Predicting a diagnosis of ankylosing spondylitis using primary care health records – a machine learning approach

Kennedy, Jonathan; Kennedy, Natasha; Cooksey, Roxanne; Choy, Ernest; Siebert, Stefan; Rahman, Muhammad Azizur; Brophy, Sinéad

doi:10.1101/2021.04.22.21255659

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Three health conditions affecting the nervous system were addressed [114][115][116], which were post stroke spasticity, epilepsy specifically mortality four years before and after its diagnosis (G40) [115], and a rare neurodegenerative disease progressive supra-nuclear palsy (G23) [116]. A few studies investigated musculoskeletal and The musculoskeletal and connective tissue condition were back pain (M54) prognosis within PHC settings [109], ankylosing spondylitis (M45) [110]. The gastrointestinal and kidney diseases were examined in four studies, namely inflammatory bowel diseases (K50-K52), including Crohn's disease and ulcerative colitis [26,71], peptic ulcers (K27)/gastroesophageal reflux (K21), and chronic kidney disease (N18) [90,133].…”

Section: Plos Onementioning

confidence: 99%

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem,

Whitelaw,

Schauberger

et al. 2023

PLoS ONE

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With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models’ development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer’s disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

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Section: Plos Onementioning

confidence: 99%

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem,

Whitelaw,

Schauberger

et al. 2023

PLoS ONE

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Section: Other Health Conditionsmentioning

confidence: 99%

“…Regarding musculoskeletal and connective tissue disorders [98][99][100][122][123][124], back pain (M54) prognosis within PHC settings could be predicted through focusing on patient function-related predictors more than on resolving pain [99]. A study revealed that models can be created using only data from medical records and had prediction values of 70-80% for identifying persons who are at risk of acquiring ankylosing spondylitis (M45) [100]. Two digestive health conditions were addressed in two studies [36,62], which were inflammatory bowel diseases (K50-K52), including Crohn's disease and ulcerative colitis, and peptic ulcers (K27)/gastroesophageal reflux (K21).…”

Section: Other Health Conditionsmentioning

confidence: 99%

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem

Whitelaw

Schauberger

et al. 2022

Preprint

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Aim: With the rapid advances in technology and data science, machine learning (ML) is being adopted by the health care sector; but there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC). To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify the health conditions targeted by ML in PHC. Methods: We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included any primary study addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. We performed literature screening, data extraction, and risk of bias assessment. Health conditions were categorized according to international classification of diseases. Extracted date were analyzed quantitatively and qualitatively. Results: We identified 109 studies investigating 42 health conditions. These studies included 273 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 82% of the studies were retrospective. 76.6% of the studies reported diagnostic predictive ML models. 77% of all reported models aimed for models’ development without external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were Alzheimer’s disease and diabetes mellitus. Conclusions: To the best of our knowledge, this is the first review to investigate the extent of the health conditions targeted by the ML prediction models within PHC settings. Our study provides an important summary on the presently available ML models in PHC, which can be used in further research and implementation efforts.

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Barriers and facilitators in diagnosing axial spondyloarthritis: a qualitative study

Hay,

Packham,

Prior

et al. 2024

Rheumatol Int

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Introduction Diagnosis of axial spondyloarthritis (axSpA) is frequently delayed for years after symptom onset. However, little is known about patient and healthcare professional (HCP) perspectives on barriers and facilitators in axSpA diagnosis. This study explored the experiences and perceptions of both groups regarding the factors affecting the timely diagnosis of axSpA. Method Semi-structured interviews with patients with axSpA and axSpA-interested HCPs from the United Kingdom (UK) were performed by telephone or Microsoft Teams and focussed on the individuals’ perspective of the diagnostic journey for axSpA. Interview transcripts were thematically analysed. Results Fourteen patients with axSpA (10 female, 4 male) and 14 UK based HCPs were recruited, the latter comprising of 5 physiotherapists, 4 General Practitioners, 3 rheumatologists, a nurse, and an occupational therapist. Barriers to diagnosis identified by patients and HCPs were: difficult to diagnose, a lack of awareness, unclear referral pathways, patient behaviour and patient/HCP communication. Patient-identified facilitators of diagnosis were patient advocacy, clear referral processes and pathways, increased awareness, and serendipity. HCPs identified promoting awareness as a facilitator of diagnosis, along with symptom recognition, improvements to healthcare practice and patient/HCP communications. Conclusion Poor communication and a lack of understanding of axSpA in the professional and public spheres undermine progress towards timely diagnosis of axSpA. Improving communication and awareness for patients and HCPs, along with systemic changes in healthcare (such as improved referral pathways) could reduce diagnostic delay.

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Predicting a diagnosis of ankylosing spondylitis using primary care health records – a machine learning approach

Cited by 3 publications

References 13 publications

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Barriers and facilitators in diagnosing axial spondyloarthritis: a qualitative study

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