{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:27:52Z","timestamp":1760239672704,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T00:00:00Z","timestamp":1607644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["108-2221-E-030 -013 -MY2"],"award-info":[{"award-number":["108-2221-E-030 -013 -MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predicting systems need clinical information collected by the medical staff from the patients in hospitals. In this paper, we propose a deep learning algorithm to classify disease types and identify diagnostic codes by using only the subjective component of progress notes in medical records. In this study, we have a dataset, consisting of about one hundred and sixty-eight thousand medical records, from a medical center, collected during 2003 and 2017. First, we apply standard text processing procedures to parse the sentences and word embedding techniques for vector representations. Next, we build a convolution neural network model on the medical records to predict the ICD-9 code by using a subjective component of the progress note. The prediction performance is evaluated by ten-fold cross-validation and yields an accuracy of 0.409, recall of 0.409 and precision of 0.436. If we only consider the \u201cchapter match\u201d of ICD-9 code, our model achieves an accuracy of 0.580, recall of 0.580, and precision of 0.582. Since our diagnostic code prediction model is solely based on subjective components (mainly, patients\u2019 self-report descriptions), the proposed approach could serve as a remote and self-diagnosis assistance tool, prior to seeking medical advice or going to the hospital. In addition, our work may be used as a primary evaluation tool for discomfort in the rural area where medical resources are restricted.<\/jats:p>","DOI":"10.3390\/s20247116","type":"journal-article","created":{"date-parts":[[2020,12,13]],"date-time":"2020-12-13T23:39:36Z","timestamp":1607902776000},"page":"7116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4818-3198","authenticated-orcid":false,"given":"Jia-Lien","family":"Hsu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}]},{"given":"Teng-Jie","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}]},{"given":"Chung-Ho","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111045, Taiwan"}]},{"given":"Anandakumar","family":"Singaravelan","sequence":"additional","affiliation":[{"name":"Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. 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