{
  "format": "dataset-manifest/1",
  "slug": "ai-driven-mental-health-monitoring",
  "version": 2,
  "seed": "ai-driven-mental-health-monitoring:1",
  "spec": {
    "format": "dataset/1",
    "slug": "ai-driven-mental-health-monitoring",
    "name": "Mental Health Insights",
    "tagline": "AI-driven monitoring for effective mental health support.",
    "description": "Clinicians monitor patients' mental health using advanced technology to provide timely and effective support. They track vital data points such as consent for information sharing, the development of personalized crisis plans, and the dynamics of the patient-clinician relationship. By analyzing vocal patterns and interactions through AI chat messages, clinicians gain insights into patient well-being and can adjust treatment as necessary. The daily interactions with patients often manifest in routine journal entries, which provide a foundation for understanding general trends, while the creation of detailed crisis plans and tailored recommendations, though less frequent, plays a critical role for those at heightened risk.\n\nIn this dataset, you will find several interconnected tables that together offer a comprehensive view of mental health management. The 'patients' table holds individual patient records, detailing demographics and consent status. Each patient is linked to a clinician in the 'clinicians' table, which catalogs the professionals responsible for care. The 'crisis_plans' table connects specific patients with their customized plans, providing insight into preventative measures tailored to individual needs. Meanwhile, 'vocal_patterns' captures audio data that can be analyzed for emotional indicators, and 'ai_interactions' documents conversations between patients and AI applications designed for mental health support. This intricate web of data allows for a deep dive into the effectiveness of various approaches in mental health care.\n\nYou can explore questions such as how the frequency of AI interactions correlates with mental health improvements, or which vocal patterns are most indicative of anxiety or depression. This synthetic dataset provides an excellent opportunity for developers to practice data analysis and visualization techniques in a healthcare context, helping to create innovative solutions that enhance mental health support.",
    "domain": "healthcare",
    "shape": "ledger",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "mental health",
      "AI monitoring",
      "patient data",
      "crisis plans",
      "vocal patterns",
      "healthcare analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "patients": {
        "description": "This table contains records of patients, including their demographics and consent for monitoring. Each patient can have multiple entries in related tables.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each patient."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the patient."
          },
          "age": {
            "kind": "int",
            "min": 18,
            "max": 80,
            "description": "Age of the patient.",
            "distribution": "normal"
          },
          "consent": {
            "kind": "bool",
            "trueRate": 0.9,
            "description": "Indicates whether the patient has consented to monitoring."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the patient record was created.",
            "dayProfile": "business"
          }
        }
      },
      "clinicians": {
        "description": "This table includes information about clinicians, linking them to patients. Clinicians can have multiple patients.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each clinician."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the clinician."
          },
          "specialty": {
            "kind": "enum",
            "values": [
              "Psychiatry",
              "Psychology",
              "Counseling"
            ],
            "description": "The specialty of the clinician."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The email address of the clinician."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the clinician record was created.",
            "dayProfile": "business"
          }
        }
      },
      "crisis_plans": {
        "description": "This table records crisis plans for patients, linking to the patients table. Each plan is unique to a patient and may include multiple entries.",
        "rows": {
          "per": "patients",
          "via": "patient_id",
          "min": 1,
          "max": 3,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each crisis plan."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "Reference to the patient the crisis plan belongs to."
          },
          "plan_details": {
            "kind": "sentence",
            "description": "Details of the crisis plan."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the crisis plan was created.",
            "dayProfile": "business"
          }
        }
      },
      "vocal_patterns": {
        "description": "This table captures vocal patterns of patients during sessions. It is linked to patients and may contain multiple recordings.",
        "rows": {
          "per": "patients",
          "via": "patient_id",
          "min": 1,
          "max": 5,
          "skew": "heavy-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each vocal pattern entry."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "Reference to the patient the vocal pattern belongs to."
          },
          "recording_url": {
            "kind": "url",
            "description": "Link to the vocal pattern recording."
          },
          "duration_seconds": {
            "kind": "int",
            "min": 1,
            "max": 300,
            "description": "Duration of the vocal recording in seconds.",
            "distribution": "long-tail"
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the vocal pattern was recorded.",
            "dayProfile": "waking"
          }
        }
      },
      "ai_interactions": {
        "description": "This table captures interactions between patients and AI chat systems, linked to patients. Each patient can have multiple interactions.",
        "rows": {
          "per": "patients",
          "via": "patient_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each AI interaction."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "Reference to the patient involved in the AI interaction."
          },
          "message": {
            "kind": "sentence",
            "min": 5,
            "max": 50,
            "description": "The content of the message exchanged during the interaction."
          },
          "response_time_seconds": {
            "kind": "int",
            "min": 1,
            "max": 60,
            "description": "Time taken by the AI to respond in seconds.",
            "distribution": "normal"
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the AI interaction occurred.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "nullable",
      "timestamped",
      "categorical",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "How many patients have consented to AI monitoring?",
      "What is the average number of crisis plans per patient?",
      "Which clinician specialties are most common among patients?",
      "What patterns can be observed in vocal recordings over time?",
      "How do AI interactions differ among patients with and without crisis plans?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Patients and Clinicians",
        "sql": "SELECT patients.full_name, clinicians.full_name FROM patients JOIN clinicians ON patients.id = clinicians.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count of Crisis Plans by Patient",
        "sql": "SELECT patient_id, COUNT(*) FROM crisis_plans GROUP BY patient_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Duration of Vocal Patterns",
        "sql": "SELECT AVG(duration_seconds) FROM vocal_patterns;",
        "dialect": "postgres"
      },
      {
        "title": "AI Interaction Response Times Over Time",
        "sql": "SELECT created_at, AVG(response_time_seconds) FROM ai_interactions GROUP BY created_at;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Mental Health Research",
        "body": "Researchers in psychology can utilize this dataset to study the effectiveness of AI interventions in mental health care. By analyzing patient interactions with AI and vocal patterns, they can derive insights into patient engagement and the emotional state of individuals."
      },
      {
        "title": "Training Mental Health Professionals",
        "body": "Mental health training programs can use this dataset to teach students about patient monitoring techniques and consent management. By working with real-world-like data, students can better understand the complexities involved in mental health treatment and patient relationships."
      },
      {
        "title": "Clinical Evaluations",
        "body": "Clinicians can leverage this dataset to evaluate their patient relationships and the efficacy of crisis plans. Analyzing consent rates and crisis plan implementation can help them identify areas for improvement in their practice."
      }
    ]
  },
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    "clinicians": 100,
    "patients": 250,
    "ai_interactions": 1022,
    "crisis_plans": 415,
    "vocal_patterns": 445
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