Example_medspacy_dutch.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "import medspacy\n",
    "import quickumls.spacy_component\n",
    "from quickumls import constants\n",
    "from medspacy.visualization import visualize_ent, visualize_dep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<spacy.pipeline.sentencizer.Sentencizer at 0x1e8cf7054c0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Create a new spacy pipeline\n",
    "nlp = spacy.blank(\"nl\")\n",
    "nlp.add_pipe('sentencizer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sentencizer', 'medspacy_quickumls']\n"
     ]
    }
   ],
   "source": [
    "#### Add the concept extraction module\n",
    "\n",
    "# set the location of the concept database\n",
    "quickumls_file_path = \"./QuickUMLS_resources/name_of_database_folder/\"\n",
    "\n",
    "# set the semtypes you want to find (T000 to T999)\n",
    "semtypes=constants.ACCEPTED_SEMTYPES.add(tuple([\"T\"+str(x).zfill(3) for x in range(0,1000)]))\n",
    "\n",
    "# setup and set configuration options\n",
    "nlp.add_pipe(\"medspacy_quickumls\", config={\"threshold\": 0.7,\n",
    "                                            \"result_type\": \"ents\",\n",
    "                                            \"accepted_semtypes\":semtypes,\n",
    "                                            \"best_match\": True, \n",
    "                                            \"window\": 10,\n",
    "                                            \"similarity_name\":\"jaccard\",\n",
    "                                            \"min_match_length\": 3,\n",
    "                                            \"overlapping_criteria\":\"score\",\n",
    "                                            \"quickumls_fp\": quickumls_file_path})\n",
    "\n",
    "print(nlp.pipe_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sentencizer', 'medspacy_quickumls', 'medspacy_context']\n"
     ]
    }
   ],
   "source": [
    "#### Add the context extraction module\n",
    "\n",
    "# set the location of the rules\"\n",
    "rules_path=\"./Context_resources/context_rules_Dutch.json\"\n",
    "\n",
    "nlp.add_pipe(\"medspacy_context\", config={\"rules\":rules_path})\n",
    "\n",
    "print(nlp.pipe_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-03-12T12:36:07.337257] 4 extracted from 22 tokens\n"
     ]
    }
   ],
   "source": [
    "#### test the pipeline\n",
    "concept_text = 'Patient heeft een jaar geleden zijn been gebroken maar heeft nu nog steeds veel pijn, maar geen moeite met lopen.'\n",
    "doc = nlp(concept_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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    "visualize_ent(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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