CARER - C linic A l Reasoning-Enhanced Representation for Temporal Health Risk Prediction (2024)

Abstract: The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high generalizability. Inspired by how human experts leverage reasoning for medical diagnosis, we propose CARER, a novel health risk prediction framework, that enhances deep learning models with clinical rationales derived from medically proficient Large Language Models (LLMs). In addition, we provide a cross-view alignment loss which aligns the “local” view from the patient’s health status with the “global” view from the external LLM’s clinical reasoning to boost the mutual feature learning. Through extensive experiments on two predictive tasks using two popular EHR datasets, our CARER’s significantly exceeds the performance of state-of-the-art models by up to 11.2%, especially in improving data efficiency and generalizability. Our code is available at https://github.com/tuandung2812/CARER-EMNLP-2024

## introduction electronic health records (ehrs) are valuable sources, widely adopted by clinicians globally to predict future health events, such as diagnosis @xcite , mortality @xcite , and readmission @xcite . ehr information can be divided into two categories: (1) structured data, encompassing icd codes, laboratory measurements, and demographic details, and (2) unstructured, free-form clinical notes. current 1 code is available at https://github.com/ tuandung2812/carer-emnlp-2024 . in recent years, state-of-the-art large language models (llms) @xcite have demonstrated substantial reasoning capabilities and efficacy across various tasks @xcite @xcite . therefore, leveraging llms to provide reasoning capabilities, thereby advancing health risk prediction, presents a compelling approach. however, the implementation of this approach poses the following challenges: our solution. to overcome the issues mentioned above, we propose carer -clinical reasoning-enhanced representation for temporal health risk prediction (fig. 1 ) -which leverages chainof-thought (cot) prompting to query clinical reasoning steps from multimodal ehr sources. to address c1, we employ retrieval augmented generation (rag) via external medical knowledge base to enrich the context of the reasoning process. additionally, we utilize the cot prompting technique to guide the reasoning process of the llm. regarding c2, we propose a verbalization mechanism to convert numerical data into text format, enriched with comprehensive semantics. to address c3, we introduce a cross-view alignment objective to enhance consistency between the local multimodal features and the global clinical rationale features. our contributions are three-fold as follows. through comprehensive experiments, our carer model surpasses state-of-the-art models by up to 11.2% on two diagnostic tasks across the mimic-iii (johnson et al., 2016) and mimic-iv (johnson et al., 2023) public benchmarks. ## related work many studies have applied deep learning models to predict diagnoses and health outcomes from temporal ehrs. we categorize the following methods into three primary approaches based on the information sources used for prediction: structured data, unstructured data or multimodal inputs, and the use of external knowledge. structured data. initial efforts focused on mining structured data like icd codes for prediction. retain @xcite uses a double rnn to process icd sequences bi-directionally, followed by a two-level attention mechanism. other methods, such as dipole @xcite , timeline @xcite , and t-lstm @xcite , combine rnns with attention. time-aware models like t-lstm @xcite and hitanet @xcite capture temporal patterns using time-decay and self-attention mechanisms. later approaches use graph-learning mechanisms from visit-level or patient-level graphs to understand relationships between medical codes @xcite @xcite . unstructured data and multimodality. recent works incorporate unstructured data, mainly clinical notes, for prediction. cgl @xcite uses attention mechanisms to emphasize important words in medical text. vecocare @xcite integrates medical codes and text representations using two transformer architectures and pretraining tasks for semantic alignment. other recent studies combine multiple structured features (icd codes, lab values, demographics, drug codes) with unstructured clinical notes. mufasa @xcite employs neural architecture search for optimal multimodal network architecture, while medhmp @xcite uses a multimodal pretraining paradigm with tasks like reconstruction and contrastive learning for better representation. external knowledge. multiple graph-based methods have exploited medical ontologies/knowledge graphs, like g-bert @xcite , cgl @xcite , sherbert @xcite metacare @xcite gram @xcite utilizing icd-9 diseases hierarchy, or kerprint utilizing medical knowledge graph containing multiple entities like diseases, lab tests, medications from snomed ct @xcite . others utilize unstructured medical documents as auxiliary information, such as medretriever @xcite and @xcite . tab. 1 compares our method with other state-of-the-art baselines. to the extent of our knowledge, we are the first to incorporate clinical reasoning for ehr's predictive modeling. 3 carer 3.1 ehr structure each patient's historical health record includes a sequence of hospital admissions @xmath0, . . . , a n ], with each admission a t (@xmath1, ..., n) contains multiple ehr modalities: categorical diagnosis codes i t , lab values v t , demographic features d t , and clinical notes n t . diagnosis codes i @xmath2, . . . , i t d ] include icd-9 codes diagnosed by healthcare professionals. lab values v @xmath3, . . . , v t m ] represent continuous numerical data, e.g., blood glucose levels and blood pressure. clinical notes n @xmath4, . . . , n t k ] are written by medical professionals, and demographic features d t include race, gender, and age. for simplicity, we omit patient-level and visit-level indices in the remaining sections. ## performance evaluation in this section, we compare the performance of cares with other diagnosis baselines on two ehr benchmarks to validate their predictive performance. afterward, we conduct an in-depth analysis on our algorithm's generalizability and ablation studies on different components that made up our work. ## conclusion this paper introduces carer, a health risk prediction framework that enhances multimodal deep learning models with clinical reasoning from large language models (llms). additionally, we introduce a multi-view alignment objective that enhances the consistency between the local view of patient-specific raw data and the global view of external llm's reasoning. extensive experiments on two predictive tasks using popular ehr datasets demonstrate that carer significantly outperforms recent models, by up to 11.2%, particularly in enhancing data efficiency and generalizability.