Personalized disease prediction using a CNN-based similarity learning method

Suo, Qiuling; Ma, Fenglong; Yuan, Ye; Huai, Mengdi; Zhong, Wenhui; Zhang, Aidong; Gao, Jing

doi:10.1109/bibm.2017.8217759

Cited by 74 publications

(28 citation statements)

References 16 publications

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“…CNN filters applied to EHRs usually perform a one-side convolution operation across time via filter sliding. A filter can be defined as k 2 R h N , where h is the variable window size and N is the embedding dimension 60,61 . Our approach differs in that it processes embedding matrices as they were RGB images carrying a third "depth" dimension.…”

Section: The Convae Architecturementioning

confidence: 99%

Deep representation learning of electronic health records to unlock patient stratification at scale

et al. 2020

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Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into lowdimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson's disease, and Alzheimer's disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.

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Section: The Convae Architecturementioning

confidence: 99%

Deep representation learning of electronic health records to unlock patient stratification at scale

et al. 2020

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“…The following three benchmark methods are evaluated in our study: temporal multiple measurements case-based reasoning (MMCBR) [ 10 ], time-aware long short-term memory (T-LSTM) network [ 11 ], and time fusion convolutional neural network (CNN) [ 12 ]. The results show that the proposed method significantly outperforms all the benchmarks, as measured by recall, F-measure, and area under the curve (AUC) while maintaining comparable precision values.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complication Phenotypes

Sheng

Liu

et al. 2021

J Med Internet Res

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Background Acute diseases present severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians’ care and management of patients with acute diseases by predicting crucial complication phenotypes for a timely diagnosis and treatment. However, effective phenotype predictions require several challenges to be overcome. First, patient data collected in the early stages of an acute disease (eg, clinical data and laboratory results) are less informative for predicting phenotypic outcomes. Second, patient data are temporal and heterogeneous; for example, patients receive laboratory tests at different time intervals and frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for predicting complication phenotypes. Objective To predict crucial complication phenotypes among patients with acute diseases, we propose a novel, deep learning–based method that uses recurrent neural network–based sequence embedding to represent disease progression while considering temporal heterogeneities in patient data. Our method incorporates a latent regulator to alleviate data insufficiency constraints by accounting for the underlying mechanisms that are not observed in patient data. The proposed method also includes cost-sensitive learning to address imbalanced outcome distributions in patient data for improved predictions. Methods From a major health care organization in Taiwan, we obtained a sample of 10,354 electronic health records that pertained to 6545 patients with peritonitis. The proposed method projects these temporal, heterogeneous, and clinical data into a substantially reduced feature space and then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Moreover, our method employs cost-sensitive learning to further increase the predictive performance. Results We evaluated the efficacy of the proposed method for predicting two hepatic complication phenotypes in patients with peritonitis: acute hepatic encephalopathy and hepatorenal syndrome. The following three benchmark techniques were evaluated: temporal multiple measurement case-based reasoning (MMCBR), temporal short long-term memory (T-SLTM) networks, and time fusion convolutional neural network (CNN). For acute hepatic encephalopathy predictions, our method attained an area under the curve (AUC) value of 0.82, which outperforms temporal MMCBR by 64%, T-SLTM by 26%, and time fusion CNN by 26%. For hepatorenal syndrome predictions, our method achieved an AUC value of 0.64, which is 29% better than that of temporal MMCBR (0.54). Overall, the evaluation results show that the proposed method significantly outperforms all the benchmarks, as measured by recall, F-measure, and AUC while maintaining comparable precision values. Conclusions The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes and offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios that are characterized by insufficient patient clinical data availability, temporal heterogeneities, and imbalanced distributions of important patient outcomes.

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“…According to their study CNN achieved 76% by micro

in label-to-chapter. Suo et al [ 23 ] used convolutional neural networks to create a model to predict diabetes mellitus, obesity, and chronic obstructive pulmonary disease with accuracy up to 0.74. Cheng et al [ 24 ] also used convolutional neural networks to establish a model to predict future recurrence of chronic heart failure and chronic obstructive pulmonary disease.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the authors in [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] used patient profile, clinical examination reports and physician diagnosis results to establish models for the prediction of certain diseases that are commonly seen or with high mortality rates. On the contrary, in this paper, we only make use of patients’ self-report data (i.e., the subjective component in the progress note of EMR) to establish a predictive model for a variety of diseases.…”

Section: Introductionmentioning

confidence: 99%

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

Hsu

Hsieh³

et al. 2020

Sensors

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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 “chapter match” 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’ 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.

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Personalized disease prediction using a CNN-based similarity learning method

Cited by 74 publications

References 16 publications

Deep representation learning of electronic health records to unlock patient stratification at scale

Deep representation learning of electronic health records to unlock patient stratification at scale

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning–Based Method to Predict Crucial Complication Phenotypes

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

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