Chatbots are intelligent conversational agents that can interact with users through natural languages. As chatbots can perform a variety of tasks, many companies have committed numerous resources to develop and deploy chatbots to enhance various business processes. However, we lack an up‐to‐date critical review that thoroughly examines both state‐of‐the‐art technologies and innovative applications of chatbots. In this review, we not only critically analyze the various computational approaches used to develop state‐of‐the‐art chatbots, but also thoroughly review the usability and applications of chatbots for various business sectors. We also identify gaps in chatbot‐related studies and propose new research directions to address the shortcomings of existing studies and applications. Our review advances both academic research and practical business applications of state‐of‐the‐art chatbots. We provide guidance for practitioners to fully realize the business value of chatbots and assist in making sensible decisions related to the development and deployment of chatbots in various business contexts. Researchers interested in the design and development of chatbots can also gain useful insights from our critical review and identify fruitful research topics and future research directions based on the research gaps discussed herein. This article is categorized under: Technologies > Machine Learning Application Areas > Business and Industry

Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74–0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73–0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

BackgroundThe sequencing platform BGISEQ-500 is based on DNBSEQ technology and provides high throughput with low costs. This sequencer has been widely used in various areas of scientific and clinical research. A better understanding of the sequencing process and performance of this system is essential for stabilizing the sequencing process, accurately interpreting sequencing results and efficiently solving sequencing problems. To address these concerns, a comprehensive database, SEQdata-BEACON, was constructed to accumulate the run performance data in BGISEQ-500.ResultsA total of 60 BGISEQ-500 instruments in the BGI-Wuhan lab were used to collect sequencing performance data. Lanes in paired-end 100 (PE100) sequencing using 10 bp barcode were chosen, and each lane was assigned a unique entry number as its identification number (ID). From November 2018 to April 2019, 2236 entries were recorded in the database containing 65 metrics about sample, yield, quality, machine state and supplies information. Using a correlation matrix, 52 numerical metrics were clustered into three groups signifying yield-quality, machine state and sequencing calibration. The distributions of the metrics also delivered information about patterns and rendered clues for further explanation or analysis of the sequencing process. Using the data of a total of 200 cycles, a linear regression model well simulated the final outputs. Moreover, the predicted final yield could be provided in the 15th cycle of the early stage of sequencing, and the corresponding R2 of the 200th and 15th cycle models were 0.97 and 0.81, respectively. The model was run with the test sets obtained from May 2019 to predict the yield, which resulted in an R2 of 0.96. These results indicate that our simulation model was reliable and effective.ConclusionsData sources, statistical findings and application tools provide a constantly updated reference for BGISEQ-500 users to comprehensively understand DNBSEQ technology, solve sequencing problems and optimize run performance. These resources are available on our website http://seqBEACON.genomics.cn:443/home.html.

The Chinese tapertail anchovy, Coilia nasus, is a socioeconomically important anadromous fish that migrates from near ocean waters to freshwater to spawn every spring. The analysis of genomic architecture and information of C. nasus were hindered by the previously released versions of reference genomes with gaps. Here, we report the assembly of a chromosome-level gap-free genome of C. nasus by incorporating high-coverage and accurate long-read sequence data with multiple assembly strategies. All 24 chromosomes were assembled without gaps, representing the highest completeness and assembly quality. We assembled the genome with a size of 851.67 Mb and used BUSCO to estimate the completeness of the assembly as 92.5%. Using a combination of de novo prediction, protein homology and RNA-seq annotation, 21,900 genes were functionally annotated, representing 99.68% of the total predicted protein-coding genes. The availability of gap-free reference genomes for C. nasus will provide the opportunity for understanding genome structure and function, and will also lay a solid foundation for further management and conservation of this important species.

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