A Review Paper on Classification of Stem Cell Transplant to Identify the High Survival Rate

A patient undergoing hematopoietic stem cell transplant faces various risk factors and has become the standard of care for congenital or acquired disorders of the hematopoietic system or with chemo-sensitive, radiosensitive or immunosensitive malignancies. Analyzing and classifying the data from past transplant can enhance the understanding of the factors leading to highest survival rates among the patients. Over the last few decades there has been tremendous use of technology in this field. Stem cell transplant remains a dangerous procedure as it requires significant infrastructure and a network of specialists from all fields of medicine. In this paper, we are using a classification algorithm known as Support Vector Machine to classify the patients who have undergone stem cell transplant with high odds of survival. We are also keeping track of information about the donors within the family and outside the family which has a direct impact in the prioritization of resources. Classification of this information is useful to create the need for a global perspective for all cell, tissue, and organ transplants and to reveal statistical structure with potential implications in evidence-based prioritization of resources. Machine-learning techniques proved useful in analyzing the correct data from various datasets as this techniques were previously been considered too complex to analyze. DOI: 10.17762/ijritcc2321-8169.16043

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p