

Pathology reports are rich in narrative statements that encode a complex web of relations among medical concepts. These relations are routinely used by doctors to reason on diagnoses, but often require hand-crafted rules or supervised learning to extract into prespecified forms for computational disease modeling. We aim to automatically capture relations from narrative text without supervision.

Methods

We design a novel framework that translates sentences into graph representations, automatically mines sentence subgraphs, reduces redundancy in mined subgraphs, and automatically generates subgraph features for subsequent classification tasks. To ensure meaningful interpretations over the sentence graphs, we use the Unified Medical Language System Metathesaurus to map token subsequences to concepts, and in turn sentence graph nodes. We test our system with multiple lymphoma classification tasks that together mimic the differential diagnosis by a pathologist. To this end, we prevent our classifiers from looking at explicit mentions or synonyms of lymphomas in the text.

Results and Conclusions

We compare our system with three baseline classifiers using standard n-grams, full MetaMap concepts, and filtered MetaMap concepts. Our system achieves high F-measures on multiple binary classifications of lymphoma (Burkitt lymphoma, 0.8; diffuse large B-cell lymphoma, 0.909; follicular lymphoma, 0.84; Hodgkin lymphoma, 0.912). Significance tests show that our system outperforms all three baselines. Moreover, feature analysis identifies subgraph features that contribute to improved performance; these features agree with the state-of-the-art knowledge about lymphoma classification. We also highlight how these unsupervised relation features may provide meaningful insights into lymphoma classification.     

Research and applications: Automatic glaucoma diagnosis through medical imaging informatics

Background

Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease.

Objective

To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient''s genome information for screening.

Materials and methods

2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features.

Results and discussion

Receiver operating characteristic curves were plotted to compare AGLAIA-MII''s performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure.

Conclusions

AGLAIA-MII demonstrates for the first time the capability of integrating patients’ personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.