al-BERT: a semi-supervised denoising technique for disease prediction.

BACKGROUND: Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlations and enhancing the model's predictive accuracy and utility in medical diagnostics. METHOD: To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance. RESULT: In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction. CONCLUSION: The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT.

Meridian study on the response current affected by acupuncture needling direction.

Acupuncture manipulation with needling direction is important for the therapeutic effect based on traditional Chinese medicine theory. However, there is controversy over directional manipulation and therapeutic effect, despite some research showing that acupuncture manipulations may have something to do with therapeutic effect. Moreover, research usually focuses on the therapeutic effects on the acupoints and acupuncture time rather than exploring the manipulation method. This study applies a semiconductor analyzer to investigate the effects of acupuncture manipulation. 10 healthy participants were recruited for the study. We used a cross-over design to compare the effect of different manipulation on individuals. This study employed an Agilent B1500A semiconductor analyzer to investigate the electric characteristics of meridians under directional supplementation and draining manipulation. We measured the electric current of meridians under different manipulation, and compared the difference between supplementation and draining manipulation in healthy individuals. The electric current was significantly larger in supplementation manipulation compared to draining manipulation in the meridians (P < .001). The measured electric current in the same manipulation methods did not show a statistical difference between meridians (P = .094). The different directional manipulation result in different electric currents in humans. Our finding implies that the supplementation and draining manipulation may result in different therapeutic effects clinically as the description of traditional Chinese medicine theory. Therefore, directional manipulation may need to be taken into consideration in future acupuncture studies and clinical management.

Analysis of Meridian Flow Direction by Electrical Stimulation Method.

Meridians constitute the theoretical foundation of acupuncture in traditional Chinese medicine (TCM), and they have been described for 2000 years. Classical TCM advocates for the directionality of meridians. Finding an accurate method to verify this directionality is an important goal of TCM doctors and researchers. In this study, we objectively explored the physical properties of meridians, such as response current from electrical stimulation, to explore their directionality. The Agilent B1500A semiconductor measurement analyzer was utilized to input the alternating current waveforms and detect the response current on the meridians. The results showed that the direction of the meridians influences the intensity of the response current. Therefore, the mechanisms behind the directions of ion transportation and the meridians were investigated using the response time and the intensity of the response current. Thereafter, we propose a model to explain this mechanism. Afterward, a comparison between the direction of the meridian in this experiment and ancient Chinese medicine classics was performed.

Investigation of the Capacitance-Voltage Electrical Characteristics of Thin-Film Transistors Caused by Hydrogen Diffusion under Negative Bias Stress in a Moist Environment.

In this study, the impact of moisture on the electrical characteristics of an amorphous In-Ga-Zn-O thin-film transistor (a-IGZO TFT) was investigated. In commercial applications of such TFTs, high stability and quality performance in humid environments are essential. During TFT operation under ambient moisture, the electrolysis of water molecules occurs via the tip electric field effect. Hydrogen diffuses from the etch-stop layer or back-channel into the main channel under a negative electric field. The hydrogen atoms act as shallow donors (which causes the carrier concentration in the channel to rise), causing the threshold voltage (VTH) to shift in the negative direction. Hydrogen diffusion from the overlap of the source/drain and gate electrodes to the channel center caused by the tip electric field induces a significant barrier lowering and VTH shifts in a short-channel device. However, under negative bias stress (NBS) in ambient moisture, the negative VTH shift is more obvious in short- than in long-channel devices, indicating suppressed hydrogen diffusion in long-channel devices. This is attributed to the electrolysis of water by the tip electric field at the source, drain, and gate electrodes, which causes hydrogen to diffuse to the center of the channel. Here, a novel physical model of the capacitance-voltage (C-V) electrical property changes under ambient moisture is proposed, based on the early appearance of abnormalities in the C-V measurements. The electrolysis of water caused by the tip electric field and electrical abnormalities caused by hydrogen diffusion into the a-IGZO active layer are explained by this model. A secondary-ion mass spectrometry analysis shows that hydrogen content in the channel generally increases under NBS in ambient moisture. The degradation behavior due to moisture in a-IGZO is clarified. Thus, inhibiting the tip electric field may benefit future flexible-display and gas-sensing applications.