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1.  Automated Reconciliation of Radiology Reports and Discharge Summaries 
We study machine learning techniques to automatically identify limb abnormalities (including fractures, dislocations and foreign bodies) from radiology reports. For patients presenting to the Emergency Room (ER) with suspected limb abnormalities (e.g., fractures) there is often a multi-day delay before the radiology report is available to ER staff, by which time the patient may have been discharged home with the possibility of undiagnosed fractures. ER staff, currently, have to manually review and reconcile radiology reports with the ER discharge diagnosis; this is a laborious and error-prone manual process. Using radiology reports from three different hospitals, we show that extracting detailed features from the reports to train Support Vector Machines can effectively automate the identification of limb fractures, dislocations and foreign bodies. These can be automatically reconciled with a patient’s discharge diagnosis from the ER to identify a number of cases where limb abnormalities went undiagnosed.
PMCID: PMC4765582  PMID: 26958213
2.  Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology 
The Australasian Medical Journal  2013;6(5):301-307.
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.
doi:10.4066/AMJ.2013.1651
PMCID: PMC3674422  PMID: 23745152
Limb fractures; emergency department; radiology reports; classification; rule-based method; machine learning
3.  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.
Results
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.
PMCID: PMC3845773  PMID: 24303284
4.  Improving self-care of patients with chronic disease using online personal health record 
The Australasian Medical Journal  2012;5(9):517-521.
Background
Effective management of chronic diseases such as prostate cancer is important. Research suggests a tendency to use self-care treatment options such as over-the-counter (OTC) complementary medications among prostate cancer patients. The current trend in patient-driven recording of health data in an online Personal Health Record (PHR) presents an opportunity to develop new data-driven approaches for improving prostate cancer patient care. However, the ability of current online solutions to share patients’ data for better decision support is limited. An informatics approach may improve online sharing of self-care interventions among these patients. It can also provide better evidence to support decisions made during their self-managed care.
Aims
To identify requirements for an online system and describe a new case-based reasoning (CBR) method for improving self-care of advanced prostate cancer patients in an online PHR environment.
Method
A non-identifying online survey was conducted to understand self-care patterns among prostate cancer patients and to identify requirements for an online information system. The pilot study was carried out between August 2010 and December 2010. A case-base of 52 patients was developed.
Results
The data analysis showed self-care patterns among the prostate cancer patients. Selenium (55%) was the common complementary supplement used by the patients. Paracetamol (about 45%) was the commonly used OTC by the patients.
Conclusion
The results of this study specified requirements for an online case-based reasoning information system. The outcomes of this study are being incorporated in design of the proposed Artificial Intelligence (Al) driven patient journey browser system. A basic version of the proposed system is currently being considered for implementation.
doi:10.4066/AMJ.2012.1358
PMCID: PMC3477782  PMID: 23115588
Advanced Prostate Cancer; Self-care; Patient Journey; PHR; Case-based reasoning
5.  Identifying Symptom Groups from Emergency Department Presenting Complaint Free Text using SNOMED CT 
AMIA Annual Symposium Proceedings  2011;2011:1446-1453.
Patients presenting to Emergency Departments may be categorised into different symptom groups for the purpose of research and quality improvement. The grouping is challenging due to the variability in the way presenting complaints are recorded by clinical staff. This work proposes analysis of the presenting complaint free-text using the semantics encoded in the SNOMED CT ontology. This work demonstrates a validated prototype system that can classify unstructured free-text narratives into patient’s symptom group. A rule-based mechanism was developed using variety of keywords to identify the patient’s symptom group. The system was validated against the manual identification of the symptom groups by two expert clinical research nurses on 794 patient presentations from six participating hospitals. The comparison of system results with one clinical research nurse showed 99.3% sensitivity; 80.0% specificity and 0.9 F-score for identifying “chest pain” symptom group.
PMCID: PMC3243271  PMID: 22195208

Results 1-5 (5)