Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs.

OBJECTIVES: Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. METHODS: New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. RESULTS: Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. CONCLUSION: Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

What are we drinking? Assessment of water quality in an urban city of Punjab, India.

INTRODUCTION: Ground water is the ultimate and most suitable fresh water resource for human consumption in the urban areas of India. Studies regarding ground water quality have shown that the higher rate of exploration as compared to the rate of recharging, inappropriate dumping of solid, as well as liquid waste, lack of strict enforcement of law has led to the deterioration of ground water quality. The present study was thus, carried out to evaluate physicochemical, as well as a microbiological profile of tap water, and filtered water in urban areas of Patiala, Punjab. MATERIALS AND METHODS: The three zones under Municipal Corporation and two areas under Public Health Department were chosen according to the simple random sampling from Patiala city. From each area, 10 houses were chosen according to the systematic random sampling technique (n = 50). Water was taken from two sources, tap water, and from the water filter. Two samples were taken from each source one for the physicochemical analysis and another for bacteriological analysis. The samples which were sent for bacteriological assessment were collected in a sterile container. RESULTS: The number of water samples found to be within desirable limits with respect to physicochemical parameters were significantly more with the filter water sample than the tap water samples. Suspicious/unsatisfactory microbiological quality of water was observed in 28% and 4% of tap and filter water samples, respectively. CONCLUSION: The results indicate that certain chemical parameters such as hardness, chloride, and fluoride levels were beyond the permissible limits. Therefore, we recommend that home filters should be installed, serviced appropriately, and their water quality should be checked routinely. Also, any leak from sewage pipes should be promptly repaired to prevent contamination of drinking water.

Pattern of Dermatophytoses in and Around Varanasi.

Four hundered cases of dermatophytoses confirmed microscopically, were analysed. it was more common in 11 to 20 years age group mid males. Females acquired tinea corporis more commonly. Tinea cruris was more frequent in males. Ma3dmum cases were of tinea cruris followed by tinea cruris et corpmis and tinea corporis in that order. the isolation rate on culture was 70.75%. The relative incidence of Trichophyton rubrum was maximum and it was found in all clinical entities.