No abstract

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.

Type 2 diabetes mellitus (T2DM) is a metabolic disorder that is characterized by insulin resistance and hyperglycemia. Leptin inhibits the glucose-stimulated insulin secretion, and leptin receptors are present on b cells as well as on fat cells, thus enabling leptin to modulate both insulin secretion and action. Therefore, leptin (LEP) and leptin receptor (LEPR) genes could play a role in the regulation of glucose and insulin after an oral glucose load. For the association study of LEP and LEPR with T2DM and metabolic traits, 752 women from Seoul National University Hospital (SNUH data) and 532 women from the Korean Health and Genome Study (KHGS data) were selected. Using the SNUH data, we identified that LEP2632G.A and 14998A.C polymorphisms were marginally associated with T2DM, LEP14950G.A was significantly associated with several metabolic traits, and LEPR15193G.A, 17187A.C, 127265G.A, 135861T.C, and 152289A.G showed strongly significant association with body mass index (BMI). We observed reproducibility of these results using the KHGS data; LEP14950G.A and 14998A.C were significantly associated with systolic blood pressure and low-density lipoprotein cholesterol level, respectively. In conclusion, we observed that several polymorphisms in LEPR that had previous reports of association with BMI were significantly replicated in our samples and newly found that some variations of LEP were associated with T2DM and metabolic traits.

Objective: By using the Korean Pancreatic Cancer (K-PaC) registry, we compared the clinical outcomes of FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GNP) in patients with metastatic pancreatic cancer (MPC). Methods: We constructed a web-based database of 3748 anonymized patients diagnosed with pancreatic ductal adenocarcinoma. MPC patients who received first-line FFX or GNP were enrolled. Overall survival (OS), progression-free survival, grade III to IV toxicity, and cross-over treatment were analyzed. Results: A total of 413 patients (232 vs. 181, FFX vs. GNP; all data are presented in this sequence) were eligible. Median age was 63 years (60 vs. 69 y) with 43% (39% vs. 47%) comprising female individuals. The major metastatic sites were the liver (64%), peritoneum (25%), and distant lymph nodes (18%). The median OS was 11.5 versus 12.7 months (hazard ratio [HR]=0.87, 95% confidence interval [CI]: 0.68-1.12, P=0.286), and median progression-free survival was 7.5 versus 8.1 months (HR=0.92, 95% CI: 0.70-1.20, P=0.517), respectively. The frequency of grade III to IV febrile neutropenia was higher in the FFX group (18% vs. 11%, P=0.040), and that of peripheral neuropathy was higher in the GNP group (8% vs. 14%, P=0.046). The chance to receive second-line chemotherapy was higher in the GNP group (45% vs. 56%, P=0.036). In the cross-over treatment, the median OS of the FFX-GNP group (n=43) and the GNP-FFX group (n=47) was 16.8 versus 17.7 months (HR=0.79, 95% CI: 0.44-1.41, P=0.425). Conclusions: FFX and GNP showed similar efficacy and comparable toxicity in MPC patients. Although the GNP group had a higher chance to receive second-line chemotherapy, they did not have improved overall survival.

Background Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information. Objective The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach. Methods In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type. Results In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables. Conclusions A machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.

Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. Data were collected from electronic medical records, genomic sequences, tumor biopsy specimens, and national cancer control initiative databases in the National Cancer Center (NCC), Korea. Data were de-identified and stored in a safe and independent space. Unstructured clinical data were standardized and incorporated into cancer registries and linked to cancer genome sequences and tumor biopsy specimens. Finally, national cancer control initiative data from the public domain were independently organized and linked to cancer registries. We constructed a system for integrating and providing various cancer data called the Korea Cancer Big Data Platform (K-CBP). Although the K-CBP could be used for cancer research, the legal and regulatory aspects of data distribution and usage need to be addressed first. Nonetheless, the system will continue collecting data from cancer-related resources that will hopefully facilitate precision-based research.

ObjectivesRemote medical services have been expanding globally, and this is expansion is steadily increasing. It has had many positive effects, including medical access convenience, timeliness of service, and cost reduction. The speed of research and development in remote medical technology has been gradually accelerating. Therefore, it is expected to expand to enable various high-tech information and communications technology (ICT)-based remote medical services. However, the current state lacks an appropriate security framework that can resolve security issues centered on the Internet of things (IoT) environment that will be utilized significantly in telemedicine.MethodsThis study developed a medical service-oriented frame work for secure remote medical services, possessing flexibility regarding new service and security elements through its service-oriented structure. First, the common architecture of remote medical services is defined. Next medical-oriented secu rity threats and requirements within the IoT environment are identified. Finally, we propose a "service-oriented security frame work for remote medical services" based on previous work and requirements for secure remote medical services in the IoT.ResultsThe proposed framework is a secure framework based on service-oriented cases in the medical environment. A com parative analysis focusing on the security elements (confidentiality, integrity, availability, privacy) was conducted, and the analysis results demonstrate the security of the proposed framework for remote medical services with IoT.ConclusionsThe proposed framework is service-oriented structure. It can support dynamic security elements in accordance with demands related to new remote medical services which will be diversely generated in the IoT environment. We anticipate that it will enable secure services to be provided that can guarantee confidentiality, integrity, and availability for all, including patients, non-patients, and medical staff.

Background There has been significant effort in attempting to use health care data. However, laws that protect patients’ privacy have restricted data use because health care data contain sensitive information. Thus, discussions on privacy laws now focus on the active use of health care data beyond protection. However, current literature does not clarify the obstacles that make data usage and deidentification processes difficult or elaborate on users’ needs for data linking from practical perspectives. Objective The objective of this study is to investigate (1) the current status of data use in each medical area, (2) institutional efforts and difficulties in deidentification processes, and (3) users’ data linking needs. Methods We conducted a cross-sectional online survey. To recruit people who have used health care data, we publicized the promotion campaign and sent official documents to an academic society encouraging participation in the online survey. Results In total, 128 participants responded to the online survey; 10 participants were excluded for either inconsistent responses or lack of demand for health care data. Finally, 118 participants’ responses were analyzed. The majority of participants worked in general hospitals or universities (62/118, 52.5% and 51/118, 43.2%, respectively, multiple-choice answers). More than half of participants responded that they have a need for clinical data (82/118, 69.5%) and public data (76/118, 64.4%). Furthermore, 85.6% (101/118) of respondents conducted deidentification measures when using data, and they considered rigid social culture as an obstacle for deidentification (28/101, 27.7%). In addition, they required data linking (98/118, 83.1%), and they noted deregulation and data standardization to allow access to health care data linking (33/98, 33.7% and 38/98, 38.8%, respectively). There were no significant differences in the proportion of responded data needs and linking in groups that used health care data for either public purposes or commercial purposes. Conclusions This study provides a cross-sectional view from a practical, user-oriented perspective on the kinds of data users want to utilize, efforts and difficulties in deidentification processes, and the needs for data linking. Most users want to use clinical and public data, and most participants conduct deidentification processes and express a desire to conduct data linking. Our study confirmed that they noted regulation as a primary obstacle whether their purpose is commercial or public. A legal system based on both data utilization and data protection needs is required.