The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal

Background@#Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy.However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that may be used to determine which of these methods would work on an individual patient. @*Objective@#This study aimed at developing a machine learning algorithm that improved the prediction of the outcome of wart removing using cryotherapy and immunotherapy. @*Methods@#Support vector machines, core vector machines, random forest, k-nearest neighbours, multilayer perceptron and binary logistic regression was applied on datasets in to create a model that predicted the outcome of an immunotherapy and cryotherapy treatments based on sex, age, time that has passed since last treatment, number of warts, type, area, diameter and result of treatment. @*Results@#The average accuracy of the immunotherapy prediction was 88.6%±8.0% while the same measure for cryotherapy prediction was 94.6%±4.0%. The most efficient immunotherapy and cryotherapy model had an accuracy of 100%, predicating the correct treatment outcome when applied to all test cases. @*Conclusion@#This study suc-cessfully created a machine learning model that improved the prediction ability of the outcome of immunotherapy and cryotherapy for wart removal. This model created a more in-depth guideline for understanding is immunotherapy would work and took a new approach to cryotherapy.

The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal

Background@#Warts can be extremely painful conditions that may be associated with localised bleeding and discharge. They are commonly treated by cryotherapy or immunotherapy.However, each of these therapies have discomforting side effects and are no official dermatological guideline that exist that may be used to determine which of these methods would work on an individual patient. @*Objective@#This study aimed at developing a machine learning algorithm that improved the prediction of the outcome of wart removing using cryotherapy and immunotherapy. @*Methods@#Support vector machines, core vector machines, random forest, k-nearest neighbours, multilayer perceptron and binary logistic regression was applied on datasets in to create a model that predicted the outcome of an immunotherapy and cryotherapy treatments based on sex, age, time that has passed since last treatment, number of warts, type, area, diameter and result of treatment. @*Results@#The average accuracy of the immunotherapy prediction was 88.6%±8.0% while the same measure for cryotherapy prediction was 94.6%±4.0%. The most efficient immunotherapy and cryotherapy model had an accuracy of 100%, predicating the correct treatment outcome when applied to all test cases. @*Conclusion@#This study suc-cessfully created a machine learning model that improved the prediction ability of the outcome of immunotherapy and cryotherapy for wart removal. This model created a more in-depth guideline for understanding is immunotherapy would work and took a new approach to cryotherapy.

PACS Implementation Challenges in a Public Healthcare Institution: A South African Vendor Perspective / 대한의료정보학회지

OBJECTIVES: Conventional radiological processes have been replaced by digital images and information technology systems within South Africa and other developing countries. Picture Archiving and Communication Systems (PACS) technology offers many benefits to institutions, medical personnel and patients; however, the implementation of such systems can be a challenging task. It has been documented that South Africa has been using PACS for more than a decade in public hospitals with moderate success. The aim of this study was to identify and describe the PACS challenges endured by PACS vendors during implementation in the South African public healthcare sector. METHODS: This was achieved by engaging in a methodological approach that was qualitative in nature collecting data through semi structured interviews from 10 PACS experts/participants which were later analysed qualitatively. RESULTS: The findings show that PACS vendors have countless challenges, some of which include space, insufficient infrastructure, image storage capacity, system maturity and vendor related concerns. It was clear that the PACS experts readily offered contextually appropriate descriptions of their encounters during PACS implementations in South African public healthcare institutions. CONCLUSIONS: PACS vendors anticipate these challenges when facing a public healthcare institution and it is recommended that the hospital management and potential PACS stakeholders be made aware of these challenges to mitigate their effects and aid in a successful implementation.

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance / 대한의료정보학회지

OBJECTIVES: Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance. METHODS: A genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured. RESULTS: This study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%. CONCLUSIONS: The study shows that there is a significant improvement in the prediction ability of ANRS gold standard.