A method for inferring medical diagnoses from patient similarities

BMC Medicine

Roded Sharan¹  Russ B Altman²  Eytan Ruppin¹  Gideon Y Stein³  Assaf Gottlieb² 

[1] Blavatnik School of Computer Science, Tel-Aviv University, Klausner St., Tel Aviv 69978, Israel;Departments of Bioengineering & Genetics, Stanford University, 318 Campus Drive, Stanford 94305, USA;Department of Internal Medicine "B", Beilinson Hospital, Rabin Medical Center, 39 Jabotinski St., Petah-Tikva 49100, Israel

关键词: Diagnosis prediction;    Electronic health records;    Patient similarity;   

Others  :  856932
DOI  :  10.1186/1741-7015-11-194

 received in 2013-04-12, accepted in 2013-07-24,  发布年份 2013

【 摘 要 】

Background

Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.

Methods

Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.

Results

Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses.

Conclusions

Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.

【 授权许可】

   
2013 Gottlieb et al.; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Warner HR, Haug P, Bouhaddou O, Lincoln M, Warner H Jr, Sorenson D, Williamson JW, Fan C: ILIAD as an expert consultant to teach differential diagnosis. In Proceedings of the Annual Symposium on Computer Application in Medical Care. 4720 Montgomery Lane, Suite 500 Bethesda, Maryland 20814: American Medical Informatics Association; 1988:371-376.
• [2]Garg AX, Adhikari NKJ, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. JAMA 2005, 293:1223-1238.
• [3]Kawamoto K, Houlihan CA, Balas EA, Lobach DF: Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005, 330:765.
• [4]Wright A, Sittig DF: A four-phase model of the evolution of clinical decision support architectures. Int J Med Inform 2008, 77:641-649.
• [5]Hunt DL, Haynes RB, Hanna SE, Smith K: Effects of computer-based clinical decision support systems on physician performance and patient outcomes. JAMA 1998, 280:1339-1346.
• [6]Spiegelhalter DJ, Knill-Jones RP: Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology. J R Stat Soc Ser A (General) 1984, 147:35-77.
• [7]Wang SJ, Middleton B, Prosser LA, Bardon CG, Spurr CD, Carchidi PJ, Kittler AF, Goldszer RC, Fairchild DG, Sussman AJ: A cost-benefit analysis of electronic medical records in primary care. Am J Med 2003, 114:397-403.
• [8]Kaushal R, Bates DW: Information technology and medication safety: what is the benefit? Qual Saf Health Care 2002, 11:261-265.
• [9]Dean BB, Lam J, Natoli JL, Butler Q, Aguilar D, Nordyke RJ: Review: use of electronic medical records for health outcomes research: a literature review. Med Care Res Rev 2009, 66:611-638.
• [10]Marcos M, Maldonado JA, Martinez-Salvador B, Bosca D, Robles M: Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility. J Biomed Inform 2013, 46:676-689.
• [11]Romano MJ, Stafford RS: Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med 2011, 171:897-903.
• [12]Wu J, Roy J, Stewart WF: Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med Care 2010, 48:S106-113.
• [13]Wang F, Hu J, Sun J: Medical prognosis based on patient similarity and expert feedback. 2012, 1799-1802. [Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE]
• [14]Iezzoni LI: Assessing quality using administrative data. Ann Intern Med 1997, 127:666-674.
• [15]Schneider RJ, Seibert K, Passe S, Little C, Gee T, Lee Iii BJ, Mike V, Young CW: Prognostic significance of serum lactate dehydrogenase in malignant lymphoma. Cancer 1980, 46:139-143.
• [16]Katus HA, Remppis A, Neumann FJ, Scheffold T, Diederich KW, Vinar G, Noe A, Matern G, Kuebler W: Diagnostic efficiency of troponin T measurements in acute myocardial infarction. Circulation 1991, 83:902-912.
• [17]Popescu M, Khalilia M: Improving disease prediction using ICD-9 ontological features. 2011, 1805-1809. [IEEE]
• [18]Jaccard P: Nouvelles recherches sur la distribution florale. Bul Soc Vaudoise Sci Nat 1908, 44:223-270.
• [19]Gottlieb A, Stein GY, Ruppin E, Sharan R: PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011, 7:496.
• [20]Dargie H: Heart failure post-myocardial infarction: a review of the issues. Heart 2005, 91(Suppl 2):ii3-ii6.
• [21]Smith DL: Anemia in the elderly. Iron Disorders Institute Guide to Anemia 2009, 9:96-103.
• [22]Kacprzak M, Kidawa M, Zielinska M: Fever in myocardial infarction: is it still common, is it still predictive? Cardiol J 2012, 19:369-373.