{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T10:32:44Z","timestamp":1769596364212,"version":"3.49.0"},"reference-count":70,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014338","name":"National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber","doi-asserted-by":"publisher","award":["NIHR203473"],"award-info":[{"award-number":["NIHR203473"]}],"id":[{"id":"10.13039\/501100014338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Aim<\/jats:title>\n<jats:p>The coded prevalence of complex mental health difficulties in electronic health records, such as personality disorder and dysthymia,is much lower than expected from population surveys. We aimed to identify features in primary care records that might be useful in promoting greater recognition of complex mental health difficulties.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods and Findings<\/jats:title>\n<jats:p>We analysed Connected Bradford, an anonymised primary care database of approximately 1.15M citizens. We used multiple approaches to generate a large set of features representing multi-level collections of patient attributes across time and dimensions of healthcare. Feature sets included antecedent and concurrent problems (psychiatric, social and medical), patterns of prescription and service use and temporal stability of attendance. These were tested individually and in combination. We analysed the relationship between features and diagnostic codes using scaled mutual information.<\/jats:p>\n<jats:p>We identified 3,040 records satisfying our definition of complex mental health difficulties. This was 0.3% of the population compared to an expected prevalence of 3\u20135%. We generated &gt;500,000 features. The most informative feature was count of unique psychiatric diagnoses. Other features were identified, including binary features (e.g., presence or absence of prescription for antipsychotic medication), continuous features (e.g., entropy of non-attendance) and counts of features (e.g., concerning behaviours such as self-harm &amp; substance misuse). Several of these showed odds ratios\u2009&gt;=5 or\u2009&lt;=0.2 but low positive predictive value. We suggest this is due to the large number of \u201ccases\u201d being uncoded and, thus appearing as \u201ccontrols\u201d.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Complex mental health difficulties are poorly coded. We demonstrated the feasibility of using information theoretic approaches to develop a large set of novel features in electronic health records. While these are currently insufficient for diagnosis, several can act as prompts to consider further diagnostic assessment.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0322771","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T17:29:04Z","timestamp":1746725344000},"page":"e0322771","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identifying primary-care features associated with complex mental health difficulties"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7620-7110","authenticated-orcid":true,"given":"Ciar\u00e1n 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