├── LICENSE ├── README.md ├── evaluate_performance.py ├── helpers.py ├── predictions ├── GPT-3.5 │ ├── README.md │ ├── predictions_gpt_35.json │ └── predictions_tree_search_gpt35.tsv └── Llama-70B │ ├── README.md │ ├── llama_70B_predictions.json │ └── predictions_tree_search_llama70B.tsv ├── prompt_templates.py ├── run_tree_search.py ├── translate_files.py └── tree_search_icd.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Anand Subramanian 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # automated-icd-coding-llm 2 | 3 | ### Overview 4 | This is an unofficial implementation of the algorithm described in the paper [Automated clinical coding using off-the-shelf large language models](https://openreview.net/pdf?id=mqnR8rGWkn). 5 | 6 | ### LLM Guided Tree-Search Algorithm 7 | 8 | This paper discusses the feasibility of employing off-the-shelf Large Language Models (LLMs) for ICD coding. The authors utilize a novel and practical approach which they term LLM-guided tree-search. Each ICD code is part of a hierarchical relationship where parent codes encompass broader conditions and child codes represent specific ailments. The search begins at the root and uses the LLM to choose which branches to investigate, proceeding iteratively until no further paths remain. This process determines the most relevant ICD codes, adding them as predicted labels for the clinical note. 9 | 10 | #### Process 11 | The search process operates as follows: 12 | 1. **Initiation**: Begins at the ontology's root. 13 | 2. **Navigation**: Uses the LLM to determine which branches to explore. 14 | 3. **Iteration**: Proceeds iteratively, exploring further until no viable paths remain. 15 | 4. **Conclusion**: Identifies the most relevant ICD codes and adds them as predicted labels for the medical note. 16 | 17 | ### Running the code 18 | 19 | #### Download the CodiEsp Dataset 20 | You can download the CodiEsp Dataset from this [link](https://zenodo.org/records/3837305#.XsZFoXUzZpg) 21 | 22 | #### Configure the OpenAI credentials for the client 23 | * In order to run this code, you need to configure the credentials for accessing the LLMs via API. For this, you need API keys for GPT-3.5/GPT-4 and Llama-2 70B Chat. 24 | 25 | * For GPT-3.5/GPT-4, you need to create an account with OpenAI and create an API key. 26 | 27 | * For Llama-2 70B Chat, you can create an account with [deepinfra](https://deepinfra.com/). You can create an API key, and then create an deployment of Llama-2 70B Chat, and obtain the base url for this deployment. 28 | 29 | * Both LLMs can be accessed through the `openai` library, and Line 9 in `helpers.py` need to be configured as follows: 30 | 31 | ##### For GPT-3.5/GPT-4 32 | ``` 33 | client = OpenAI(api_key=) 34 | ``` 35 | ##### For Llama-2 70B Chat 36 | ``` 37 | client = OpenAI(api_key=, base_url=) 38 | ``` 39 | 40 | #### Translate the Spanish medical notes to English 41 | Once you've downloaded the dataset, run 42 | 43 | ``` 44 | python translate_files.py --input_dir --output_dir 45 | ``` 46 | 47 | The script will use GPT-3.5 to translate the files and then save the outputs to the directory. 48 | 49 | #### Run the Tree Search Algorithm 50 | After translating the dataset, run 51 | 52 | ``` 53 | python run_tree_search.py --input_dir --output_file --model_name 54 | ``` 55 | 56 | The model name can be either `gpt-3.5-turbo-0613` for GPT-3.5 or `meta-llama/Llama-2-70b-chat-hf` for Llama-2 70B Chat. The output predictions are dumped as a json file. 57 | 58 | #### Evaluate the performance 59 | The performance is evaluated in terms of macro-average and micro-average precision, recall and f1-scores. 60 | The script for evaluation was provided by the authors of the [paper](https://openreview.net/pdf?id=mqnR8rGWkn). The evaluation script provided by the authors, is a modified version of the CodiEsp Shared Task Evaluation script. 61 | Run the script as: 62 | ``` 63 | python evaluate_performace.py --input_json --gold_standard_tsv 64 | ``` 65 | Please note the script internally converts the predictions_json file to a tsv file format, and also modifies the gold standard tsv file by adding the appropriate column names and dumps the modified version locally. This is then removed by the script after evaluation is complete. 66 | 67 | ### Differences in Implementation from the original paper 68 | **Please note** this is purely my implementation based on my understanding of the paper, and may differ in some areas compared to the original implementation, which may impact the results obtained by running this. 69 | I've jotted down a few potential differences: 70 | 71 | 1. **Prompt used for translating the documents from Spanish to English**: The paper mentions that they translate the Spanish documents to English using GPT-3.5, instead of utilizing the original translated version. I decided to implement this as well, and translated the documents to English. However, there may be some potential differences in the translated document content based on the prompt utilized, which may have an impact on the scores. I've uploaded my version of the [translated CodiEsp test set](https://drive.google.com/file/d/1iIhtAbqmEq3MRPJMBYnAeDJTiZl-q2Nl/view?usp=sharing) for usage. 72 | 73 | 2. **Extracting the predicted code descriptions from the LLM's predictions**: In the original paper, the authors follow the following steps to extract the matched ICD Descriptions: 74 | 75 | > Resolving the LLM generated text into per-code predictions is performed by processing the text as a set of lines. These lines are greedily matched, starting with the longest code description in the current prompt 76 | 77 | In my implementation, I simplify the extraction logic a bit, by assuming that each predicted ICD code descriptions is on a per-line basis and split each line by ":". This is because ideally the predictions should be of the format " ...", and splitting by the first colon should give the description and the LLM prediction. I drop the predicted line if the description extracted by the LLM is not an exact match against the ICD tree. This may have an impact on the final performance. 78 | 79 | While I've highlighted the main differences in my implementation, there may be some other minor differences that could also impact performance. Please keep these in mind, if you try this code. 80 | 81 | * While I've used the versions of the GPT-3.5 model with the parameters mentioned in the paper for reproducibility, it is possible that there could be some randomness inherent to the LLM calls for various reasons, that can impact the final performance. 82 | * I've reconstructed the prompts based on the examples based in the paper. There could be some minor differences in the prompt that may affect the performance. 83 | 84 | ### Results 85 | The results of the evaluations are presented here. This was possible, thanks to the authors of the paper who shared their evaluation code when I communicated with them. 86 | However, while my implementation's metrics are roughly in the ball-park of the reported scores, there are some note-worthy differences: 87 | 1. In my implementation, GPT-3.5's micro-average metrics slightly exceed the reported figures, whereas the macro-average metrics fall a bit short of the reported values. 88 | 2. In my implementation, Llama-70B's micro-average metrics either match or slightly exceed the reported figures, but the macro-average metrics are lesser than the reported values. 89 | 90 | As mentioned earlier, this implementation differs from the paper in some small ways, all of which impact the final performance. 91 | 92 | | Model | Micro-Average Precision | Micro-Average Recall | Micro-Average F1-Score | Macro-Average Precision | Macro-Average Recall | Macro-Average F1-Score | 93 | |-----------|:-----------------------:|:--------------------:|:----------------------:|:-----------------------:|:--------------------:|:----------------------:| 94 | | GPT-3.5 | 0.173 | 0.241 | 0.201 | 0.219 | 0.213 | 0.196 | 95 | | Llama-2 70B Chat| 0.051 | 0.172 | 0.078 | 0.113 | 0.155 | 0.11 | 96 | 97 | ### Citation 98 | If you use this code, please cite the original papers: 99 | ``` 100 | @inproceedings{ 101 | boyle2023automated, 102 | title={Automated clinical coding using off-the-shelf large language models}, 103 | author={Joseph Boyle and Antanas Kascenas and Pat Lok and Maria Liakata and Alison O'Neil}, 104 | booktitle={Deep Generative Models for Health Workshop NeurIPS 2023}, 105 | year={2023}, 106 | url={https://openreview.net/forum?id=mqnR8rGWkn} 107 | } 108 | ``` 109 | 110 | If you use the CodiEsp Dataset, please cite the following papers: 111 | 112 | ``` 113 | @inproceedings{ 114 | miranda2020overview, 115 | title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, 116 | author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{'e}, Jordi and Krallinger, Martin}, 117 | booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, 118 | year={2020} 119 | } 120 | ``` 121 | 122 | ``` 123 | @dataset{miranda_escalada_2020_3837305, 124 | author = {Miranda-Escalada, Antonio and 125 | Gonzalez-Agirre, Aitor and 126 | Krallinger, Martin}, 127 | title = {{CodiEsp corpus: gold standard Spanish clinical 128 | cases coded in ICD10 (CIE10) - eHealth CLEF2020}}, 129 | month = may, 130 | year = 2020, 131 | publisher = {Zenodo}, 132 | version = {1.4}, 133 | doi = {10.5281/zenodo.3837305}, 134 | url = {https://doi.org/10.5281/zenodo.3837305} 135 | } 136 | ``` 137 | -------------------------------------------------------------------------------- /evaluate_performance.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import warnings 3 | import numpy as np 4 | import pandas as pd 5 | import simple_icd_10_cm as cm 6 | import os 7 | import json 8 | 9 | """ 10 | @authors: antonio, Joseph Boyle 11 | 12 | The original version of this evaluation script is sourced from the CodiEsp Shared Task evaluation scripts, provided by the authors. 13 | 14 | The current version is a modified version of the original, provided by the authors of the "Automated clinical coding using off-the-shelf large language models" paper. 15 | 16 | This script contains some modifications I made for parsing the predictions made by the LLMs before calculating the metrics 17 | """ 18 | 19 | # Load valid codes lists 20 | def read_gs(gs_path: str, valid_codes: set) -> pd.DataFrame: 21 | """Read the gold-standard labels and filter to select the set of valid codes.""" 22 | gs_data = pd.read_csv(gs_path, sep="\t", names=['clinical_case', 'code'], dtype={'clinical_case': object, 'code': object}) 23 | gs_data = gs_data[gs_data['code'].str.lower().isin(valid_codes)] 24 | gs_data.code = gs_data.code.str.lower() 25 | return gs_data 26 | 27 | def read_run(pred_path: str, valid_codes: set) -> pd.DataFrame: 28 | run_data = pd.read_csv(pred_path, sep="\t", names=['clinical_case', 'code'], dtype={'clinical_case': object, 'code': object}) 29 | run_data.code = run_data.code.str.lower() 30 | run_data = run_data[run_data['code'].isin(valid_codes)] 31 | if run_data.shape[0] == 0: 32 | warnings.warn('None of the predicted codes are considered valid codes') 33 | return run_data 34 | 35 | def calculate_metrics(df_gs: pd.DataFrame, df_pred: pd.DataFrame) -> tuple[float]: 36 | pred_per_cc = df_pred.drop_duplicates(subset=['clinical_case', "code"]).groupby("clinical_case")["code"].count() 37 | Pred_Pos = df_pred.drop_duplicates(subset=['clinical_case', "code"]).shape[0] 38 | true_per_cc = df_gs.drop_duplicates(subset=['clinical_case', "code"]).groupby("clinical_case")["code"].count() 39 | GS_Pos = df_gs.drop_duplicates(subset=['clinical_case', "code"]).shape[0] 40 | cc = set(df_gs.clinical_case.tolist()) 41 | TP_per_cc = pd.Series(dtype=float) 42 | for c in cc: 43 | pred = set(df_pred.loc[df_pred['clinical_case'] == c, 'code'].values) 44 | gs = set(df_gs.loc[df_gs['clinical_case'] == c, 'code'].values) 45 | TP_per_cc[c] = len(pred.intersection(gs)) 46 | TP = sum(TP_per_cc.values) 47 | precision_per_cc = TP_per_cc / pred_per_cc 48 | recall_per_cc = TP_per_cc / true_per_cc 49 | f1_score_per_cc = (2 * precision_per_cc * recall_per_cc) / (precision_per_cc + recall_per_cc + 1e-10) 50 | precision = TP / Pred_Pos 51 | recall = TP / GS_Pos 52 | f1_score = (2 * precision * recall) / (precision + recall + 1e-10) 53 | return precision_per_cc, precision, recall_per_cc, recall, f1_score_per_cc, f1_score 54 | 55 | def calculate_metrics_simple(true: pd.DataFrame, pred: pd.DataFrame) -> dict: 56 | """Compute the macro-precision, macro-recall and macro-f1 scores.""" 57 | true_positives = 0 58 | for case_id in set(true.clinical_case): 59 | true_labels = set(true.loc[true.clinical_case == case_id].code) 60 | pred_labels = set(pred.loc[pred.clinical_case == case_id].code) 61 | true_positives += len(pred_labels.intersection(true_labels)) 62 | macro_precision = true_positives / len(pred) 63 | macro_recall = true_positives / len(true) 64 | macro_f1 = 2 / (macro_recall**-1 + macro_precision**-1) 65 | return dict(precision=macro_precision, recall=macro_recall, f1_score=macro_f1) 66 | 67 | def compute_macro_averaged_scores(df_gs: pd.DataFrame, df_run: pd.DataFrame) -> tuple[float]: 68 | codes = set(df_gs.code) 69 | precisions, recalls, f1_scores = [], [], [] 70 | for code in codes: 71 | true_cases = df_gs[df_gs.code == code] 72 | pred_cases = df_run[df_run.code == code] 73 | true_positive_count = len(set(pred_cases.clinical_case).intersection(set(true_cases.clinical_case))) 74 | precision = true_positive_count / len(pred_cases) if true_positive_count > 0 else 0 75 | recall = true_positive_count / len(true_cases) 76 | f1_score = 2 * precision * recall / (precision + recall) if precision > 0 and recall > 0 else 0 77 | precisions.append(precision) 78 | recalls.append(recall) 79 | f1_scores.append(f1_score) 80 | return np.mean(precisions), np.mean(recalls), np.mean(f1_scores) 81 | 82 | def analyse_errors(true: pd.DataFrame, pred: pd.DataFrame) -> None: 83 | """Print some randomly sampled Errors.""" 84 | import simple_icd_10_cm as cm 85 | cm.get_all_codes() 86 | total_errors, related_preds = 0, 0 87 | for case_id in ("S0004-06142006000100010-1", "S2254-28842014000300010-1", "S0004-06142006000100010-1"): 88 | print("##################\nCASE ID : ", case_id) 89 | true_labels = set(true.loc[true.clinical_case == case_id].code) 90 | pred_labels = set(pred.loc[pred.clinical_case == case_id].code) 91 | false_positives = pred_labels.difference(true_labels) 92 | false_negatives = true_labels.difference(pred_labels) 93 | print("ASSIGNED DESC'S: ", "\n\t".join([cm.get_description(x.upper()) for x in list(pred_labels)])) 94 | true_positives = pred_labels.intersection(true_labels) 95 | print(f"True positives: {len(true_positives)} / {len(pred_labels)} \n\t", "\n\t".join([cm.get_description(x.upper()) for x in list(true_positives)])) 96 | print("False positives:\n\t", "\n\t".join([cm.get_description(x.upper()) for x in list(false_positives)])) 97 | print("False negatives:\n\t", "\n\t".join([cm.get_description(x.upper()) for x in list(false_negatives)])) 98 | total_errors += len(false_positives) + len(false_negatives) 99 | subcategory = {c[:3] for c in pred_labels} 100 | related_preds += len({c for c in false_positives if c[:3] in subcategory}) 101 | print("TOTAL ERRORS ", total_errors) 102 | print("RELATED PREDS ", related_preds) 103 | 104 | def parse_arguments() -> tuple[str]: 105 | parser = argparse.ArgumentParser(description='Process user-given parameters') 106 | parser.add_argument("-g", "--gs_path", required=True, dest="gs_path", help="Path to GS file") 107 | parser.add_argument("-p", "--pred_path", required=True, dest="pred_path", help="Path to predictions file") 108 | parser.add_argument("-c", "--valid_codes_path", default='codiesp/codiesp-D_codes.tsv', dest="codes_path", help="Path to valid codes TSV") 109 | parser.add_argument("-n", "--n_first_documents", default=None, dest="n_first_documents", type=int) 110 | args = parser.parse_args() 111 | return args.gs_path, args.pred_path, args.codes_path, args.n_first_documents 112 | 113 | def read_valid_codes(codes_path: str) -> set: 114 | valid_codes = set(pd.read_csv(codes_path, sep='\t', header=None, usecols=[0])[0].tolist()) 115 | valid_codes = set(x.lower() for x in valid_codes) 116 | return valid_codes 117 | 118 | if __name__ == "__main__": 119 | 120 | parser = argparse.ArgumentParser(description="Process and evaluate medical text predictions.") 121 | parser.add_argument("--input_json", help="JSON file with predictions") 122 | parser.add_argument("--gold_standard_tsv", help="Gold standard TSV file") 123 | args = parser.parse_args() 124 | 125 | code_map = json.loads(open(args.input_json).read()) 126 | file_tsv_predictions = [] 127 | 128 | for key, value in code_map.items(): 129 | 130 | if value == []: 131 | file_tsv_predictions.append([key.replace(".txt", ""), ""]) 132 | 133 | else: 134 | for code in value: 135 | file_tsv_predictions.append([key.replace(".txt", ""),code.lower()]) 136 | 137 | df_pred = pd.DataFrame(file_tsv_predictions) 138 | df_pred.columns = ["clinical_case", "code"] 139 | df_pred.to_csv(args.input_json.replace(".json", ".tsv"), sep="\t", index=False) 140 | 141 | 142 | df_gs = pd.read_csv(args.gold_standard_tsv, sep="\t") 143 | df_gs.columns = ["clinical_case", "code"] 144 | df_gs.to_csv("gt_test.tsv", sep="\t", index=False) 145 | 146 | valid_codes = set([x.lower() for x in cm.get_all_codes() if cm.is_leaf(x)]) 147 | 148 | gs_path = "gt_test.tsv" 149 | pred_path = args.input_json.replace(".json", ".tsv") 150 | df_gs = read_gs(gs_path, valid_codes) 151 | df_run = read_run(pred_path, valid_codes) 152 | 153 | precision_per_cc, precision, recall_per_cc, recall, f1_per_cc, f1_score = calculate_metrics(df_gs, df_run) 154 | print('\n-----------------------------------------------------') 155 | print('Clinical case name\t\t\tPrecision') 156 | print('-----------------------------------------------------') 157 | 158 | for index, val in precision_per_cc.items(): 159 | print(f"{index}\t\t{round(val, 3)}") 160 | if any(precision_per_cc.isna()): 161 | warnings.warn('Some documents do not have predicted codes, document-wise Precision not computed for them.') 162 | print('\nMicro-average precision = {}\n'.format(round(precision, 3))) 163 | print('\n-----------------------------------------------------') 164 | print('Clinical case name\t\t\tRecall') 165 | print('-----------------------------------------------------') 166 | 167 | for index, val in recall_per_cc.items(): 168 | print(f"{index}\t\t{round(val, 3)}") 169 | if any(recall_per_cc.isna()): 170 | warnings.warn('Some documents do not have Gold Standard codes, document-wise Recall not computed for them.') 171 | print('\nMicro-average recall = {}\n'.format(round(recall, 3))) 172 | print('\n-----------------------------------------------------') 173 | print('Clinical case name\t\t\tF-score') 174 | print('-----------------------------------------------------') 175 | 176 | print('\nMicro-average F-score = {}\n'.format(round(f1_score, 3))) 177 | 178 | macro_precision, macro_recall, macro_f1 = compute_macro_averaged_scores(df_gs, df_run) 179 | print('MACRO-AVERAGE STATISTICS:') 180 | print(f"Macro-average precision = {round(macro_precision, 3)}") 181 | print(f"Macro-average recall = {round(macro_recall, 3)}") 182 | print(f"Macro-average F-score = {round(macro_f1, 3)}") 183 | print(f"Number of documents with predictions {len(set(df_run.clinical_case))}") 184 | 185 | # clear the temporary gt tsv file 186 | os.remove("gt_test.tsv") 187 | -------------------------------------------------------------------------------- /helpers.py: -------------------------------------------------------------------------------- 1 | import re 2 | import simple_icd_10_cm as cm 3 | from transformers import AutoModelForCausalLM, AutoTokenizer 4 | from openai import OpenAI 5 | from prompt_templates import * 6 | 7 | CHAPTER_LIST = cm.chapter_list 8 | 9 | client = OpenAI() 10 | 11 | def construct_translation_prompt(medical_note): 12 | """ 13 | Construct a prompt template for translating spanish medical notes to english. 14 | 15 | Args: 16 | medical_note (str): The medical case note. 17 | 18 | Returns: 19 | str: A structured template ready to be used as input for a language model. 20 | """ 21 | translation_prompt = """You are an expert Spanish-to-English translator. You are provided with a clinical note written in Spanish. 22 | You must translate the note into English. You must ensure that you properly translate the medical and technical terms from Spanish to English without any mistakes. 23 | Spanish Medical Note: 24 | {medical_note}""" 25 | 26 | return translation_prompt.format(medical_note = medical_note) 27 | 28 | def build_translation_prompt(input_note, system_prompt=""): 29 | """ 30 | Build a zero-shot prompt for translating spanish medical notes to english. 31 | 32 | Args: 33 | input_note (str): The input note or query. 34 | system_prompt (str): Optional initial system prompt or instruction. 35 | 36 | Returns: 37 | list of dict: A structured list of dictionaries defining the role and content of each message. 38 | """ 39 | input_prompt = construct_translation_prompt(input_note) 40 | 41 | return [{"role": "system", "content": system_prompt}, {"role": "user", "content": input_prompt}] 42 | 43 | def remove_extra_spaces(text): 44 | """ 45 | Remove extra spaces from a given text. 46 | 47 | Args: 48 | text (str): The original text string. 49 | 50 | Returns: 51 | str: The cleaned text with extra spaces removed. 52 | """ 53 | return re.sub(r'\s+', ' ', text).strip() 54 | 55 | def remove_last_parenthesis(text): 56 | """ 57 | Removes the last occurrence of content within parentheses from the provided text. 58 | 59 | Args: 60 | text (str): The input string from which to remove the last parentheses and its content. 61 | 62 | Returns: 63 | str: The modified string with the last parentheses content removed. 64 | """ 65 | pattern = r'\([^()]*\)(?!.*\([^()]*\))' 66 | cleaned_text = re.sub(pattern, '', text) 67 | return cleaned_text 68 | 69 | def format_code_descriptions(text, model_name): 70 | """ 71 | Format the ICD-10 code descriptions by removing content inside brackets and extra spaces. 72 | 73 | Args: 74 | text (str): The original text containing ICD-10 code descriptions. 75 | 76 | Returns: 77 | str: The cleaned text with content in brackets removed and extra spaces cleaned up. 78 | """ 79 | pattern = r'\([^()]*\)(?!.*\([^()]*\))' 80 | cleaned_text = remove_last_parenthesis(text) 81 | cleaned_text = remove_extra_spaces(cleaned_text) 82 | 83 | return cleaned_text 84 | 85 | def construct_prompt_template(case_note, code_descriptions, model_name): 86 | """ 87 | Construct a prompt template for evaluating ICD-10 code descriptions against a given case note. 88 | 89 | Args: 90 | case_note (str): The medical case note. 91 | code_descriptions (str): The ICD-10 code descriptions formatted as a single string. 92 | 93 | Returns: 94 | str: A structured template ready to be used as input for a language model. 95 | """ 96 | template = prompt_template_dict[model_name] 97 | 98 | return template.format(note=case_note, code_descriptions=code_descriptions) 99 | 100 | def build_zero_shot_prompt(input_note, descriptions, model_name, system_prompt=""): 101 | """ 102 | Build a zero-shot classification prompt with system and user roles for a language model. 103 | 104 | Args: 105 | input_note (str): The input note or query. 106 | descriptions (list of str): List of ICD-10 code descriptions. 107 | system_prompt (str): Optional initial system prompt or instruction. 108 | 109 | Returns: 110 | list of dict: A structured list of dictionaries defining the role and content of each message. 111 | """ 112 | if model_name == "meta-llama/Llama-2-70b-chat-hf": 113 | code_descriptions = "\n".join(["* " + x for x in descriptions]) 114 | else: 115 | code_descriptions = "\n".join(descriptions) 116 | 117 | input_prompt = construct_prompt_template(input_note, code_descriptions, model_name) 118 | return [{"role": "system", "content": system_prompt}, {"role": "user", "content": input_prompt}] 119 | 120 | def get_response(messages, model_name, temperature=0.0, max_tokens=500): 121 | """ 122 | Obtain responses from a specified model via the chat-completions API. 123 | 124 | Args: 125 | messages (list of dict): List of messages structured for API input. 126 | model_name (str): Identifier for the model to query. 127 | temperature (float): Controls randomness of response, where 0 is deterministic. 128 | max_tokens (int): Limit on the number of tokens in the response. 129 | 130 | Returns: 131 | str: The content of the response message from the model. 132 | """ 133 | response = client.chat.completions.create( 134 | model=model_name, 135 | messages=messages, 136 | temperature=temperature, 137 | max_tokens=max_tokens 138 | ) 139 | return response.choices[0].message.content 140 | 141 | def remove_noisy_prefix(text): 142 | # Removing numbers or letters followed by a dot and optional space at the beginning of the string 143 | cleaned_text = text.replace("* ", "").strip() 144 | cleaned_text = re.sub(r"^\s*\w+\.\s*", "", cleaned_text) 145 | return cleaned_text.strip() 146 | 147 | def parse_outputs(output, code_description_map, model_name): 148 | """ 149 | Parse model outputs to confirm ICD-10 codes based on a given description map. 150 | 151 | Args: 152 | output (str): The model output containing confirmations. 153 | code_description_map (dict): Mapping of descriptions to ICD-10 codes. 154 | 155 | Returns: 156 | list of dict: A list of confirmed codes and their descriptions. 157 | """ 158 | confirmed_codes = [] 159 | split_outputs = [x for x in output.split("\n") if x] 160 | for item in split_outputs: 161 | try: 162 | code_description, confirmation = item.split(":", 1) 163 | if model_name == "meta-llama/Llama-2-70b-chat-hf": 164 | code_description = remove_noisy_prefix(code_description) 165 | 166 | if confirmation.lower().strip().startswith("yes"): 167 | try: 168 | code = code_description_map[code_description] 169 | confirmed_codes.append({"code": code, "description": code_description}) 170 | except Exception as e: 171 | print(str(e) + " Here") 172 | continue 173 | except: 174 | continue 175 | return confirmed_codes 176 | 177 | def get_name_and_description(code, model_name): 178 | """ 179 | Retrieve the name and description of an ICD-10 code. 180 | 181 | Args: 182 | code (str): The ICD-10 code. 183 | 184 | Returns: 185 | tuple: A tuple containing the formatted description and the name of the code. 186 | """ 187 | full_data = cm.get_full_data(code).split("\n") 188 | return format_code_descriptions(full_data[3], model_name), full_data[1] 189 | -------------------------------------------------------------------------------- /predictions/GPT-3.5/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /predictions/GPT-3.5/predictions_gpt_35.json: -------------------------------------------------------------------------------- 1 | {"S0465-546X2010000200006-1.txt": ["M60-M79", "T07-T88", "V00-X58", "W00-X58", "R52", "R26.2", "R93.6", "W04.XXXA", "R22.41", "W01.0XXA", "S80.00XA", "S80.01XA", "W18.00XA", "W18.30XA", "W18.31XA", "W18.40XA", "S80.251A", "S81.001A"], "S1130-01082005001000011-1.txt": ["R97.1", "R19.8", "R93.3", "R93.5", "C50.111", "R10.10", "R10.13", 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"M79.0", "M79.9", "M79.0", "M79.9", "B08.20", "B97.10", "B97.11", "B97.19"], "S1139-76322017000200009-1.txt": ["L00", "B00.0", "B00.1", "B00.7", "B00.9", "B08.8", "B10.81", "B10.89", "B33.8", "B97.6", "L01.1", "L08.0", "L08.9", "L20.9", "L29.0", "L29.1", "L29.2", "L29.3", "L29.8", "L29.9", "B00.89", "B01.81", "B01.89", "B08.20", "B08.21", "B97.10", "B97.11", "B97.19", "L01.00", "L01.01", "L08.89", "L20.81", "L20.82", "L20.83", "L20.89", "L03.319"], "S1139-76322017000200010-1.txt": ["B09", "L00", "R05", "R21", "R52", "B00.0", "B00.1", "B00.2", "B00.7", "B00.9", "B08.8", "B30.0", "B30.1", "B30.2", "B30.8", "B30.9", "B33.8", "B33.8", "B34.1", "B34.8", "B34.9", "L20.9", "L29.0", "L29.3", "L29.8", "L29.9", "L49.9", "L50.0", "L50.9", "L53.0", "L53.8", "L53.9", "R06.2", "R06.4", "R06.5", "R06.9", "R07.0", "R20.2", "R20.8", "R23.4", "R23.8", "R53.1", "B00.53", "B00.89", "B08.20", "B08.21", "B95.61", "B96.89", "L20.81", "L20.82", "L20.89", "R06.02", "R06.03", "R06.82", "R06.89", "R09.81", "R09.82", "R09.81", "R09.82"], "S1139-76322017000200016-3.txt": ["R12", "R21", "R12", "R21", "L20.9", "L29.0", "L29.3", "L29.8", "L29.9", "L53.2", "L53.9", "R11.0", "R11.2", "R20.2", "R20.8", "R23.4", "R23.8", "R53.1", "L20.9", "L29.0", "L29.3", "L29.9", "L53.2", "L53.9", "R11.0", "R11.2", "R20.2", "R20.8", "R23.4", "R23.8", "R53.1", "L20.81", "L20.89", "R11.10", "R53.83", "R53.83", "L20.81", "L20.89", "R11.10", "R53.83", "R53.83"], "S1698-44472004000400012-1.txt": ["C00-C96", "C00-C75", "E68", "R17", "R52", "R58", "R64", "R97.0", "R97.8", "C15.9", "C22.0", "C22.8", "E66.3", "E66.9", "K00.2", "K00.4", "K00.8", "K01.0", "K01.1", "K01.0", "K01.1", "K04.1", "K04.2", "K04.3", "K04.4", "K04.5", "K08.0", "K70.0", "K70.2", "K70.9", "K73.0", "K73.9", "K73.0", "K73.9", "K74.1", "K74.2", "K76.0", "K76.6", "K76.7", "R00.0", "R00.8", "R00.9", "R04.0", "R04.1", "R04.2", "R07.0", "R10.0", "R16.0", "R18.0", "R25.0", "R50.9", "R50.9", "R51.0"], "S1698-44472005000400012-1.txt": ["C00-C96", "C00-C75", "C05.0", "C05.8", "C11.0"], "S1699-695X2015000100011-1.txt": ["A86", "A89", "B99.8", "B99.9", "F05", "F09", "F54", "F59", "G92", "R05", "R52", "R55", "A81.9", "A85.0", "A85.8", "A88.8", "B25.9", "B33.3", "B33.8", "B34.8", "B34.9", "F06.0", "F06.2", "F06.8", "F07.0", "F07.9", "F30.2", "F30.9", "F32.0", "F32.1", "F32.3", "F32.9", "F34.0", "F34.9", "F51.9", "G25.1", "G25.9", "G47.9", "G89.0", "G89.4", "G93.3", "G93.9", "G96.9", "G98.8", "R00.0", "R00.9", "R00.0", "R00.9", "R03.0"], "S1699-695X2015000100013-1.txt": ["I10", "R55", "I11.0", "I36.1", "I36.9", "I42.0", "I45.0", "I45.2", "I45.4", "I45.5", "I49.1", "I49.3", "I49.8", "I50.1", "I50.9", "I51.5", "I51.7", "I95.1", "I99.8", "R00.0", "R00.2", "R00.8", "R00.9", "R01.0", "R01.1", "R03.0", "R03.0", "R09.2", "R53.1", "R93.1", "R94.8", "I45.10", "I45.19", "I45.81", "I45.89", "I49.01", "I50.21", "I50.32", "I50.43", "R06.00", "R06.02", "R06.09", "R06.82", "R06.89", "R09.02", "R09.02", "R09.81", "R09.82", "R09.89", "R53.81", "R53.83", "R68.89", "R68.89", "R94.30", "R94.31", "R94.39"], "S1699-695X2015000200013-1.txt": ["E03.9", "F84.0", "F84.5", "F84.8", "F90.0", "F90.2", "F98.4", "F98.8", "L08.0", "L08.9", "L20.9", "L30.0", "L08.89", "L20.82", "L20.89"], "S1887-85712012000400006-1.txt": [], "S1887-85712016000100005-1.txt": ["C00-C96", "C00-C75", "M00-M25", "M60-M79", "M80-M94", "T07-T88", "V00-X58", "W00-X58", "C19", "C20", "G64", "I10", "R52", "R58", "G54.0", "G54.8", "I11.0", "I20.9", "I21.4", "I23.7", "I70.0", "I70.8", "I73.9", "I74.2", "L76.01", "L76.02", "L76.11", "L76.12", "L76.21", "L76.22", "L76.21", "L76.22", "L76.31", "L76.32"], "S1887-85712017000100004-1.txt": ["M00-M25", "M40-M54", "M60-M79", "T07-T88", "V00-X58", "W00-X58", "Y62-Y84", "Y69", "M60.9", "M62.3", "T79.2XXA", "T79.2XXD", "T79.2XXS", "T79.8XXA", "T79.8XXD", "T79.8XXS", "T79.9XXA", "T79.9XXD", "T79.9XXS", "Y62.0", "M21.20", "M23.91", "M24.00", "M24.08", "M24.50"], "S1888-75462016000400180-1.txt": ["M00-M25", "M40-M54", "M60-M79", "M80-M94", "T07-T88", "V00-X58", "V00-V99", "W00-X58", "I10", "R52", "D68.4", "D70.3", "D70.8", "D70.9", "D72.9", "I80.8", "I82.1", "I87.1", "I87.2", "I87.8", "M53.0", "M53.1", "M54.2", "M54.6", "M54.9", "M84.9", "M85.9", "R00.0", "R00.9", "R07.0", "R07.1", "R07.2", "R25.2"], "S1888-75462017000100042-1.txt": ["M00-M25", "M60-M79", "M80-M94", "V00-X58", "W00-X58", "R52", "G52.2", "G52.7", "G52.8", "G54.0", "G54.8", "M65.9", "M77.8", "M79.2", "M79.9", "M84.9", "R20.0", "R20.1", "R20.2", "R22.0", "R25.2", "R26.2", "R26.9", "R29.2", "R60.0", "R93.0", "R93.6", "R93.7", "G56.00", "G56.01"], "S2254-28842014000300010-1.txt": ["M40-M54", "M80-M94", "G09", "G64", "I10", "I76", "R52", "A40.0", "A40.3", "A40.8", "A40.9", "A48.0", "A48.8", "G00.2", "G00.3", "G06.1", "G54.1", "G54.4", "G62.0", "G89.0", "G89.4", "G95.9", "G98.8", "G98.8", "I11.0", "I12.0", "I12.9", "I13.0", "I13.2", "I20.0", "I20.9", "I21.9", "I23.0", "I23.6", "I23.7", "I23.8", "I25.2", "I25.5", "I25.9"]} -------------------------------------------------------------------------------- /prompt_templates.py: -------------------------------------------------------------------------------- 1 | prompt_template_dict = {"gpt-3.5-turbo-0613" : """[Case note]: 2 | {note} 3 | [Example]: 4 | 5 | Gastro-esophageal reflux disease 6 | Enteropotosis 7 | 8 | 9 | Gastro-esophageal reflux disease: Yes, Patient was prescribed omeprazole. 10 | Enteropotosis: No. 11 | 12 | [Task]: 13 | Consider each of the following ICD-10 code descriptions and evaluate if there are any related mentions in the case note. 14 | Follow the format in the example precisely. 15 | 16 | {code_descriptions}""", 17 | 18 | "meta-llama/Llama-2-70b-chat-hf": """[Case note]: 19 | {note} 20 | 21 | [Example]: 22 | 23 | * Gastro-esophageal reflux disease 24 | * Enteroptosis 25 | * Acute Nasopharyngitis [Common Cold] 26 | 27 | 28 | 29 | * Gastro-esophageal reflux disease: Yes, Patient was prescribed omeprazole. 30 | * Enteroptosis: No. 31 | * Acute Nasopharyngitis [Common Cold]: No. 32 | 33 | 34 | [Task]: 35 | Follow the format in the example response exactly, including the entire description before your (Yes|No) judgement, followed by a newline. 36 | Consider each of the following ICD-10 code descriptions and evaluate if there are any related mentions in the Case note. 37 | 38 | {code_descriptions}""" 39 | } 40 | 41 | 42 | -------------------------------------------------------------------------------- /run_tree_search.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import json 4 | from tree_search_icd import get_icd_codes 5 | from tqdm import tqdm 6 | 7 | def process_medical_notes(input_dir, output_file, model_name): 8 | code_map = {} 9 | # Ensure the input directory is valid 10 | if not os.path.isdir(input_dir): 11 | raise ValueError("The specified input directory does not exist.") 12 | 13 | # Process each file in the input directory 14 | for files in tqdm(os.listdir(input_dir)): 15 | file_path = os.path.join(input_dir, files) 16 | with open(file_path, "r", encoding="utf-8") as file: 17 | medical_note = file.read() 18 | 19 | icd_codes = get_icd_codes(medical_note, model_name) 20 | code_map[files] = icd_codes 21 | 22 | # Save the ICD codes to a JSON file 23 | with open(output_file, "w") as f: 24 | json.dump(code_map, f, indent=4) 25 | 26 | if __name__ == "__main__": 27 | parser = argparse.ArgumentParser(description="Process medical notes to extract ICD codes using a specified model.") 28 | parser.add_argument("--input_dir", help="Directory containing the medical text files") 29 | parser.add_argument("--output_file", help="File to save the extracted ICD codes in JSON format") 30 | parser.add_argument("--model_name", default="gpt-3.5-turbo-0613", help="Model name to use for ICD code extraction") 31 | 32 | args = parser.parse_args() 33 | process_medical_notes(args.input_dir, args.output_file, args.model_name) 34 | -------------------------------------------------------------------------------- /translate_files.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from helpers import build_translation_prompt, get_response 4 | from tqdm import tqdm 5 | 6 | def translate_directory(input_dir, output_dir, model_name): 7 | # Create the output directory if it does not exist 8 | os.makedirs(output_dir, exist_ok=True) 9 | 10 | translated_files = {} 11 | # Process each file in the input directory 12 | for item in tqdm(os.listdir(input_dir)): 13 | input_path = os.path.join(input_dir, item) 14 | with open(input_path, "r", encoding="utf-8") as file: 15 | input_note = file.read() 16 | 17 | translation_prompt = build_translation_prompt(input_note) 18 | translated_note = get_response(translation_prompt, model_name=model_name) 19 | translated_files[item] = translated_note 20 | 21 | # Save the translated files to the output directory 22 | for key, value in translated_files.items(): 23 | output_path = os.path.join(output_dir, key) 24 | with open(output_path, "w", encoding="utf-8") as file: 25 | file.write(value) 26 | 27 | if __name__ == "__main__": 28 | parser = argparse.ArgumentParser(description="Translate text files from one directory to another using a specified model.") 29 | parser.add_argument("--input_dir", help="Directory containing text files to be translated") 30 | parser.add_argument("--output_dir", help="Directory to save the translated text files") 31 | parser.add_argument("--model_name", default="gpt-3.5-turbo-0613", help="Model name to use for translation") 32 | 33 | args = parser.parse_args() 34 | translate_directory(args.input_dir, args.output_dir, args.model_name) 35 | -------------------------------------------------------------------------------- /tree_search_icd.py: -------------------------------------------------------------------------------- 1 | from helpers import * 2 | 3 | def get_icd_codes(medical_note, model_name="gpt-3.5-turbo-0613", temperature=0.0): 4 | """ 5 | Identifies relevant ICD-10 codes for a given medical note by querying a language model. 6 | 7 | This function implements the tree-search algorithm for ICD coding described in https://openreview.net/forum?id=mqnR8rGWkn. 8 | 9 | Args: 10 | medical_note (str): The medical note for which ICD-10 codes are to be identified. 11 | model_name (str): The identifier for the language model used in the API (default is 'gpt-3.5-turbo-0613'). 12 | 13 | Returns: 14 | list of str: A list of confirmed ICD-10 codes that are relevant to the medical note. 15 | """ 16 | assigned_codes = [] 17 | candidate_codes = [x.name for x in CHAPTER_LIST] 18 | parent_codes = [] 19 | prompt_count = 0 20 | 21 | while prompt_count < 50: 22 | code_descriptions = {} 23 | for x in candidate_codes: 24 | description, code = get_name_and_description(x, model_name) 25 | code_descriptions[description] = code 26 | 27 | prompt = build_zero_shot_prompt(medical_note, list(code_descriptions.keys()), model_name=model_name) 28 | lm_response = get_response(prompt, model_name, temperature=temperature, max_tokens=500) 29 | predicted_codes = parse_outputs(lm_response, code_descriptions, model_name=model_name) 30 | 31 | for code in predicted_codes: 32 | if cm.is_leaf(code["code"]): 33 | assigned_codes.append(code["code"]) 34 | else: 35 | parent_codes.append(code) 36 | 37 | if len(parent_codes) > 0: 38 | parent_code = parent_codes.pop(0) 39 | candidate_codes = cm.get_children(parent_code["code"]) 40 | else: 41 | break 42 | 43 | prompt_count += 1 44 | 45 | return assigned_codes 46 | --------------------------------------------------------------------------------