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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07560 |
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| _version_ | 1866914793875570688 |
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| author | Pedersen, Bjørn Islam, Maisha Kristoffersen, Doris Tove Bongo, Lars Ailo Garrett, Eilidh Reid, Alice Sommerseth, Hilde |
| author_facet | Pedersen, Bjørn Islam, Maisha Kristoffersen, Doris Tove Bongo, Lars Ailo Garrett, Eilidh Reid, Alice Sommerseth, Hilde |
| contents | This paper investigates the feasibility of using pre-trained generative Large Language Models (LLMs) to automate the assignment of ICD-10 codes to historical causes of death. Due to the complex narratives often found in historical causes of death, this task has traditionally been manually performed by coding experts. We evaluate the ability of GPT-3.5, GPT-4, and Llama 2 LLMs to accurately assign ICD-10 codes on the HiCaD dataset that contains causes of death recorded in the civil death register entries of 19,361 individuals from Ipswich, Kilmarnock, and the Isle of Skye from the UK between 1861-1901. Our findings show that GPT-3.5, GPT-4, and Llama 2 assign the correct code for 69%, 83%, and 40% of causes, respectively. However, we achieve a maximum accuracy of 89% by standard machine learning techniques. All LLMs performed better for causes of death that contained terms still in use today, compared to archaic terms. Also they perform better for short causes (1-2 words) compared to longer causes. LLMs therefore do not currently perform well enough for historical ICD-10 code assignment tasks. We suggest further fine-tuning or alternative frameworks to achieve adequate performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coding historical causes of death data with Large Language Models Pedersen, Bjørn Islam, Maisha Kristoffersen, Doris Tove Bongo, Lars Ailo Garrett, Eilidh Reid, Alice Sommerseth, Hilde Machine Learning This paper investigates the feasibility of using pre-trained generative Large Language Models (LLMs) to automate the assignment of ICD-10 codes to historical causes of death. Due to the complex narratives often found in historical causes of death, this task has traditionally been manually performed by coding experts. We evaluate the ability of GPT-3.5, GPT-4, and Llama 2 LLMs to accurately assign ICD-10 codes on the HiCaD dataset that contains causes of death recorded in the civil death register entries of 19,361 individuals from Ipswich, Kilmarnock, and the Isle of Skye from the UK between 1861-1901. Our findings show that GPT-3.5, GPT-4, and Llama 2 assign the correct code for 69%, 83%, and 40% of causes, respectively. However, we achieve a maximum accuracy of 89% by standard machine learning techniques. All LLMs performed better for causes of death that contained terms still in use today, compared to archaic terms. Also they perform better for short causes (1-2 words) compared to longer causes. LLMs therefore do not currently perform well enough for historical ICD-10 code assignment tasks. We suggest further fine-tuning or alternative frameworks to achieve adequate performance. |
| title | Coding historical causes of death data with Large Language Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.07560 |