0.0.9 reference text term frequency

app.py

@@ -73,10 +73,7 @@ f'''
 f'''
 #### How can <what most are doing> help with <what few are doing>?
 ''')
-
-
-
-
+    
 from llama_index import StorageContext
 from llama_index import ServiceContext
 from llama_index import load_index_from_storage 
@@ -325,28 +322,37 @@ if(st.session_state.question):
             from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
             import matplotlib.pyplot as plt
             from PIL import Image
-            wc_all, wq_question = st.columns([2, 2])
-            wordcloud = WordCloud(max_font_size=50, max_words=1000, background_color="white").generate(answer_str)
+            wc_all, wc_question, wc_reference  = st.columns([3, 3, 3])
+            wordcloud = WordCloud(max_font_size=50, max_words=1000, background_color="white")
             with wc_all:
-                #st.write('''### Corpus''')
                 image = Image.open('docs/images/all_papers_wordcloud.png')
                 st.image(image)
-                st.caption('''###### Corpus word frequecy.''')
-            with wq_question:
-                #st.write('''### Question''')
-                st.image(wordcloud.to_array())
-                st.caption('''###### Query word frequecy.''')
+                st.caption('''###### Corpus term frequecy.''')
+            with wc_question:
+                wordcloud_q = wordcloud.generate(answer_str)
+                st.image(wordcloud_q.to_array())
+                st.caption('''###### Answer term frequecy.''')
+            with wc_reference:
+                all_reference_texts = ''
+                for nodewithscore in answer.source_nodes:  
+                    node = nodewithscore.node
+                    from llama_index.schema import NodeRelationship
+                    #if NodeRelationship.SOURCE in node.relationships:
+                    all_reference_texts = all_reference_texts + '\n' + node.text
+                wordcloud_r = wordcloud.generate(all_reference_texts)
+                st.image(wordcloud_r.to_array())
+                st.caption('''###### Reference plus graph term frequecy.''')
+                
         with explainable:
             #st.write(answer.source_nodes)
             from pyvis.network import Network
             graph = Network(height="450px", width="100%")
             sources_table = []
+            #all_reference_texts = ''
             for nodewithscore in answer.source_nodes:  
                 node = nodewithscore.node
                 from llama_index.schema import NodeRelationship
                 if NodeRelationship.SOURCE in node.relationships:
-                    #st.write(node.relationships[NodeRelationship.SOURCE].node_id)
-                    #st.write(node.text)
                     node_id = node.relationships[NodeRelationship.SOURCE].node_id
                     node_id = node_id.split('/')[-1]
                     title = node_id.split('.')[2].replace('_', ' ')
@@ -354,9 +360,9 @@ if(st.session_state.question):
                     link = f'https://arxiv.org/abs/{link}'
                     href = f'<a  target="_blank" href="{link}">{title}</a>'
                     sources_table.extend([[href, node.text]])
+                    #all_reference_texts = all_reference_texts + '\n' + node.text
                 else:
-                    #st.write(node.metadata['kg_rel_map'])
-                    #st.write(node.text) TODO
+                    #st.write(node.text) TODO second level relationships
                     rel_map = node.metadata['kg_rel_map']
                     for concept in rel_map.keys():
                         #st.write(concept)
@@ -365,13 +371,14 @@ if(st.session_state.question):
                         for rel in rels:
                             graph.add_node(rel[1],  rel[1], title=rel[1])
                             graph.add_edge(concept,  rel[1], title=rel[0])
+            # --- display the query terms graph
             st.session_state.graph_name = 'graph.html'
             graph.save_graph(st.session_state.graph_name)            
             import streamlit.components.v1 as components
             graphHtml = open(st.session_state.graph_name, 'r', encoding='utf-8')
             source_code = graphHtml.read() 
-            #print(source_code)
             components.html(source_code, height = 500)
+            # --- display the reference texts table
             import pandas as pd
             df = pd.DataFrame(sources_table)
             df.columns = ['paper', 'relevant text']
@@ -381,8 +388,8 @@ if(st.session_state.question):
                             }
                             </style> """, unsafe_allow_html=True)
             st.write(df.to_html(escape=False), unsafe_allow_html=True)
-            #st.table(df)
-            #st.write(answer.source_nodes[0].node)
+            # reference text wordcloud
+            #st.session_state.reference_wcloud = all_reference_texts
 
     except Exception as e:
         #print(f'{type(e)}, {e}')

graph.html

@@ -88,8 +88,8 @@
                   
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"color": "#97c2fc", "id": "clinicians", "label": "clinicians", "shape": "dot", "title": "clinicians"}, {"color": "#97c2fc", "id": "remote", "label": "remote", "shape": "dot", "title": "remote"}, {"color": "#97c2fc", "id": "TKM", "label": "TKM", "shape": "dot", "title": "TKM"}, {"color": "#97c2fc", "id": "decision-making", "label": "decision-making", "shape": "dot", "title": "decision-making"}, {"color": "#97c2fc", "id": "semantic web", "label": "semantic web", "shape": "dot", "title": "semantic web"}, {"color": "#97c2fc", "id": "ontologies", "label": "ontologies", "shape": "dot", "title": "ontologies"}, {"color": "#97c2fc", "id": "data", "label": "data", "shape": "dot", "title": "data"}, {"color": "#97c2fc", "id": "deep learning models", "label": "deep learning models", "shape": "dot", "title": "deep learning models"}, {"color": "#97c2fc", "id": "patient outcomes prediction", "label": "patient outcomes prediction", "shape": "dot", "title": "patient outcomes prediction"}, {"color": "#97c2fc", "id": "post-discharge readmissions", "label": "post-discharge readmissions", "shape": "dot", "title": "post-discharge readmissions"}, {"color": "#97c2fc", "id": "information", "label": "information", "shape": "dot", "title": "information"}, {"color": "#97c2fc", "id": "tabular and unstructured", "label": "tabular and unstructured", "shape": "dot", "title": "tabular and unstructured"}, {"color": "#97c2fc", "id": "enrich", "label": "enrich", "shape": "dot", "title": "enrich"}, {"color": "#97c2fc", "id": "BERT base tokenizer", "label": "BERT base tokenizer", "shape": "dot", "title": "BERT base tokenizer"}, {"color": "#97c2fc", "id": "BERT variant models", "label": "BERT variant models", "shape": "dot", "title": "BERT variant models"}, {"color": "#97c2fc", "id": "BERT variants in medical domain", "label": "BERT variants in medical domain", "shape": "dot", "title": "BERT variants in medical domain"}, {"color": "#97c2fc", "id": "approach", "label": "approach", "shape": "dot", "title": "approach"}, {"color": "#97c2fc", "id": "biomedical", "label": "biomedical", "shape": "dot", "title": "biomedical"}, {"color": "#97c2fc", "id": "multimodal", "label": "multimodal", "shape": "dot", "title": "multimodal"}, {"color": "#97c2fc", "id": "instruction", "label": "instruction", "shape": "dot", "title": "instruction"}, {"color": "#97c2fc", "id": "responses", "label": "responses", "shape": "dot", "title": "responses"}, {"color": "#97c2fc", "id": "visual", "label": "visual", "shape": "dot", "title": "visual"}, {"color": "#97c2fc", "id": "chat", "label": "chat", "shape": "dot", "title": "chat"}, {"color": "#97c2fc", "id": "medical", "label": "medical", "shape": "dot", "title": "medical"}, {"color": "#97c2fc", "id": "alignment", "label": "alignment", "shape": "dot", "title": "alignment"}, {"color": "#97c2fc", "id": "tuning", "label": "tuning", "shape": "dot", "title": "tuning"}, {"color": "#97c2fc", "id": "epochs", "label": "epochs", "shape": "dot", "title": "epochs"}, {"color": "#97c2fc", "id": "samples", "label": "samples", "shape": "dot", "title": "samples"}, {"color": "#97c2fc", "id": "hours", "label": "hours", "shape": "dot", "title": "hours"}, {"color": "#97c2fc", "id": "downstrea", "label": "downstrea", "shape": "dot", "title": "downstrea"}, {"color": "#97c2fc", "id": "in HKGs", "label": "in HKGs", "shape": "dot", "title": "in HKGs"}, {"color": "#97c2fc", "id": "HKGs", "label": "HKGs", "shape": "dot", "title": "HKGs"}, {"color": "#97c2fc", "id": "medical concepts", "label": "medical concepts", "shape": "dot", "title": "medical concepts"}, {"color": "#97c2fc", "id": "medical literature", "label": "medical literature", "shape": "dot", "title": "medical literature"}, {"color": "#97c2fc", "id": "clinical trials", "label": "clinical trials", "shape": "dot", "title": "clinical trials"}, {"color": "#97c2fc", "id": "patientgenerated data", "label": "patientgenerated data", "shape": "dot", "title": "patientgenerated data"}, {"color": "#97c2fc", "id": "medical entities", "label": "medical entities", "shape": "dot", "title": "medical entities"}, {"color": "#97c2fc", "id": "relationships", "label": "relationships", "shape": "dot", "title": "relationships"}, {"color": "#97c2fc", "id": "to structured format", "label": "to structured format", "shape": "dot", "title": "to structured format"}, {"color": "#97c2fc", "id": "entities and relationships", "label": "entities and relationships", "shape": "dot", "title": "entities and relationships"}, {"color": "#97c2fc", "id": "to chosen ontologies", "label": "to chosen ontologies", "shape": "dot", "title": "to chosen ontologies"}, {"color": "#97c2fc", "id": "PubMed", "label": "PubMed", "shape": "dot", "title": "PubMed"}, {"color": "#97c2fc", "id": "navigational queries", "label": "navigational queries", "shape": "dot", "title": "navigational queries"}, {"color": "#97c2fc", "id": "Field Sensor", "label": "Field Sensor", "shape": "dot", "title": "Field Sensor"}, {"color": "#97c2fc", "id": "removed articles", "label": "removed articles", "shape": "dot", "title": "removed articles"}, {"color": "#97c2fc", "id": "articles without titles or abstracts", "label": "articles without titles or abstracts", "shape": "dot", "title": "articles without titles or abstracts"}, {"color": "#97c2fc", "id": "predictions", "label": "predictions", "shape": "dot", "title": "predictions"}, {"color": "#97c2fc", "id": "classification", "label": "classification", "shape": "dot", "title": "classification"}, {"color": "#97c2fc", "id": "framing", "label": "framing", "shape": "dot", "title": "framing"}, {"color": "#97c2fc", "id": "records", "label": "records", "shape": "dot", "title": "records"}, {"color": "#97c2fc", "id": "features", "label": "features", "shape": "dot", "title": "features"}, {"color": "#97c2fc", "id": "utilizing", "label": "utilizing", "shape": "dot", "title": "utilizing"}, {"color": "#97c2fc", "id": "new paradigm", "label": "new paradigm", "shape": "dot", "title": "new paradigm"}, {"color": "#97c2fc", "id": "NLEKMD", "label": "NLEKMD", "shape": "dot", "title": "NLEKMD"}, {"color": "#97c2fc", "id": "TKM clinicians", "label": "TKM clinicians", "shape": "dot", "title": "TKM clinicians"}]);
-                  edges = new vis.DataSet([{"from": "clinicians", "title": "local", "to": "remote"}, {"from": "TKM", "title": "is", "to": "decision-making"}, {"from": "TKM", "title": "is", "to": "semantic web"}, {"from": "TKM", "title": "is", "to": "ontologies"}, {"from": "TKM", "title": "implicit", "to": "data"}, {"from": "TKM", "title": "limitation", "to": "deep learning models"}, {"from": "TKM", "title": "shortage", "to": "patient outcomes prediction"}, {"from": "TKM", "title": "imbalance", "to": "post-discharge readmissions"}, {"from": "TKM", "title": "contains", "to": "information"}, {"from": "TKM", "title": "mix", "to": "tabular and unstructured"}, {"from": "TKM", "title": "and", "to": "enrich"}, {"from": "TKM", "title": "tokenized using", "to": "BERT base tokenizer"}, {"from": "TKM", "title": "IOB tagging scheme", "to": "BERT variant models"}, {"from": "TKM", "title": "CRF layer as tag encoding", "to": "BERT variants in medical domain"}, {"from": "TKM", "title": "augmentation", "to": "approach"}, {"from": "TKM", "title": "statistics", "to": "biomedical"}, {"from": "TKM", "title": "statistics", "to": "multimodal"}, {"from": "TKM", "title": "statistics", "to": "instruction"}, {"from": "TKM", "title": "statistics", "to": "responses"}, {"from": "TKM", "title": "statistics", "to": "visual"}, {"from": "TKM", "title": "statistics", "to": "chat"}, {"from": "TKM", "title": "statistics", "to": "medical"}, {"from": "TKM", "title": "statistics", "to": "alignment"}, {"from": "TKM", "title": "statistics", "to": "tuning"}, {"from": "TKM", "title": "statistics", "to": "epochs"}, {"from": "TKM", "title": "statistics", "to": "samples"}, {"from": "TKM", "title": "statistics", "to": "hours"}, {"from": "TKM", "title": "statistics", "to": "downstrea"}, {"from": "TKM", "title": "heterogeneity", "to": "in HKGs"}, {"from": "TKM", "title": "in", "to": "HKGs"}, {"from": "TKM", "title": "in", "to": "medical concepts"}, {"from": "TKM", "title": "gather", "to": "medical literature"}, {"from": "TKM", "title": "gather", "to": "clinical trials"}, {"from": "TKM", "title": "gather", "to": "patientgenerated data"}, {"from": "TKM", "title": "extract", "to": "medical entities"}, {"from": "TKM", "title": "extract", "to": "relationships"}, {"from": "TKM", "title": "transform", "to": "to structured format"}, {"from": "TKM", "title": "map", "to": "entities and relationships"}, {"from": "TKM", "title": "map", "to": "to chosen ontologies"}, {"from": "TKM", "title": "collection", "to": "PubMed"}, {"from": "TKM", "title": "collection", "to": "navigational queries"}, {"from": "TKM", "title": "collection", "to": "Field Sensor"}, {"from": "TKM", "title": "collection", "to": "removed articles"}, {"from": "TKM", "title": "collection", "to": "articles without titles or abstracts"}, {"from": "TKM", "title": "patterns", "to": "predictions"}, {"from": "TKM", "title": "algorithms", "to": "classification"}, {"from": "TKM", "title": "problem", "to": "framing"}, {"from": "TKM", "title": "medical", "to": "records"}, {"from": "TKM", "title": "key", "to": "features"}, {"from": "TKM", "title": "models", "to": "utilizing"}, {"from": "TKM", "title": "could benefit from", "to": "new paradigm"}, {"from": "TKM", "title": "near-passing performance", "to": "NLEKMD"}]);
+                  nodes = new vis.DataSet([{"color": "#97c2fc", "id": "TKM", "label": "TKM", "shape": "dot", "title": "TKM"}, {"color": "#97c2fc", "id": "decision-making", "label": "decision-making", "shape": "dot", "title": "decision-making"}, {"color": "#97c2fc", "id": "semantic web", "label": "semantic web", "shape": "dot", "title": "semantic web"}, {"color": "#97c2fc", "id": "ontologies", "label": "ontologies", "shape": "dot", "title": "ontologies"}, {"color": "#97c2fc", "id": "data", "label": "data", "shape": "dot", "title": "data"}, {"color": "#97c2fc", "id": "deep learning models", "label": "deep learning models", "shape": "dot", "title": "deep learning models"}, {"color": "#97c2fc", "id": "patient outcomes prediction", "label": "patient outcomes prediction", "shape": "dot", "title": "patient outcomes prediction"}, {"color": "#97c2fc", "id": "post-discharge readmissions", "label": "post-discharge readmissions", "shape": "dot", "title": "post-discharge readmissions"}, {"color": "#97c2fc", "id": "information", "label": "information", "shape": "dot", "title": "information"}, {"color": "#97c2fc", "id": "tabular and unstructured", "label": "tabular and unstructured", "shape": "dot", "title": "tabular and unstructured"}, {"color": "#97c2fc", "id": "enrich", "label": "enrich", "shape": "dot", "title": "enrich"}, {"color": "#97c2fc", "id": "BERT base tokenizer", "label": "BERT base tokenizer", "shape": "dot", "title": "BERT base tokenizer"}, {"color": "#97c2fc", "id": "BERT variant models", "label": "BERT variant models", "shape": "dot", "title": "BERT variant models"}, {"color": "#97c2fc", "id": "BERT variants in medical domain", "label": "BERT variants in medical domain", "shape": "dot", "title": "BERT variants in medical domain"}, {"color": "#97c2fc", "id": "approach", "label": "approach", "shape": "dot", "title": "approach"}, {"color": "#97c2fc", "id": "biomedical", "label": "biomedical", "shape": "dot", "title": "biomedical"}, {"color": "#97c2fc", "id": "multimodal", "label": "multimodal", "shape": "dot", "title": "multimodal"}, {"color": "#97c2fc", "id": "instruction", "label": "instruction", "shape": "dot", "title": "instruction"}, {"color": "#97c2fc", "id": "responses", "label": "responses", "shape": "dot", "title": "responses"}, {"color": "#97c2fc", "id": "visual", "label": "visual", "shape": "dot", "title": "visual"}, {"color": "#97c2fc", "id": "chat", "label": "chat", "shape": "dot", "title": "chat"}, {"color": "#97c2fc", "id": "medical", "label": "medical", "shape": "dot", "title": "medical"}, {"color": "#97c2fc", "id": "alignment", "label": "alignment", "shape": "dot", "title": "alignment"}, {"color": "#97c2fc", "id": "tuning", "label": "tuning", "shape": "dot", "title": "tuning"}, {"color": "#97c2fc", "id": "epochs", "label": "epochs", "shape": "dot", "title": "epochs"}, {"color": "#97c2fc", "id": "samples", "label": "samples", "shape": "dot", "title": "samples"}, {"color": "#97c2fc", "id": "hours", "label": "hours", "shape": "dot", "title": "hours"}, {"color": "#97c2fc", "id": "downstrea", "label": "downstrea", "shape": "dot", "title": "downstrea"}, {"color": "#97c2fc", "id": "in HKGs", "label": "in HKGs", "shape": "dot", "title": "in HKGs"}, {"color": "#97c2fc", "id": "HKGs", "label": "HKGs", "shape": "dot", "title": "HKGs"}, {"color": "#97c2fc", "id": "medical concepts", "label": "medical concepts", "shape": "dot", "title": "medical concepts"}, {"color": "#97c2fc", "id": "medical literature", "label": "medical literature", "shape": "dot", "title": "medical literature"}, {"color": "#97c2fc", "id": "clinical trials", "label": "clinical trials", "shape": "dot", "title": "clinical trials"}, {"color": "#97c2fc", "id": "patientgenerated data", "label": "patientgenerated data", "shape": "dot", "title": "patientgenerated data"}, {"color": "#97c2fc", "id": "medical entities", "label": "medical entities", "shape": "dot", "title": "medical entities"}, {"color": "#97c2fc", "id": "relationships", "label": "relationships", "shape": "dot", "title": "relationships"}, {"color": "#97c2fc", "id": "to structured format", "label": "to structured format", "shape": "dot", "title": "to structured format"}, {"color": "#97c2fc", "id": "entities and relationships", "label": "entities and relationships", "shape": "dot", "title": "entities and relationships"}, {"color": "#97c2fc", "id": "to chosen ontologies", "label": "to chosen ontologies", "shape": "dot", "title": "to chosen ontologies"}, {"color": "#97c2fc", "id": "PubMed", "label": "PubMed", "shape": "dot", "title": "PubMed"}, {"color": "#97c2fc", "id": "navigational queries", "label": "navigational queries", "shape": "dot", "title": "navigational queries"}, {"color": "#97c2fc", "id": "Field Sensor", "label": "Field Sensor", "shape": "dot", "title": "Field Sensor"}, {"color": "#97c2fc", "id": "removed articles", "label": "removed articles", "shape": "dot", "title": "removed articles"}, {"color": "#97c2fc", "id": "articles without titles or abstracts", "label": "articles without titles or abstracts", "shape": "dot", "title": "articles without titles or abstracts"}, {"color": "#97c2fc", "id": "predictions", "label": "predictions", "shape": "dot", "title": "predictions"}, {"color": "#97c2fc", "id": "classification", "label": "classification", "shape": "dot", "title": "classification"}, {"color": "#97c2fc", "id": "framing", "label": "framing", "shape": "dot", "title": "framing"}, {"color": "#97c2fc", "id": "records", "label": "records", "shape": "dot", "title": "records"}, {"color": "#97c2fc", "id": "features", "label": "features", "shape": "dot", "title": "features"}, {"color": "#97c2fc", "id": "utilizing", "label": "utilizing", "shape": "dot", "title": "utilizing"}, {"color": "#97c2fc", "id": "new paradigm", "label": "new paradigm", "shape": "dot", "title": "new paradigm"}, {"color": "#97c2fc", "id": "NLEKMD", "label": "NLEKMD", "shape": "dot", "title": "NLEKMD"}, {"color": "#97c2fc", "id": "making", "label": "making", "shape": "dot", "title": "making"}, {"color": "#97c2fc", "id": "difficult", "label": "difficult", "shape": "dot", "title": "difficult"}, {"color": "#97c2fc", "id": "widely used", "label": "widely used", "shape": "dot", "title": "widely used"}, {"color": "#97c2fc", "id": "decision", "label": "decision", "shape": "dot", "title": "decision"}, {"color": "#97c2fc", "id": "clinicians", "label": "clinicians", "shape": "dot", "title": "clinicians"}, {"color": "#97c2fc", "id": "remote", "label": "remote", "shape": "dot", "title": "remote"}]);
+                  edges = new vis.DataSet([{"from": "TKM", "title": "is", "to": "decision-making"}, {"from": "TKM", "title": "is", "to": "semantic web"}, {"from": "TKM", "title": "is", "to": "ontologies"}, {"from": "TKM", "title": "implicit", "to": "data"}, {"from": "TKM", "title": "limitation", "to": "deep learning models"}, {"from": "TKM", "title": "shortage", "to": "patient outcomes prediction"}, {"from": "TKM", "title": "imbalance", "to": "post-discharge readmissions"}, {"from": "TKM", "title": "contains", "to": "information"}, {"from": "TKM", "title": "mix", "to": "tabular and unstructured"}, {"from": "TKM", "title": "and", "to": "enrich"}, {"from": "TKM", "title": "tokenized using", "to": "BERT base tokenizer"}, {"from": "TKM", "title": "IOB tagging scheme", "to": "BERT variant models"}, {"from": "TKM", "title": "CRF layer as tag encoding", "to": "BERT variants in medical domain"}, {"from": "TKM", "title": "augmentation", "to": "approach"}, {"from": "TKM", "title": "statistics", "to": "biomedical"}, {"from": "TKM", "title": "statistics", "to": "multimodal"}, {"from": "TKM", "title": "statistics", "to": "instruction"}, {"from": "TKM", "title": "statistics", "to": "responses"}, {"from": "TKM", "title": "statistics", "to": "visual"}, {"from": "TKM", "title": "statistics", "to": "chat"}, {"from": "TKM", "title": "statistics", "to": "medical"}, {"from": "TKM", "title": "statistics", "to": "alignment"}, {"from": "TKM", "title": "statistics", "to": "tuning"}, {"from": "TKM", "title": "statistics", "to": "epochs"}, {"from": "TKM", "title": "statistics", "to": "samples"}, {"from": "TKM", "title": "statistics", "to": "hours"}, {"from": "TKM", "title": "statistics", "to": "downstrea"}, {"from": "TKM", "title": "heterogeneity", "to": "in HKGs"}, {"from": "TKM", "title": "in", "to": "HKGs"}, {"from": "TKM", "title": "in", "to": "medical concepts"}, {"from": "TKM", "title": "gather", "to": "medical literature"}, {"from": "TKM", "title": "gather", "to": "clinical trials"}, {"from": "TKM", "title": "gather", "to": "patientgenerated data"}, {"from": "TKM", "title": "extract", "to": "medical entities"}, {"from": "TKM", "title": "extract", "to": "relationships"}, {"from": "TKM", "title": "transform", "to": "to structured format"}, {"from": "TKM", "title": "map", "to": "entities and relationships"}, {"from": "TKM", "title": "map", "to": "to chosen ontologies"}, {"from": "TKM", "title": "collection", "to": "PubMed"}, {"from": "TKM", "title": "collection", "to": "navigational queries"}, {"from": "TKM", "title": "collection", "to": "Field Sensor"}, {"from": "TKM", "title": "collection", "to": "removed articles"}, {"from": "TKM", "title": "collection", "to": "articles without titles or abstracts"}, {"from": "TKM", "title": "patterns", "to": "predictions"}, {"from": "TKM", "title": "algorithms", "to": "classification"}, {"from": "TKM", "title": "problem", "to": "framing"}, {"from": "TKM", "title": "medical", "to": "records"}, {"from": "TKM", "title": "key", "to": "features"}, {"from": "TKM", "title": "models", "to": "utilizing"}, {"from": "TKM", "title": "could benefit from", "to": "new paradigm"}, {"from": "TKM", "title": "near-passing performance", "to": "NLEKMD"}, {"from": "making", "title": "it", "to": "difficult"}, {"from": "making", "title": "valuable", "to": "widely used"}, {"from": "clinicians", "title": "local", "to": "remote"}]);
 
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });