ingestion/ingestion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "26f65668",
   "metadata": {},
   "source": [
    "# LLM Powered Medical Case Sheet Ingestion\n",
    "## Outline\n",
    "1. Data Cleansing\n",
    "2. Prompt Definition\n",
    "3. Entity & Relationship Extraction\n",
    "4. Neo4j Cypher Generation\n",
    "5. Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61138c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install graphdatascience\n",
    "%pip install openai==0.28\n",
    "%pip install python-dotenv \n",
    "%pip install retry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2c97f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "from retry import retry\n",
    "import re\n",
    "from string import Template\n",
    "import json \n",
    "import ast\n",
    "import time\n",
    "import pandas as pd\n",
    "from graphdatascience import GraphDataScience\n",
    "import glob\n",
    "from timeit import default_timer as timer\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d70b41f",
   "metadata": {},
   "source": [
    "## Data Cleansing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89595dc",
   "metadata": {},
   "source": [
    "First, let's define a function that can help clean the input data. For the sake of simplicity, lets keep it simple. In the corpus, the data refers to some Figures like scan images. We dont have them and so will remove any such references."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af73c9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "  clean = \"\\n\".join([row for row in text.split(\"\\n\")])\n",
    "  clean = re.sub(r'\\(fig[^)]*\\)', '', clean, flags=re.IGNORECASE)\n",
    "  return clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5326d17d",
   "metadata": {},
   "source": [
    "Let's take this case sheet and extract entities and relations using LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09861c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "article_txt = \"\"\"The patient was a 34-yr-old man who presented with complaints of fever and a chronic cough.\n",
    "He was a smoker and had a history of pulmonary tuberculosis that had been treated and cured.\n",
    "A computed tomographic (CT) scan revealed multiple tiny nodules in both lungs.\n",
    "A thoracoscopic lung biopsy was taken from the right upper lobe.\n",
    "The microscopic examination revealed a typical LCH.\n",
    "The tumor cells had vesicular and grooved nuclei, and they formed small aggregations around the bronchioles (Fig.1).\n",
    "The tumor cells were strongly positive for S-100 protein, vimentin, CD68 and CD1a.\n",
    "There were infiltrations of lymphocytes and eosinophils around the tumor cells.\n",
    "With performing additional radiologic examinations, no other organs were thought to be involved.\n",
    "He quit smoking, but he received no other specific treatment.\n",
    "He was well for the following one year.\n",
    "After this, a follow-up CT scan was performed and it showed a 4 cm-sized mass in the left lower lobe, in addition to the multiple tiny nodules in both lungs (Fig.2).\n",
    "A needle biopsy specimen revealed the possibility of a sarcoma; therefore, a lobectomy was performed.\n",
    "Grossly, a 4 cm-sized poorly-circumscribed lobulated gray-white mass was found (Fig.3), and there were a few small satellite nodules around the main mass.\n",
    "Microscopically, the tumor cells were aggregated in large sheets and they showed an infiltrative growth.\n",
    "The cytologic features of some of the tumor cells were similar to those seen in a typical LCH.\n",
    "However, many tumor cells showed overtly malignant cytologic features such as pleomorphic/hyperchromatic nuclei and prominent nucleoli (Fig.4), and multinucleated tumor giant cells were also found.\n",
    "There were numerous mitotic figures ranging from 30 to 60 per 10 high power fields, and some of them were abnormal.\n",
    "A few foci of typical LCH remained around the main tumor mass.\n",
    "Immunohistochemically, the tumor cells were strongly positive for S-100 protein (Fig.5) and vimentin; they were also positive for CD68 (Dako N1577, Clone KPI), and focally positive for CD1a (Fig.6), and they were negative for cytokeratin, epithelial membrane antigen, CD3, CD20 and HMB45.\n",
    "The ultrastructural analysis failed to demonstrate any Birbeck granules in the cytoplasm of the tumor cells.\n",
    "Now, at five months after lobectomy, the patient is doing well with no significant change in the radiologic findings.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3236df",
   "metadata": {},
   "source": [
    "## Prompt Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c73e3d",
   "metadata": {},
   "source": [
    "**⚠️** You need to duplicate `config.env.example` file in the left and rename as `config.env`. Edit the values in this file and provide the values for API keys and Neo4j credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f94b81b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv('config.env', override=True)\n",
    "\n",
    "openai.api_type = \"azure\" #I use Azure OpenAI. You might need to comment if you use the non-Azure instance\n",
    "openai.api_base = os.getenv('OPENAI_API_BASE')\n",
    "openai.api_version = os.getenv('OPENAI_API_VERSION')\n",
    "openai.api_key = os.getenv('OPENAI_API_KEY')\n",
    "model_name = os.getenv('MODEL_NAME')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff224199",
   "metadata": {},
   "source": [
    "This is a helper function to talk to the LLM with our prompt and text input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d8592a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPT-4 Prompt to complete\n",
    "@retry(tries=2, delay=5)\n",
    "def process_gpt(system, \n",
    "                prompt):\n",
    "\n",
    "    completion = openai.ChatCompletion.create(\n",
    "        #engine=\"gpt-4-32k\",\n",
    "        #model=\"gpt-4-32k\",\n",
    "        engine=model_name,\n",
    "        model=model_name,\n",
    "        max_tokens=25000,\n",
    "        # Try to be as deterministic as possible\n",
    "        temperature=0,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system},\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ]\n",
    "    )\n",
    "    nlp_results = completion.choices[0].message.content\n",
    "    return nlp_results"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "a52639cd",
   "metadata": {},
   "source": [
    "This is a simple prompt to start with. If the processing is very complex, you can also chain the prompts as and when required. I am going to use a single prompt here that helps me to extract the text strictly as per the Entities and Relationships defined. This is a simplification. In the real scenario, especially with medical records, you have to leverage on Domain experts to define the Ontology systematically and capture the important information. You might also be fine-tuning the model as and when required.\n",
    "\n",
    "Also, instead of one single large model, you can also consider chaining a number of smaller ones as per your needs.\n",
    "\n",
    "We are going with this Graph Schema for our Case Sheet:\n",
    "![schema.png](attachment:d16d5097-785c-4343-8a48-289517fca944.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90379cd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt1=\"\"\"From the Case sheet for a patient below, extract the following Entities & relationships described in the mentioned format \n",
    "0. ALWAYS FINISH THE OUTPUT. Never send partial responses\n",
    "1. First, look for these Entity types in the text and generate as comma-separated format similar to entity type.\n",
    "   `id` property of each entity must be alphanumeric and must be unique among the entities. You will be referring this property to define the relationship between entities. Do not create new entity types that aren't mentioned below. Document must be summarized and stored inside Case entity under `summary` property. You will have to generate as many entities as needed as per the types below:\n",
    "    Entity Types:\n",
    "    label:'Case',id:string,summary:string //Case\n",
    "    label:'Person',id:string,age:string,location:string,gender:string //Patient mentioned in the case\n",
    "    label:'Symptom',id:string,description:string //Symptom Entity; `id` property is the name of the symptom, in lowercase & camel-case & should always start with an alphabet\n",
    "    label:'Disease',id:string,name:string //Disease diagnosed now or previously as per the Case sheet; `id` property is the name of the disease, in lowercase & camel-case & should always start with an alphabet\n",
    "    label:'BodySystem',id:string,name:string //Body Part affected. Eg: Chest, Lungs; id property is the name of the part, in lowercase & camel-case & should always start with an alphabet\n",
    "    label:'Diagnosis',id:string,name:string,description:string,when:string //Diagnostic procedure conducted; `id` property is the summary of the Diagnosis, in lowercase & camel-case & should always start with an alphabet\n",
    "    label:'Biological',id:string,name:string,description:string //Results identified from Diagnosis; `id` property is the summary of the Biological, in lowercase & camel-case & should always start with an alphabet\n",
    "    \n",
    "3. Next generate each relationships as triples of head, relationship and tail. To refer the head and tail entity, use their respective `id` property. Relationship property should be mentioned within brackets as comma-separated. They should follow these relationship types below. You will have to generate as many relationships as needed as defined below:\n",
    "    Relationship types:\n",
    "    case|FOR|person\n",
    "    person|HAS_SYMPTOM{when:string,frequency:string,span:string}|symptom //the properties inside HAS_SYMPTOM gets populated from the Case sheet\n",
    "    person|HAS_DISEASE{when:string}|disease //the properties inside HAS_DISEASE gets populated from the Case sheet\n",
    "    symptom|SEEN_ON|chest\n",
    "    disease|AFFECTS|heart\n",
    "    person|HAS_DIAGNOSIS|diagnosis\n",
    "    diagnosis|SHOWED|biological\n",
    "\n",
    "The output should look like :\n",
    "{\n",
    "    \"entities\": [{\"label\":\"Case\",\"id\":string,\"summary\":string}],\n",
    "    \"relationships\": [\"disease|AFFECTS|heart\"]\n",
    "}\n",
    "\n",
    "Case Sheet:\n",
    "$ctext\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a20e1c",
   "metadata": {},
   "source": [
    "Let's run our completion task with our LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98cbafb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "def run_completion(prompt, results, ctext):\n",
    "    try:\n",
    "      system = \"You are a helpful Medical Case Sheet expert who extracts relevant information and store them on a Neo4j Knowledge Graph\"\n",
    "      pr = Template(prompt).substitute(ctext=ctext)\n",
    "      res = process_gpt(system, pr)\n",
    "      results.append(json.loads(res.replace(\"\\'\", \"'\")))\n",
    "      return results\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "prompts = [prompt1]\n",
    "results = []\n",
    "for p in prompts:\n",
    "  results = run_completion(p, results, clean_text(article_txt))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc40d897",
   "metadata": {},
   "source": [
    "## Neo4j Cypher Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2a861f",
   "metadata": {},
   "source": [
    "The entities & relationships we got from the LLM have to be transformed to Cypher so we can ingest into Neo4j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b7fe8c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre-processing results for uploading into Neo4j - helper function:\n",
    "def get_prop_str(prop_dict, _id):\n",
    "    s = []\n",
    "    for key, val in prop_dict.items():\n",
    "      if key != 'label' and key != 'id':\n",
    "         s.append(_id+\".\"+key+' = \"'+str(val).replace('\\\"', '\"').replace('\"', '\\\"')+'\"') \n",
    "    return ' ON CREATE SET ' + ','.join(s)\n",
    "\n",
    "def get_cypher_compliant_var(_id):\n",
    "    return \"_\"+ re.sub(r'[\\W_]', '', _id)\n",
    "\n",
    "def generate_cypher(in_json):\n",
    "    e_map = {}\n",
    "    e_stmt = []\n",
    "    r_stmt = []\n",
    "    e_stmt_tpl = Template(\"($id:$label{id:'$key'})\")\n",
    "    r_stmt_tpl = Template(\"\"\"\n",
    "      MATCH $src\n",
    "      MATCH $tgt\n",
    "      MERGE ($src_id)-[:$rel]->($tgt_id)\n",
    "    \"\"\")\n",
    "    for obj in in_json:\n",
    "      for j in obj['entities']:\n",
    "          props = ''\n",
    "          label = j['label']\n",
    "          id = j['id']\n",
    "          if label == 'Case':\n",
    "                id = 'c'+str(time.time_ns())\n",
    "          elif label == 'Person':\n",
    "                id = 'p'+str(time.time_ns())\n",
    "          varname = get_cypher_compliant_var(j['id'])\n",
    "          stmt = e_stmt_tpl.substitute(id=varname, label=label, key=id)\n",
    "          e_map[varname] = stmt\n",
    "          e_stmt.append('MERGE '+ stmt + get_prop_str(j, varname))\n",
    "\n",
    "      for st in obj['relationships']:\n",
    "          rels = st.split(\"|\")\n",
    "          src_id = get_cypher_compliant_var(rels[0].strip())\n",
    "          rel = rels[1].strip()\n",
    "          tgt_id = get_cypher_compliant_var(rels[2].strip())\n",
    "          stmt = r_stmt_tpl.substitute(\n",
    "              src_id=src_id, tgt_id=tgt_id, src=e_map[src_id], tgt=e_map[tgt_id], rel=rel)\n",
    "          \n",
    "          r_stmt.append(stmt)\n",
    "\n",
    "    return e_stmt, r_stmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34f74bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ent_cyp, rel_cyp = generate_cypher(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13588341",
   "metadata": {},
   "source": [
    "## Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beacbd25",
   "metadata": {},
   "outputs": [],
   "source": [
    "connectionUrl = os.getenv('NEO4J_CONN_URL')\n",
    "username = os.getenv('NEO4J_USER')\n",
    "password = os.getenv('NEO4J_PASSWORD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfe9acfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "gds = GraphDataScience(connectionUrl, auth=(username, password))\n",
    "gds.version()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd579588",
   "metadata": {},
   "source": [
    "Before loading the data, create constraints as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d546fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "gds.run_cypher('CREATE CONSTRAINT unique_case_id IF NOT EXISTS FOR (n:Case) REQUIRE n.id IS UNIQUE')\n",
    "gds.run_cypher('CREATE CONSTRAINT unique_person_id IF NOT EXISTS FOR (n:Person) REQUIRE (n.id) IS UNIQUE')\n",
    "gds.run_cypher('CREATE CONSTRAINT unique_symptom_id IF NOT EXISTS FOR (n:Symptom) REQUIRE (n.id) IS UNIQUE')\n",
    "gds.run_cypher('CREATE CONSTRAINT unique_disease_id IF NOT EXISTS FOR (n:Disease) REQUIRE n.id IS UNIQUE')\n",
    "gds.run_cypher('CREATE CONSTRAINT unique_bodysys_id IF NOT EXISTS FOR (n:BodySystem) REQUIRE n.id IS UNIQUE')\n",
    "gds.run_cypher('CREATE CONSTRAINT unique_diag_id IF NOT EXISTS FOR (n:Diagnosis) REQUIRE n.id IS UNIQUE')\n",
    "gds.run_cypher('CREATE CONSTRAINT unique_biological_id IF NOT EXISTS FOR (n:Biological) REQUIRE n.id IS UNIQUE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1435e1c9",
   "metadata": {},
   "source": [
    "Ingest the entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df1f033e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "for e in ent_cyp:\n",
    "    gds.run_cypher(e)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52ce15b",
   "metadata": {},
   "source": [
    "Ingest relationships now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "684c1282",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "for r in rel_cyp:\n",
    "    gds.run_cypher(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ce7424",
   "metadata": {},
   "source": [
    "This is a helper function to ingest all case sheets inside the `data/` directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a15e3072",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_pipeline(count=191):\n",
    "    txt_files = glob.glob(\"data/*.txt\")[0:count]\n",
    "    print(f\"Running pipeline for {len(txt_files)} files\")\n",
    "    failed_files = process_pipeline(txt_files)\n",
    "    print(failed_files)\n",
    "    return failed_files\n",
    "\n",
    "def process_pipeline(files):\n",
    "    failed_files = []\n",
    "    for f in files:\n",
    "        try:\n",
    "            with open(f, 'r') as file:\n",
    "                print(f\"  {f}: Reading File...\")\n",
    "                data = file.read().rstrip()\n",
    "                text = clean_text(data)\n",
    "                print(f\"    {f}: Extracting E & R\")\n",
    "                results = extract_entities_relationships(f, text)\n",
    "                print(f\"    {f}: Generating Cypher\")\n",
    "                ent_cyp, rel_cyp = generate_cypher(results)\n",
    "                print(f\"    {f}: Ingesting Entities\")\n",
    "                for e in ent_cyp:\n",
    "                    gds.run_cypher(e)\n",
    "                print(f\"    {f}: Ingesting Relationships\")\n",
    "                for r in rel_cyp:\n",
    "                    gds.run_cypher(r)\n",
    "                print(f\"    {f}: Processing DONE\")\n",
    "        except Exception as e:\n",
    "            print(f\"    {f}: Processing Failed with exception {e}\")\n",
    "            failed_files.append(f)\n",
    "    return failed_files\n",
    "            \n",
    "def extract_entities_relationships(f, text):\n",
    "    start = timer()\n",
    "    system = \"You are a helpful Medical Case Sheet expert who extracts relevant information and store them on a Neo4j Knowledge Graph\"\n",
    "    prompts = [prompt1]\n",
    "    all_cypher = \"\"\n",
    "    results = []\n",
    "    for p in prompts:\n",
    "      p = Template(p).substitute(ctext=text)\n",
    "      res = process_gpt(system, p)\n",
    "      results.append(json.loads(res))\n",
    "    end = timer()\n",
    "    elapsed = (end-start)\n",
    "    print(f\"    {f}: E & R took {elapsed}secs\")\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c33abb7",
   "metadata": {},
   "source": [
    "Now let's run the code on a larger set of files. This will take a while (1.5 - 3 hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f1ebbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "failed_files = run_pipeline(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a559333e",
   "metadata": {},
   "source": [
    "If processing failed for some files due to API Rate limit or some other error, you can retry as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39031486",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "failed_files = process_pipeline(failed_files)\n",
    "failed_files"
   ]
  }
 ],
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