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README.md

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+
+---
+license: cc-by-nc-4.0
+library_name: mamba-tiny-16384-clmbr
+tags:
+- healthcare
+- medical
+extra_gated_prompt: "You agree to all terms outlined in 'The EHRSHOT Credentialed Health Data License' (see https://shahlab.stanford.edu/ehrshot_license). Access requires a verified CITI training certificate using the same process outlined by PhysioNet (see https://physionet.org/about/citi-course/). Please complete the 'Data or Specimens Only Research' course and please provide proof via the verification URL, which takes the form https://www.citiprogram.org/verify/?XXXXXX. You agree to not use the model to conduct experiments that cause harm to human subjects." 
+extra_gated_fields:
+ Full Name: text
+ Email: text
+ Affiliation: text
+ CITI Certification Verification URL: text
+ I agree to all terms outlined in 'The EHRSHOT Credentialed Health Data License': checkbox
+ I agree to use this model for non-commercial use ONLY: checkbox
+---
+
+# mamba-tiny-16384-clmbr
+
+This is a **mamba** model with context length **16384** from the [Context Clues paper](TODO).
+
+It is a foundation model trained from scratch on the structured data within 2.57 million deidentified EHRs from Stanford Medicine.
+
+As input, this model expects a sequence of coded medical events that have been mapped to Standard Concepts within the [OMOP-CDM vocabulary](https://ohdsi.github.io/CommonDataModel/index.html). As output, the model can generate either (a) synthetic future timelines or (b) a vector representation of a patient which can then be used for downstream prediction tasks.
+
+## Usage
+
+First, install the `hf_ehr` package:
+```bash
+pip install transformers torch hf_ehr
+```
+
+Second, run this Python script to do inference on a patient representation:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from hf_ehr.data.tokenization import CLMBRTokenizer
+from hf_ehr.config import Event
+from typing import List, Dict
+import torch
+
+####################################
+# 1. Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("StanfordShahLab/mamba-tiny-16384-clmbr")
+tokenizer = AutoTokenizer.from_pretrained("StanfordShahLab/mamba-tiny-16384-clmbr")
+
+####################################
+# 2. Define patient as sequence of `Event` objects. Only `code` is required.
+patient: List[Event] = [
+    Event(code='SNOMED/3950001', value=None, unit=None, start=None, end=None, omop_table=None),
+    Event(code='Gender/F', value=None, unit=None, start=None, end=None, omop_table=None),
+    Event(code='Ethnicity/Hispanic', value=None, unit=None, start=None, end=None, omop_table=None),
+    Event(code='SNOMED/609040007', value=None, unit=None, start=None, end=None, omop_table=None),
+    Event(code='LOINC/2236-8', value=-3.0, unit=None, start=None, end=None, omop_table=None),
+    Event(code='SNOMED/12199005', value=26.3, unit=None, start=None, end=None, omop_table=None),        
+]
+
+####################################
+# 3. Tokenize patient
+batch: Dict[str, torch.Tensor] = tokenizer([ patient ], add_special_tokens=True, return_tensors='pt')
+# > batch = {
+#     'input_ids': tensor([[ 5, 0, 7, 9, 27, 2049, 6557, 22433, 1]]), 
+#     'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
+#     'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])
+# }
+textual_tokens: List[str] = tokenizer.convert_events_to_tokens(patient)
+# > textual_tokens = ['SNOMED/3950001', 'Gender/F', 'Ethnicity/Hispanic', 'SNOMED/609040007', 'LOINC/2236-8 || None || -1.7976931348623157e+308 - 4.0', 'SNOMED/12199005 || None || 26.0 - 28.899999618530273']
+
+####################################
+# 4. Run model
+logits = model(**batch).logits
+# > logits.shape = torch.Size([1, 9, 39818])
+
+####################################
+# 5. Get patient representation for finetuning (usually we choose the last token's logits)
+representation = logits[:, -1, :]
+# > representation.shape = torch.Size([1, 39818])
+```
+
+## Model Details
+
+- **Developed by:** Shah lab @ Stanford University
+- **Funded by:** Stanford Healthcare
+- **Shared by:** Shah lab @ Stanford University
+- **Model type:** mamba
+- **Languages:** Electronic health record codes (as standardized by the [OMOP-CDM](https://ohdsi.github.io/CommonDataModel/index.html))
+- **License:** CC-BY NC 4.0
+- **Finetuned from model:** N/A -- trained from scratch
+
+## Uses
+
+This model is intended to generate representations for patients based on the structured data within their electronic health record. 
+These representations can then be used for downstream tasks such as predicting diagnoses, detecting anomalies, or doing propensity score matching for causal inference.
+
+### Direct Use
+
+You will likely want to tune the model for your downstream use case. 
+
+### Out-of-Scope Use
+
+This model is for research purposes only. It is not for use in any real-world decision making that impacts patients, providers, or hospital operations.
+
+## Bias, Risks, and Limitations
+
+This model was trained on a corpus of 2.57 million patients from Stanford Medicine. 
+The model will thus reflect the patterns of how care is delivered at Stanford Medicine, in addition to the racial and socioeconomic makeup of Stanford Medicine's patient base. 
+This model may not generalize well to other hospitals and demographic mixes. 
+
+While this is technically a generative model, we have not tested its generative abilities and thus do not anticipate it being used to generate synthetic EHR records. 
+We aim to explore its generative abilities in future work.
+
+## Training Details
+
+Full training details are provided in our accompanying paper, [TODO]
+
+### Training Data
+
+The model is trained on 2.57 million patients from the [Stanford Medicine Research Data Repository (STARR)](https://academic.oup.com/jamiaopen/article/6/3/ooad054/7236015), 
+which contains EHR data from both Stanford Health Care (primarily adult care) and Lucile Packard Children’s Hospital (primarily pediatric care). 
+The dataset contains only structured data (i.e. no clinical text or images) and covers demographics (e.g. age, sex, race), diagnoses, procedures, laboratory results, medication prescriptions, and other coded clinical observations. 
+The data is formatted according to the [Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM)](https://ohdsi.github.io/CommonDataModel/cdm53.html). 
+All data that we work with is deidentified.
+
+### Training Procedure
+
+We train our model using an autoregressive next code prediction objective, i.e. predict the next code in a patient's timeline given their previous codes.
+
+## Citation
+
+**BibTeX:**
+```
+@article{TODO,
+  title={TODO}, 
+  author={TODO},
+  booktitle={TODO},
+  year={TODO}
+}
+```
+
+## Model Card Authors
+
+Michael Wornow, Suhana Bedi, Ethan Steinberg

config.json

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+{
+  "architectures": [
+    "MambaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "cls_token_id": 5,
+  "conv_kernel": 4,
+  "d_model": 768,
+  "eos_token_id": 1,
+  "expand": 2,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.1,
+  "intermediate_size": 1536,
+  "layer_norm_epsilon": 1e-05,
+  "mask_token_id": 6,
+  "model_type": "mamba",
+  "n_layer": 24,
+  "num_hidden_layers": 24,
+  "pad_token_id": 4,
+  "rescale_prenorm_residual": false,
+  "residual_in_fp32": true,
+  "sep_token_id": 3,
+  "state_size": 16,
+  "time_step_floor": 0.0001,
+  "time_step_init_scheme": "random",
+  "time_step_max": 0.1,
+  "time_step_min": 0.001,
+  "time_step_rank": 48,
+  "time_step_scale": 1.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "unk_token_id": 2,
+  "use_bias": false,
+  "use_cache": true,
+  "use_conv_bias": true,
+  "use_mambapy": false,
+  "vocab_size": 39818
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 4,
+  "transformers_version": "4.46.3"
+}

model.safetensors

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