Yejin Kim

Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control.

The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20–49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartphone Addiction Proneness Scale (K-SAPS) for adults, the Behavioral Inhibition System/Behavioral Activation System questionnaire (BIS/BAS), the Dickman Dysfunctional Impulsivity Instrument (DDII), and the Brief Self-Control Scale (BSCS). In addition, participants reported their demographic information and smartphone usage pattern (weekday or weekend average usage hours and main use). We analyzed the data in three steps: (1) identifying predictors with logistic regression, (2) deriving causal relationships between SAP and its predictors using a Bayesian belief network (BN), and (3) computing optimal cut-off points for the identified predictors using the Youden index. Identified predictors of SAP were as follows: gender (female), weekend average usage hours, and scores on BAS-Drive, BAS-Reward Responsiveness, DDII, and BSCS. Female gender and scores on BAS-Drive and BSCS directly increased SAP. BAS-Reward Responsiveness and DDII indirectly increased SAP. We found that SAP was defined with maximal sensitivity as follows: weekend average usage hours > 4.45, BAS-Drive > 10.0, BAS-Reward Responsiveness > 13.8, DDII > 4.5, and BSCS > 37.4. This study raises the possibility that personality factors contribute to SAP. And, we calculated cut-off points for key predictors. These findings may assist clinicians screening for SAP using cut-off points, and further the understanding of SA risk factors.

Discovery of prostate specific antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy

BackgroundProstate specific antigen (PSA) is an important biomarker to monitor the response to the treatment, but has not been fully utilized as a whole sequence. We used a longitudinal biomarker PSA to discover a new prognostic pattern that predicts castration-resistant prostate cancer (CRPC) after androgen deprivation therapy.MethodsWe transformed the longitudinal PSA into a discrete sequence, used frequent sequential pattern mining to find candidate patterns from the sequences, and selected the most predictive and informative pattern among the candidates.ResultsPatients were less likely to be CRPC if, after PSA values reach nadir, the PSA decreases more than 0.048 ng/ml during a month, and the decrease occurs again. This pattern significantly increased the accuracy of predicting CRPC by supplementing information provided by existing PSA patterns such as pretreatment PSA.ConclusionsThis result can help clinicians to stratify men by the risk of CRPC and to determine the patient that needs intensive follow-up.

Federated Tensor Factorization for Computational Phenotyping

Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.

Is lymphovascular invasion a powerful predictor for biochemical recurrence in pT3 N0 prostate cancer? Results from the K-CaP database.

To assess the impact of lymphovascular invasion (LVI) on the risk of biochemical recurrence (BCR) in pT3 N0 prostate cancer, clinical data were extracted from 1,622 patients with pT3 N0 prostate cancer from the K-CaP database. Patients with neoadjuvant androgen deprivation therapy (n = 325) or insufficient pathologic or follow-up data (n = 87) were excluded. The primary endpoint was the oncologic importance of LVI, and the secondary endpoint was the hierarchical relationships for estimating BCR between the evaluated variables. LVI was noted in 260 patients (21.5%) and was significantly associated with other adverse clinicopathologic features. In the multivariate Cox regression analysis, LVI was significantly associated with an increased risk of BCR after adjusting for known prognostic factors. In the Bayesian belief network analysis, LVI and pathologic Gleason score were found to be first-degree associates of BCR, whereas prostate-specific antigen (PSA) level, seminal vesicle invasion, perineural invasion, and high-grade prostatic intraepithelial neoplasia were considered second-degree associates. In the random survival forest, pathologic Gleason score, LVI, and PSA level were three most important variables in determining BCR of patients with pT3 N0 prostate cancer. In conclusion, LVI is one of the most powerful adverse prognostic factors for BCR in patients with pT3 N0 prostate cancer.

Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization

Several studies have been conducted to evaluate the efficacy of statins in Korean and Asian patients. However, most previous studies only observed the percent reduction in low-density lipoprotein cholesterol (LDL-C) and did not consider the effects of various patient conditions simultaneously, such as abnormal test results, patient demographics, and prescribed drugs before taking a statin. Moreover, the characteristics of the patients whose percent reduction in LDL-C was higher than expected were not provided. Therefore, in this study, we aimed to derive meaningful phenotypes by using tensor factorization to observe the characteristics of the patients whose percent reduction in LDL-C was higher than expected among patients taking moderate-intensity statins. In addition, we used the derived phenotypes to predict how much the LDL-C levels of new patients decreased. We consequently identified eight phenotypes that represented the characteristics of the patients whose percent reduction in LDL-C was higher than expected. Moreover, the latent representations of the derived phenotypes achieved prediction performance similar to that obtained using the raw data. These results demonstrate that the derived phenotypes and latent representations are useful tools for observing the characteristics of patients and predicting LDL-C levels. Additionally, our findings provide direction on how to conduct clinical studies in the future.

Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins

To automatically extract medical concepts from raw electronic health records (EHRs), several applications based on machine learning techniques have been proposed. Among the various techniques, tensor factorization methods have attracted considerable attention because tensor representations can capture interactions among high-dimensional EHRs. Most of the existing tensor factorization methods for computational phenotyping are only designed to derive individual phenotypes that approximate the original data. However, deriving grouped phenotypes is desirable because patients form natural groups of interest (i.e., efficacy of treatment and disease categories). In this paper, we propose Supervised Non-negative Tensor Factorization with Multinomial Logistic Regression (SNTFL) to derive grouped phenotypes that are discriminative. We define a discriminative constraint to derive grouped phenotypes and jointly optimize a multinomial logistic regression during the tensor factorization process. Our case study on a hyperlipidemia dataset demonstrates that our proposed method obtains better discrimination on patient groups compared to the baselines and successfully discovers meaningful patient subgroups.

Smartphone dependence classification using tensor factorization

Excessive smartphone use causes personal and social problems. To address this issue, we sought to derive usage patterns that were directly correlated with smartphone dependence based on usage data. This study attempted to classify smartphone dependence using a data-driven prediction algorithm. We developed a mobile application to collect smartphone usage data. A total of 41,683 logs of 48 smartphone users were collected from March 8, 2015, to January 8, 2016. The participants were classified into the control group (SUC) or the addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and a face-to-face offline interview by a psychiatrist and a clinical psychologist (SUC = 23 and SUD = 25). We derived usage patterns using tensor factorization and found the following six optimal usage patterns: 1) social networking services (SNS) during daytime, 2) web surfing, 3) SNS at night, 4) mobile shopping, 5) entertainment, and 6) gaming at night. The membership vectors of the six patterns obtained a significantly better prediction performance than the raw data. For all patterns, the usage times of the SUD were much longer than those of the SUC. From our findings, we concluded that usage patterns and membership vectors were effective tools to assess and predict smartphone dependence and could provide an intervention guideline to predict and treat smartphone dependence based on usage data.