scholarly journals Identification of Microchip Implantation Events for Dogs and Cats in the VetCompass Australia Database

Animals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 423 ◽  
Author(s):  
Paul McGreevy ◽  
Sophie Masters ◽  
Leonie Richards ◽  
Ricardo J. Soares Magalhaes ◽  
Anne Peaston ◽  
...  
Keyword(s):  
New South ◽  
Epidemiological Studies ◽  
Data Repository ◽  
Free Text ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Unique Identifier ◽  
South Wales ◽  
Clinical Data Repository ◽  
The Ideal

In Australia, compulsory microchipping legislation requires that animals are microchipped before sale or prior to 3 months in the Australian Capital Territory, New South Wales, Queensland and Victoria, and by 6 months in Western Australia and Tasmania. Describing the implementation of microchipping in animals allows the data guardians to identify individual animals presenting to differing veterinary practices over their lifetimes, and to evaluate compliance with legislation. VetCompass Australia (VCA) collates electronic patient records from primary care veterinary practices into a database for epidemiological studies. VCA is the largest companion animal clinical data repository of its kind in Australia, and is therefore the ideal resource to analyse microchip data as a permanent unique identifier of an animal. The current study examined the free-text ‘examination record’ field in the electronic patient records of 1000 randomly selected dogs and cats in the VCA database. This field may allow identification of the date of microchip implantation, enabling comparison with other date fields in the database, such as date of birth. The study revealed that the median age at implantation for dogs presented as individual patients, rather than among litters, was 74.4 days, significantly lower than for cats (127.0 days, p = 0.003). Further exploration into reasons for later microchipping in cats may be useful in aligning common practice with legislative requirements.

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study (Preprint)

2020 ◽  
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

BACKGROUND The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. OBJECTIVE The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. METHODS We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. RESULTS In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). CONCLUSIONS A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.

scholarly journals Extracting information from free-text electronic patient records to identify practice-based evidence of the performance of coronary stents

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0182889 ◽  
Author(s):  
Yoon Seob Kim ◽  
Dukyong Yoon ◽  
JungHyun Byun ◽  
Hojun Park ◽  
Ahram Lee ◽  
...  
Keyword(s):  
Coronary Stents ◽  
Free Text ◽  
Patient Records ◽  
Electronic Patient Records

scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Large Scale ◽  
New South ◽  
New South Wales ◽  
Mental Health Disorders ◽  
Free Text ◽  
South Wales ◽  
Health Disorders

BACKGROUND Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. RESULTS The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

scholarly journals EP11 Using machine learning to recover unrecorded prehospital data

2021 ◽  
Vol 38 (9) ◽  
pp. A5.3-A6
Author(s):  
Thilo Reich ◽  
Adam Bancroft ◽  
Marcin Budka
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Vital Signs ◽  
Machine Learning Algorithms ◽  
Free Text ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Minority Class ◽  
South Central ◽  
Tree Classifier

BackgroundThe recording practices, of electronic patient records for ambulance crews, are continuously developing. South Central Ambulance Service (SCAS) adapted the common AVPU-scale (Alert, Voice, Pain, Unresponsive) in 2019 to include an option for ‘New Confusion’. Progressing to this new AVCPU-scale made comparisons with older data impossible. We demonstrate a method to retrospectively classify patients into the alertness levels most influenced by this update.MethodsSCAS provided ~1.6 million Electronic Patient Records, including vital signs, demographics, and presenting complaint free-text, these were split into training, validation, and testing datasets (80%, 10%, 10% respectively), and under sampled to the minority class. These data were used to train and validate predictions of the classes most affected by the modification of the scale (Alert, New Confusion, Voice).A transfer-learning natural language processing (NLP) classifier was used, using a language model described by Smerity et al. (2017) to classify the presenting complaint free-text.A second approach used vital signs, demographics, conveyance, and assessments (30 metrics) for classification. Categorical data were binary encoded and continuous variables were normalised. 20 machine learning algorithms were empirically tested and the best 3 combined into a voting ensemble combining three vital-sign based algorithms (Random Forest, Extra Tree Classifier, Decision Tree) with the NLP classifier using a Random Forest output layer.ResultsThe ensemble method resulted in a weighted F1 of 0.78 for the test set. The sensitivities/specificities for each of the classes are: 84%/ 90% (Alert), 73%/ 89% (Newly Confused) and 68%/ 93% (Voice).ConclusionsThe ensemble combining free text and vital signs resulted in high sensitivity and specificity when reclassifying the alertness levels of prehospital patients. This study demonstrates the capabilities of machine learning classifiers to recover missing data, allowing the comparison of data collected with different recording standards.

Word search performance for diagnoses of equine surgical colics in free-text electronic patient records

1998 ◽  
Vol 34 (2-3) ◽  
pp. 161-174 ◽  
Author(s):  
Leah Estberg ◽  
James T Case ◽  
Richard F Walters ◽  
Robert D Cardiff ◽  
Larry D Galuppo
Keyword(s):  
Free Text ◽  
Search Performance ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Word Search

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

10.2196/23725 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23725
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

Background The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. Objective The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. Methods We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. Results In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). Conclusions A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.

scholarly journals Primary care practitioner perceptions and attitudes of complementary medicine: a content analysis of free-text responses from a survey of non-metropolitan Australian general practitioners

2018 ◽  
Vol 19 (03) ◽  
pp. 246-255 ◽  
Author(s):  
Jon L. Wardle ◽  
David W. Sibbritt ◽  
Jon Adams
Keyword(s):  
Primary Care ◽  
Complementary Medicine ◽  
General Practitioners ◽  
New South ◽  
Free Text ◽  
Primary Concern ◽  
South Wales ◽  
Perceptions And Attitudes ◽  
Practitioner Perceptions ◽  
Attitudes And Knowledge

AimThis study examines GP perceptions, attitudes and knowledge of complementary medicine (CM), and to understand contextual factors that influence these perceptions, attitudes and knowledge.BackgroundCM use is increasing, and its influence on primary care becoming increasingly significant. Although general practitioners (GPs) often have central primary care gate-keeper roles within health systems, few studies have looked specifically at GPs’ perceptions, attitudes and knowledge of CM.MethodsA questionnaire was mailed to all 1486 GPs registered as practicing in non-metropolitan areas of New South Wales. The survey included one free-text qualitative question, where respondents were invited to highlight issues associated with CM in their own words. Free-text responses were analyzed qualitatively using thematic analysis.FindingsIn total, 585 GPs responded to the survey (adjusted response rate 40.1%), with 152 (26.0%) filling in the free-text question. Central themes which emerged were risk as a primary concern; opposition to, resistance to and the inappropriateness of complementary therapies; struggles with complexity and ambivalent tolerance.ConclusionGPs in Australia have a wide variety of perceptions toward CM. A minority of GPs have absolute views on CM, with most GPs having numerous caveats and qualifications of individual CM. Efficacy is only one aspect of CM critically evaluated by GPs when gauging support for individual therapies – risk, alignment with medical principles and an openness to exploring new avenues of treatment where others have failed, all appear to be equally important considerations when GPs form their views around CM.

scholarly journals Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0134208 ◽  
Author(s):  
Ehtesham Iqbal ◽  
Robbie Mallah ◽  
Richard George Jackson ◽  
Michael Ball ◽  
Zina M. Ibrahim ◽  
...  
Keyword(s):  
Mental Health ◽  
Adverse Drug Events ◽  
Free Text ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Case Register
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