Article Title: Visualizing Patient Trajectories and Disorder Co-occurrences in Child and Adolescent Mental Health Description: This project implements patient similarity visualization (age group, gender, and F90 diagnosis) and displays their trajectories in a timeline plot. Additionally, it presents co-occurrences of the F90 diagnosis (typically associated with hyperkinetic disorders such as ADHD) with other diagnoses. The clustering component represents episode-based information from Child and Adolescent Mental Health Services (CAMHS). This work aids in understanding the complexity and progression of patient conditions over time. Project Setup Instructions: Download all files Unzip the compressed files Arrange the files as per the following project structure CAMHS-Patient-Trajectories-and-Comorbidities/ ├── ClinicalEvaluationQuestionnaire/ │ ├── ClinicalEvaluationQuestionnaire.docx ├── Figures/ │ ├── NetworkGraph/ │ ├── pattern/ │ ├── trajectory/ ├── NetworkGraph/ │ ├── 1_CooccuringNetworkGraph.ipynb │ ├── 2_CooccuringNetworkGraph_adjusted.ipynb │ ├── 3_CooccuringNetworkGraph_HigherLevel3.ipynb │ ├── 4_CooccuringNetworkGraph_HigherLevel1.ipynb │ ├── allpackages.py │ ├── network_graph_backend.py │ ├── Comparing co-occurring graphs_All 6 patient groups in Original_Third_First_Levels.ipynb │ ├── Comparing co-occurring graphs_Original_Third_First_Levels_for each patient group.ipynb │ ├── For_PreSchooler_F_ADHD_comparing_different_Levels.ipynb ├── PatientTrajectory/ │ ├── 1_DatasetCreation.ipynb │ ├── 2_countdistribution.ipynb │ ├── 3_MatplotlibPatientTrajectory.ipynb │ ├── 4_AllSimilarPatientTrajectory.ipynb │ ├── all_similar_patient_trajectory.py │ ├── allpackages.py │ ├── matplot_patient_trajectory.py ├── .env.example ├── .gitignore ├── README.md ├── requirements.txt 4. Create a virtual environment or create conda environment 5. Install the requirement in requirements.txt:pip install -r requirements.txt