Optimizing Voice Recognition Informatic Robots for Effective Communication in Outpatient Settings.

Aim/Objective Within the dynamic healthcare technology landscape, this research aims to explore patient inquiries within outpatient clinics, elucidating the interplay between technology and healthcare intricacies. Building upon the initial intelligent guidance robot implementation shortcomings, this investigation seeks to enhance informatic robots with voice recognition technology. The objective is to analyze users' vocal patterns, discern age-associated vocal attributes, and facilitate age differentiation through subtle vocal nuances to enhance the efficacy of human-robot communication within outpatient clinical settings. Methods This investigation employs a multi-faceted approach. It leverages voice recognition technology to analyze users' vocal patterns. A diverse dataset of voice samples from various age groups was collected. Acoustic features encompassing pitch, formant frequencies, spectral characteristics, and vocal tract length are extracted from the audio samples. The Mel Filterbank and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for speech and audio processing tasks alongside machine learning algorithms to assess and match vocal patterns to age-related traits. Results The research reveals compelling outcomes. The incorporation of voice recognition technology contributes to a significant improvement in human-robot communication within outpatient clinical settings. Through accurate analysis of vocal patterns and age-related traits, informatic robots can differentiate age through nuanced verbal cues. This augmentation leads to enhanced contextual understanding and tailored responses, significantly advancing the efficiency of patient interactions with the robots. Conclusion Integrating voice recognition technology into informatic robots presents a noteworthy advancement in outpatient clinic settings. By enabling age differentiation through vocal nuances, this augmentation enhances the precision and relevance of responses. The study contributes to the ongoing discourse on the dynamic evolution of healthcare technology, underscoring the complex synergy between technological progression and the intricate realities within healthcare infrastructure. As healthcare continues to metamorphose, the seamless integration of voice recognition technology marks a pivotal stride in optimizing human-robot communication and elevating patient care within outpatient settings.

Performance Review of Intelligent Guidance Robot at the Outpatient Clinic Setting.

This study analyzes the implementation of a mobile intelligent guidance robot to roam hospital outpatient services and discusses the application's effect and experience. The data consist of human-robot verbal communications in November 2019 to analyze and evaluate the application according to the service volume, accuracy, and functions. Statistically, the accuracy of correct output by the intelligent guidance robot when answering related questions in outpatient services was significantly lower than the manufacturer's claimed expected accuracy. Furthermore, the utilization review of the intelligent guidance robot was surprisingly unexpected. Therefore, applying an intelligent guidance robot is not limited to merely providing directions and navigation functions but can be valuable in improving public health literacy. Nevertheless, the hospital should meet patients' needs by expanding intelligent guidance robots' service functions and increasing patient experience to finetune the application through further experiments and design.

Noninvasive Detection of Hepatocellular Carcinoma with Circulating Tumor DNA Features and α-Fetoprotein.

Liver cancer is the fifth-most common cancer worldwide, with the third-highest rate of cancer-related mortality. Hepatocellular carcinoma (HCC) is the leading pathologic subtype, contributing 85% to 90% of cases of primary liver cancer. Most HCC patients are diagnosed at an advanced stage at which treatment is not curative. This study assessed the performance of a newly developed blood-based assay that utilizes genomic features and protein markers for the early detection of HCC. Two cancer-associated hallmarks, copy-number aberrations (CNA) and fragment size (FS), were characterized by shallow whole-genome sequencing of cell-free DNA and utilized to differentiate cancer patients from healthy subjects. As a clinically implemented biomarker of HCC, plasma α-fetoprotein (AFP) was also used with the genomic surrogates to optimize the detection of HCCs. The sensitivity of AFP ≥20.0 µg/L in detecting HCC was 57.9%. The combined genomic classifier CNA + FS via cell-free DNA shallow whole-genome sequencing identified nearly half of AFP-negative HCC patients (43.8%). By integrating CNA, FS as well as AFP (HCCseek), 75.0% sensitivity was achieved at 98.0% specificity, resulting in 92.6% accuracy, with 58.6% sensitivity in stage I HCC. The quantitative output of HCCseek was correlated with the severity of the disease (tumor size, stage, and recurrence-free survival). In summary, this study describes an efficient, noninvasive, and cost-effective method to detect HCC.

[Development of remote wireless mobile voiding diary and a report of its objective voiding in 20 young people].

OBJECTIVE: To find out the data of the micturitions in healthy young people with the remote & mobile voiding diary monitoring system. METHODS: Twenty healthy young people were studied and ten of them were female. The ages ranged from 22 to 35 years (the mean age: 27.4 years). The females were 22-33 years old (the mean age: 26.4 years ) and the males 24-35 years old (the mean age: 28.4 years). With the remote & mobile voiding diary monitoring system, their voiding information was collected. Through bluetooth, the voiding information was sent to the patient's intelligent cell phone from the collector, then stored directly by intelligent cell phone and wirelessly transmitted to the workstation in the hospital. All of them completed the voiding diaries for 7 days and the data were analyzed. RESULTS: The average micturition of the young healthy people was 5.6 times (3.4-7.4) per 24 hours,in which 5.3 (3.4-7.3) times were in the daytime and 0.3 (0-1.3)times in the night. The functional voiding volume was 318 mL (66-642 mL). The mean voiding volume in 24 hours was 1 724 mL (1152-2 415 mL), in which 1 289 mL (786-2 039 mL) was in the daytime and 435 mL (292-805 mL) in the night. The mean drinking volume was 1 022 mL (453-1 721 mL) in the daytime and 7 mL (0-43 mL) in the night. The nocturia index (Ni) was 1.03, the nocturnal polyuria index (NPi) 26%, and the nocturnal bladder capacity index (NBCi) 0.27. CONCLUSION: The remote & mobile voiding diary monitoring system can help us get the objective voiding information from young health people for the first time. It is reliable, maneuverable and can be widely used in clinical diagnosis.