Evidence-Based Detection of Pancreatic Canc

This study is an effort to develop a tool for early detection of pancreatic cancer using evidential reasoning. An evidential reasoning model predicts the likelihood of an individual developing pancreatic cancer by processing the outputs of a Support Vector Classifier, and other input factors such as smoking history, drinking history, sequencing reads, biopsy location, family and personal health history. Certain features of the genomic data along with the mutated gene sequence of pancreatic cancer patients was obtained from the National Cancer Institute (NIH) Genomic Data Commons (GDC). This data was used to train the SVC. A prediction accuracy of ~85% with a ROC AUC of 83.4% was achieved. Synthetic data was assembled in different combinations to evaluate the working of evidential reasoning model. Using this, variations in the belief interval of developing pancreatic cancer are observed. When the model is provided with an input of high smoking history and family history of cancer, an increase in the evidential reasoning interval in belief of pancreatic cancer and support in the machine learning model prediction is observed. Likewise, decrease in the quantity of genetic material and an irregularity in the cellular structure near the pancreas increases support in the machine learning classifier’s prediction of having pancreatic cancer. This evidence-based approach is an attempt to diagnose the pancreatic cancer at a premalignant stage. Future work includes using the real sequencing reads as well as accurate habits and real medical and family history of individuals to increase the efficiency of the evidential reasoning model. Next steps also involve trying out different machine learning models to observe their performance on the dataset considered in this study

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Risk factors associated with the occurrence of autoimmune diseases in adult coeliac patients

Objectives. Autoimmune diseases (AD) may be associated with coeliac disease (CD), but specific risk factors have been poorly investigated. The aim of this study was to assess the spectrum of AD and its specific risk factors associated in a series of adult coeliac patients. Materials and Methods. We performed a single-center case-control study including adult newly diagnosed CD patients. To evaluate the risk factors of the association between AD and CD, 341 coeliac patients included were categorized on the basis of AD presence: 91 cases with at least one AD and 250 controls without AD were compared for clinical, serological, and histological features. Eighty-seven cases were age-gender-matched with 87 controls. Results. Among 341 CD patients, 26.6% of CD patients had at least one AD. Endocrine and dermatological diseases were the most prevalent AD encountered: autoimmune thyroiditis was present in 48.4% of cases, psoriasis in 17.6%, and type I diabetes and dermatitis herpetiformis in 11%, respectively. At logistic regression, factors associated with AD were a positive 1st-degree family history of AD (OR 3.7, 95% CI 1.93–7), a body mass index ≥ 25 kg/m2 at CD diagnosis (OR 2.95%, CI 1.1–3.8), and long standing presentation signs/symptoms before CD diagnosis (>10 years) (OR 2.1, 95% CI 1.1–3.7). Analysis on age-gender-matched patients confirmed these results. Conclusions. CD patients with family history of AD, overweight at CD diagnosis, and a delay of CD diagnosis had an increased risk of having another AD. The benefit of CD screening in these specific subsets of patients with AD awaits further investigation

Audit of Antenatal Testing of Sexually Transmissible Infections and Blood Borne Viruses at Western Australian Hospitals

In August 2007, the Western Australian Department of Health (DOH) released updated recommendations for testing of sexually transmissible infections (STI) and blood-borne viruses (BBV) in antenates. Prior to this, the Royal Australian & New Zealand College of Obstetricians & Gynaecologists (RANZCOG) antenatal testing recommendations had been accepted practice in most antenatal settings. The RANZCOG recommends that testing for HIV, syphilis, hepatitis B and C be offered at the first antenatal visit. The DOH recommends that in addition, chlamydia testing be offered. We conducted a baseline audit of antenatal STI/BBV testing in women who delivered at selected public hospitals before the DOH recommendations. We examined the medical records of 200 women who had delivered before 1st July 2007 from each of the sevenWAhospitals included in the audit. STI and BBV testing information and demographic data were collected. Of the 1,409 women included, 1,205 (86%) were non-Aboriginal and 200 (14%) were Aboriginal. High proportions of women had been tested for HIV (76%), syphilis (86%), hepatitis C (87%) and hepatitis B (88%). Overall, 72% of women had undergone STI/BBV testing in accordance with RANZCOG recommendations. However, chlamydia testing was evident in only 18% of records. STI/BBV prevalence ranged from 3.9% (CI 1.5– 6.3%) for chlamydia, to 1.7% (CI 1–2.4%) for hepatitis C, 0.7% (CI 0.3–1.2) for hepatitis B and 0.6% (CI 0.2–1) for syphilis. Prior to the DOH recommendations, nearly three-quarters of antenates had undergone STI/BBV testing in accordance with RANZCOG recommendations, but less than one fifth had been tested for chlamydia. The DOH recommendations will be further promoted with the assistance of hospitals and other stakeholders. A future audit will be conducted to determine the proportion of women tested according to the DOH recommendations. The hand book from this conference is available for download Published in 2008 by the Australasian Society for HIV Medicine Inc © Australasian Society for HIV Medicine Inc 2008 ISBN: 978-1-920773-59-

Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

Malaria in Sri Lanka: Current knowledge on transmission and control

Malaria / Disease vectors / Waterborne diseases / Environmental effects / Public health / Economic impact / Social impact / Sri Lanka