Applying Hidden Markov Models to Improve Homeopathic Management for Children with Oppositional Defiant Disorder

Hidden Markov Models (HMM) may increase homoeopathic therapy accuracy and results for children with Oppositional Defiant Disorder (ODD). HMM discovers hidden states and transitions by studying behavior and treatment response patterns, enabling personalized therapies. This technique predicts future behavior and treatment efficacy, lowering symptom severity and improving long-term results. Using HMM's capacity to identify patterns and dynamics, homoeopathic prescriptions may be adapted to individual behavior. This unique approach to recognizing and controlling ODD improves homoeopathic therapies for afflicted youngsters. The objective is to use HMM's predictive capacity to enhance ODD children's management and quality of life and optimize homoeopathic therapies. The ODD_HMM_Transition dataset shows that for 5 child symptoms, the values are 0.1-0.3 for symptom 1, 0.1-0.3 for symptom 2, 0.1-0.4, for symptom 3, 0.1-0.25 for symptom 4, and 0.1-0.25 for symptom 5. In another ODD_HMM_State dataset, symptoms are evaluated for 5 child therapies throughout 5 time periods. Symptom 1 of time 1 has a value of 0.02-0.8, symptom 2 of time 2 has the value of 0.02-0.7, symptom 3 of time 3 has the value of 0.02-0.6, symptom 4 of time 4 has the value of 0.02-0.5, and symptom 5 of time 5 has the value of 0.02_0.5.