Lee, Jinseok

BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool

Atrial fibrillation (AF) affects three to five million Americans and is associated with significant morbidity and mortality. Existing methods to diagnose this paroxysmal arrhythmia are cumbersome and/or expensive. We hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4S for AF detection, we first used two databases, the MIT-BIH AF

We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approa

Sleep disorders are a common affliction for many people even though sleep is one of the most important factors in maintaining good physiological and emotional health. Numerous researchers have proposed various approaches to monitor sleep, such as polysomnography and actigraphy. However, such approaches are costly and often require overnight treatment in clinics. With this in mind, the research presented here has emerged from the question: "Can data be easily collected and analyzed without causin

We introduce a novel method for the automatic detection of atrial fibrillation (AF) using time-varying coherence functions (TVCF). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). The two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF; the second TVTF is produced by reversing the input and output signals. We found that the resultant TVCF bet

The demand for reflective-mode pulse oximetry to monitor oxygen saturation has been continuously increasing because it can be used at diverse measurement sites such as the feet, forehead, chest, and wrists. For the wrists, in particular, pulse oximeters are easily available in the form of a band or watch. In this study, we developed a reflectance pulse oximeter and used it to measure oxygen saturation levels at the fingertips and the wrist. We analyzed the performance of this oximeter to address

BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admiss

We present a compiler technique, which is based on Shasha and Snir's delay set analysis, to hide the underlying relaxed memory consistency model for an optimizing compiler for explicitly parallel programs. The compiler presents programmers with a sequentially consistent view of the underlying machine, irrespective of whether it follows a sequentially consistent model or a relaxed model. To hide the underlying relaxed memory consistency model and to guarantee sequential consistency, our algorithm

As mounting evidence suggests a higher incidence of adverse consequences, such as disruption of the immune system, among patients with a history of COVID-19, we aimed to investigate post-COVID-19 conditions on a comprehensive set of allergic diseases including asthma, allergic rhinitis, atopic dermatitis, and food allergy. We used nationwide claims-based cohorts in South Korea (K-CoV-N; n = 836,164; main cohort) and Japan (JMDC; n = 2,541,021; replication cohort A) and the UK Biobank cohort (UKB

This paper presents an in-memory computing (IMC) macro in 28nm for fully row/column-parallel matrix-vector multiplication (MVM), exploiting precise capacitor-based analog computation to extend from binary input-vector elements to 5-b input-vector elements, for 16x increase in energy efficiency and 5x increase in throughput. The 1152(row)x256(col.) macro employs multi-level input drivers based on a digital-switch DAC implementation, which preserve compute accuracy well beyond the 8-b resolution o

Knowledge Tracing (KT) is to trace the knowledge of students as they solve a sequence of problems represented by their related skills. This involves abstract concepts of students' states of knowledge and the interactions between those states and skills. Therefore, a KT model is designed to predict whether students will give correct answers and to describe such abstract concepts. However, existing methods either give relatively low prediction accuracy or fail to explain those concepts intuitively

We hypothesized that an iPhone 4s can be used to detect atrial fibrillation (AF) based on its ability to record a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4s for AF detection, 25 prospective subjects with AF pre- and post-electrical cardioversion were recruited. Using an iPhone 4s, we collected 2-minute pulsatile time series. We investigated 3 statistical methods consisting of the Root Mean Square of Su

We present particle filtering (PF) algorithms for an accurate respiratory rate extraction from pulse oximeter recordings over a broad range: 12-90 breaths/min. These methods are based on an autoregressive (AR) model, where the aim is to find the pole angle with the highest magnitude as it corresponds to the respiratory rate. However, when SNR is low, the pole angle with the highest magnitude may not always lead to accurate estimation of the respiratory rate. To circumvent this limitation, we pro

BACKGROUND: The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. OBJECTIVE: The goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage. METHODS: We developed an AI model that predicts severity based on

We present a particle filtering algorithm, which combines both time-invariant (TIV) and time-varying autoregressive (TVAR) models for accurate extraction of breathing frequencies (BFs) that vary either slowly or suddenly. The algorithm sustains its robustness for up to 90 breaths/min (b/m) as well. The proposed algorithm automatically detects stationary and nonstationary breathing dynamics in order to use the appropriate TIV or TVAR algorithm and then uses a particle filter to extract accurate r