How to Develop a Risk Prediction Smartphone App

Mauch, Jaclyn T.; Rios-Diaz, Arturo J.; Kozak, Geoffrey M.; Zhitomirsky, Alex; Broach, Robyn B.; Fischer, John P.

doi:10.1177/1553350620974827

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…A recent study developed an app that aids medical personnel in predicting incisional hernia occurrence [22]. ICD-9 codes are utilized to cross verify the eligible patients.…”

Section: Related Workmentioning

confidence: 99%

Predicting ICD-9 Codes Using Self-Report of Patients

Singaravelan

Hsieh²,

Liao

et al. 2021

Applied Sciences

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The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool.

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“…A recent study developed an app that aids medical personnel in predicting incisional hernia occurrence [22]. ICD-9 codes are utilized to cross verify the eligible patients.…”

Section: Related Workmentioning

confidence: 99%

Predicting ICD-9 Codes Using Self-Report of Patients

Singaravelan

Hsieh²,

Liao

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

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“…Nursing notes are used in numerous studies to predict ICD-9 codes [ 23 ]. An app was created by Mauch et al [ 24 ] to predict incisional hernias. Discharge summaries were used by Kavuluru et al [ 25 ] to predict the EMRs’ ICD codes.…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

et al. 2023

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The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient’s medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53–0.96; recall: 0.85–0.99; and F-score: 0.65–0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.

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