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Public Health Public Health

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

In Nature communications ; h5-index 260.0

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Gao Yue, Cai Guang-Yao, Fang Wei, Li Hua-Yi, Wang Si-Yuan, Chen Lingxi, Yu Yang, Liu Dan, Xu Sen, Cui Peng-Fei, Zeng Shao-Qing, Feng Xin-Xia, Yu Rui-Di, Wang Ya, Yuan Yuan, Jiao Xiao-Fei, Chi Jian-Hua, Liu Jia-Hao, Li Ru-Yuan, Zheng Xu, Song Chun-Yan, Jin Ning, Gong Wen-Jian, Liu Xing-Yu, Huang Lei, Tian Xun, Li Lin, Xing Hui, Ma Ding, Li Chun-Rui, Ye Fei, Gao Qing-Lei


General General

Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition

ArXiv Preprint

Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment. This disease knowledge is critical for many health-related and biomedical tasks, including consumer health question answering, medical language inference and disease name recognition. While pre-trained language models like BERT have shown success in capturing syntactic, semantic, and world knowledge from text, we find they can be further complemented by specific information like knowledge of symptoms, diagnoses, treatments, and other disease aspects. Hence, we integrate BERT with disease knowledge for improving these important tasks. Specifically, we propose a new disease knowledge infusion training procedure and evaluate it on a suite of BERT models including BERT, BioBERT, SciBERT, ClinicalBERT, BlueBERT, and ALBERT. Experiments over the three tasks show that these models can be enhanced in nearly all cases, demonstrating the viability of disease knowledge infusion. For example, accuracy of BioBERT on consumer health question answering is improved from 68.29% to 72.09%, while new SOTA results are observed in two datasets. We make our data and code freely available.

Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee


Surgery Surgery

Predicting mortality with applied machine learning: Can we get there?

In Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. International Symposium of Human Factors and Ergonomics in Healthcare

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are 'ready yet' for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

Patterson Emily S, Hansen C J, Allen Theodore T, Yang Qiwei, Moffatt-Bruce Susan D


Pathology Pathology

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors.

In World journal of clinical cases

As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.

Mao Wei-Bo, Lyu Jia-Yu, Vaishnani Deep K, Lyu Yu-Man, Gong Wei, Xue Xi-Ling, Shentu Yang-Ping, Ma Jun


Artificial intelligence, Artificial neural network, Deep learning, Gastrointestinal tumor, Tumor detection

General General

Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC).

In PeerJ

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

Kumar Sugandh, Patnaik Srinivas, Dixit Anshuman


Biomarker, Head and neck cancer, Machine learning, TNM stage, mRNA, microRNA

Surgery Surgery

Artificial intelligence in gastric cancer: Application and future perspectives.

In World journal of gastroenterology ; h5-index 103.0

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Niu Peng-Hui, Zhao Lu-Lu, Wu Hong-Liang, Zhao Dong-Bing, Chen Ying-Tai


Artificial intelligence, Deep learning, Gastric cancer, Image-based diagnosis, Machine learning, Prognosis prediction