{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:36:45Z","timestamp":1740202605874,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Effective self-management can decrease up to 50% of heart failure hospitalizations. Unfortunately, self-management by patients with heart failure remains poor. This pilot study aimed to explore the use of text-mining to identify heart failure patients with ineffective self-management. We first built a comprehensive self-management vocabulary based on the literature and clinical notes review. We then randomly selected 545 heart failure patients treated within Partners Healthcare hospitals (Boston, MA, USA) and conducted a regular expression search with the compiled vocabulary within 43,107 interdisciplinary clinical notes of these patients. We found that 38.2% (n = 208) patients had documentation of ineffective heart failure self-management in the domains of poor diet adherence (28.4%), missed medical encounters (26.4%) poor medication adherence (20.2%) and non-specified self-management issues (e.g., &amp;ldquo;compliance issues&amp;rdquo;, 34.6%). We showed the feasibility of using text-mining to identify patients with ineffective self-management. More natural language processing algorithms are needed to help busy clinicians identify these patients.<\/jats:p>","DOI":"10.3233\/978-1-61499-658-3-856","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:29:25Z","timestamp":1740158965000},"source":"Crossref","is-referenced-by-count":0,"title":["Mining Clinicians' Electronic Documentation to Identify Heart Failure Patients with Ineffective Self-Management: A Pilot Text-Mining Study"],"prefix":"10.3233","author":[{"family":"Topaz Maxim","sequence":"additional","affiliation":[]},{"family":"Radhakrishnan Kavita","sequence":"additional","affiliation":[]},{"family":"Lei Victor","sequence":"additional","affiliation":[]},{"family":"Zhou Li","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Nursing Informatics 2016"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T18:17:42Z","timestamp":1740161862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-657-6&spage=856&doi=10.3233\/978-1-61499-658-3-856"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-658-3-856","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}