RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

Anonymized electronic medical records are an increasingly popular source of research data. However, these datasets often lack race and ethnicity information. This creates problems for researchers modeling human disease, as race and ethnicity are powerful confounders for many health exposures and treatment outcomes; race and ethnicity are closely linked to population-specific genetic variation. We showed that deep neural networks generate more accurate estimates for missing racial and ethnic information than competing methods (e.g., logistic regression, random forest). RIDDLE yielded significantly better classification performance across all metrics that were considered: accuracy, cross-entropy loss (error), and area under the curve for receiver operating characteristic plots (all $p < 10^{-6}$). We made specific efforts to interpret the trained neural network models to identify, quantify, and visualize medical features which are predictive of race and ethnicity. We used these characterizations of informative features to perform a systematic comparison of differential disease patterns by race and ethnicity. The fact that clinical histories are informative for imputing race and ethnicity could reflect (1) a skewed distribution of blue- and white-collar professions across racial and ethnic groups, (2) uneven accessibility and subjective importance of prophylactic health, (3) possible variation in lifestyle, such as dietary habits, and (4) differences in background genetic variation which predispose to diseases

Brain-like synapse thin-film transistors using oxide semiconductor channels and solid electrolytic gate insulators

Human brain has astonishing advantages such as tolerance to system faults, low power consumption, and adaptive learning ability from pre-obtained data unlike conventional computer system with Von Neumann architecture. One of the most attractive benefits of human brain is an ability of parallel processing. Considering these advantages, new types of devices featured to emulate the human brain are quite demanding. These artificially designed systems are called neuromorphic systems, in which neurons are inspired by electronic signals applied to synapses. They can be useful components in artificial perception and action systems. Recently, many types of electronic devices mimicking the functions of human brain have been energetically studied [1-2]. However, these devices have following limitations. First, it needs additional external memories for storing synapse information because CMOS devices cannot work as nonvolatile memories. Second, conventional Si-based electronic technology is not compatible with large-area electronics implemented on glass or flexible substrates. In this work, we propose new type of synapse device with thin-film transistor (TFT) configuration for realizing simple device structure and operation for synaptic learning, in which oxide semiconductor In-Ga-Zn-O (IGZO) and solid electrolytes, such as poly(4-vinylphenol) - sodium β-alumina (PVP-SBA) and polypropylene carbonate (PPC), are employed as active channel and solid-electrolytic gate insulators for TFT operations, respectively. Solid electrolytes can be promising for controlling the synaptic weights by exploiting the movements of charged ions within the electrolytes. Lithium and sodium ions are transported in PPC and PVP-SBA, respectively. Synaptic weights can be determined as the modulated channel conductance and be estimated from the gradual variations in drain currents. Figs. 1(a) and 1(b) show cross-sectional view of fabricated synapse TFT using PVP-SBA gate insulator and schematic structures of biological synapse & neuron, respectively. Figs. 2(a) and 2(b) showed the variations in output drain currents as functions of two input pulse parameters, amplitude and width. First, the voltage pulses were repeatedly applied to the gate terminal one-hundred times with a fixed pulse width of 50 ms. The output drain current gradually increased with increasing the number of pulses [Fig. 2(a)]. When the pulse amplitude was varied to 10 and 20 V at ten-time applications, the value of obtained output drain current markedly increased and the current ratio for two conditions were calculated to be approximately 2.6. These results clearly suggest that the synaptic weights could be more fortified when larger voltage signals (20 V) were employed for synaptic operations. In other words, it means that the quantity of residual memory or response significantly increased with stronger stimuli. When pulse signals with a fixed amplitude of 10 V were applied with variations in pulse widths of 10, 50, and 100 ms. The output drain currents more quickly increased for longer pulses [Fig. 2(b)]. The obtained current ratios of 100-ms conditions to 10-ms condition at ten- and one-hundred-time applications were estimated to be approximately 3.1 and 2.1, respectively. These results emulate the changes in learning time at a single event. Our proposed synapse TFTs can be a suitable candidate for the next-generation low-power large-area electronics such as neuromorphic systems and flexible bio-mimicking devices. Please click Additional Files below to see the full abstract

Application of Depth-Averaged 2D Numerical Model for Evaluation of the Vegetal Resistance in a Natural River

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv