Background: Advent of a novel pandemic requires development of faster medicine discovery protocol compared to traditional approach. Normally these are placebo controlled clinical trials, such trials usually involve high risk and a lot of time and money and repetitive exposure of the patients involved. In this study we gathered all the pathognomonic features of COVID-19 and translated them to homeopathic clinical features by using the known technique of repertorization, using a software. Top 10 ranked remedies were selected for further exploration. A surrogate model was created for simulation based on real patient data available in which all patients received a random combination of ranked repertorized homeopathic medicine. This output was then fed to a Neural Network. The NN learnt by recognizing patterns that mapped to patients’ initial state to the results of remedies administered, fluctuations were averaged out and different patient features were discovered. Thus, enabling the NN to better predict optimum homeopathy remedies than the traditional method stated before. Method: We designed a mathematical model based upon the principles of machine learning and created a virtual clinical trial first of 200 patients and then updated it to 800 in lieu of a real one. The Results of these Surrogate Digital Clinical trial [SDCT] were fed to a neural network. The Neural Network Clinical Learning [NNCL], clearly gave us a list of drugs and a possible genus epidemicus for this covid 19. These results were compared with actual field results to a data of 130 patients of covid like illnesses, covid or pneumonia treated on OPD basic or through tele medicine. Results: The conclusion was reached by comparing the simulated clinical trials, predictions by the NN and findings in the observational studies. Although the model shows reasonable stability, it is presented as a proof of concept, which should be further rigorously studied and tested by other homeopathic practitioners for further optimization if required. In this study merc sol merged prominently as a genus epidemicus. A further change in the remedy in the reference to a possible second or third wave could be predicted by adding some valuable clinical data to the model. Conclusion: This study could resolve many issues faced by homoeopathic practitioners across the globe and could predict a fairly accurate results making us better prepare in the field.
| Published in | American Journal of Clinical and Experimental Medicine (Volume 8, Issue 6) |
| DOI | 10.11648/j.ajcem.20200806.13 |
| Page(s) | 111-120 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Homoeopathy, Covid19, merc sol, genus epidemicus, Randomized Placebo Control Clinical Trial, Surrogate Digital Clinical Trial [SDTC], Neural Network Clinical Learning [NNCL]
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APA Style
Shailendra Vaishampayan, Joshua Joshi, Amruta Vaishampayan, Gulnaz Shaikh. (2020). Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning. American Journal of Clinical and Experimental Medicine, 8(6), 111-120. https://doi.org/10.11648/j.ajcem.20200806.13
ACS Style
Shailendra Vaishampayan; Joshua Joshi; Amruta Vaishampayan; Gulnaz Shaikh. Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning. Am. J. Clin. Exp. Med. 2020, 8(6), 111-120. doi: 10.11648/j.ajcem.20200806.13
AMA Style
Shailendra Vaishampayan, Joshua Joshi, Amruta Vaishampayan, Gulnaz Shaikh. Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning. Am J Clin Exp Med. 2020;8(6):111-120. doi: 10.11648/j.ajcem.20200806.13
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Download@article{10.11648/j.ajcem.20200806.13,
author = {Shailendra Vaishampayan and Joshua Joshi and Amruta Vaishampayan and Gulnaz Shaikh},
title = {Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning},
journal = {American Journal of Clinical and Experimental Medicine},
volume = {8},
number = {6},
pages = {111-120},
doi = {10.11648/j.ajcem.20200806.13},
url = {https://doi.org/10.11648/j.ajcem.20200806.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20200806.13},
abstract = {Background: Advent of a novel pandemic requires development of faster medicine discovery protocol compared to traditional approach. Normally these are placebo controlled clinical trials, such trials usually involve high risk and a lot of time and money and repetitive exposure of the patients involved. In this study we gathered all the pathognomonic features of COVID-19 and translated them to homeopathic clinical features by using the known technique of repertorization, using a software. Top 10 ranked remedies were selected for further exploration. A surrogate model was created for simulation based on real patient data available in which all patients received a random combination of ranked repertorized homeopathic medicine. This output was then fed to a Neural Network. The NN learnt by recognizing patterns that mapped to patients’ initial state to the results of remedies administered, fluctuations were averaged out and different patient features were discovered. Thus, enabling the NN to better predict optimum homeopathy remedies than the traditional method stated before. Method: We designed a mathematical model based upon the principles of machine learning and created a virtual clinical trial first of 200 patients and then updated it to 800 in lieu of a real one. The Results of these Surrogate Digital Clinical trial [SDCT] were fed to a neural network. The Neural Network Clinical Learning [NNCL], clearly gave us a list of drugs and a possible genus epidemicus for this covid 19. These results were compared with actual field results to a data of 130 patients of covid like illnesses, covid or pneumonia treated on OPD basic or through tele medicine. Results: The conclusion was reached by comparing the simulated clinical trials, predictions by the NN and findings in the observational studies. Although the model shows reasonable stability, it is presented as a proof of concept, which should be further rigorously studied and tested by other homeopathic practitioners for further optimization if required. In this study merc sol merged prominently as a genus epidemicus. A further change in the remedy in the reference to a possible second or third wave could be predicted by adding some valuable clinical data to the model. Conclusion: This study could resolve many issues faced by homoeopathic practitioners across the globe and could predict a fairly accurate results making us better prepare in the field.},
year = {2020}
}
TY - JOUR T1 - Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning AU - Shailendra Vaishampayan AU - Joshua Joshi AU - Amruta Vaishampayan AU - Gulnaz Shaikh Y1 - 2020/12/22 PY - 2020 N1 - https://doi.org/10.11648/j.ajcem.20200806.13 DO - 10.11648/j.ajcem.20200806.13 T2 - American Journal of Clinical and Experimental Medicine JF - American Journal of Clinical and Experimental Medicine JO - American Journal of Clinical and Experimental Medicine SP - 111 EP - 120 PB - Science Publishing Group SN - 2330-8133 UR - https://doi.org/10.11648/j.ajcem.20200806.13 AB - Background: Advent of a novel pandemic requires development of faster medicine discovery protocol compared to traditional approach. Normally these are placebo controlled clinical trials, such trials usually involve high risk and a lot of time and money and repetitive exposure of the patients involved. In this study we gathered all the pathognomonic features of COVID-19 and translated them to homeopathic clinical features by using the known technique of repertorization, using a software. Top 10 ranked remedies were selected for further exploration. A surrogate model was created for simulation based on real patient data available in which all patients received a random combination of ranked repertorized homeopathic medicine. This output was then fed to a Neural Network. The NN learnt by recognizing patterns that mapped to patients’ initial state to the results of remedies administered, fluctuations were averaged out and different patient features were discovered. Thus, enabling the NN to better predict optimum homeopathy remedies than the traditional method stated before. Method: We designed a mathematical model based upon the principles of machine learning and created a virtual clinical trial first of 200 patients and then updated it to 800 in lieu of a real one. The Results of these Surrogate Digital Clinical trial [SDCT] were fed to a neural network. The Neural Network Clinical Learning [NNCL], clearly gave us a list of drugs and a possible genus epidemicus for this covid 19. These results were compared with actual field results to a data of 130 patients of covid like illnesses, covid or pneumonia treated on OPD basic or through tele medicine. Results: The conclusion was reached by comparing the simulated clinical trials, predictions by the NN and findings in the observational studies. Although the model shows reasonable stability, it is presented as a proof of concept, which should be further rigorously studied and tested by other homeopathic practitioners for further optimization if required. In this study merc sol merged prominently as a genus epidemicus. A further change in the remedy in the reference to a possible second or third wave could be predicted by adding some valuable clinical data to the model. Conclusion: This study could resolve many issues faced by homoeopathic practitioners across the globe and could predict a fairly accurate results making us better prepare in the field. VL - 8 IS - 6 ER -
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