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Developing a general-purpose clinical language inference model from a large corpus of clinical notes

Several biomedical language models have already been developed for clinical language inference. However, these models typically utilize general vocabularies and are trained on relatively small clinical corpora. We sought to evaluate the impact of using a domain-specific vocabulary and a large clinical training corpus on the performance of these language models in clinical language inference. We trained a Bidirectional Encoder Decoder from Transformers (BERT) model using a diverse, deidentified corpus of 75 million deidentified clinical notes authored at the University of California, San Francisco (UCSF). We evaluated this model on several clinical language inference benchmark tasks: clinical and temporal concept recognition, relation extraction and medical language inference. We also evaluated our model on two tasks using discharge summaries from UCSF: diagnostic code assignment and therapeutic class inference. Our model performs at par with the best publicly available biomedical language models of comparable sizes on the public benchmark tasks, and is significantly better than these models in a within-system evaluation on the two tasks using UCSF data. The use of in-domain vocabulary appears to improve the encoding of longer documents. The use of large clinical corpora appears to enhance document encoding and inferential accuracy. However, further research is needed to improve abbreviation resolution, and numerical, temporal, and implicitly causal inference.

training these general-purpose clinical language models. We compare the performance of our model to different publicly available clinical language models and provide further discussions ahead.
To the best of our knowledge, the study most closely related to ours is the contemporary work of training the new GatorTron clinical language model [26]. This model is a Megatron model [13] trained on a large corpus of deidentified clinical notes from University of Florida network jointly with text from biomedical articles, the MIMIC-III corpus, and Wikipedia. The smallest GatorTron model is three times the size of our model and has been trained on a pod of 992 GPUs.

Training data
Under the UCSF IRB (#21-34088), we have trained a domain-specific, transferable BERT baseline (base) language model on a corpus of 75 million clinical notes at UCSF Health, de-identified as previously described [27]. This corpus of 39 billion words encompasses data from 2.25 million patients spanning 28 million encounters between 2012 and early 2021. These notes record fine-grained details of patient visits and reflect the advanced medical knowledge of care providers at a tertiary care center in the United States. The corpus is significantly larger than many other publicly available clinical and biomedical corpora, as shown in Table 1. Some frequent note types in the corpus include imaging reports, progress notes, telephonic encounter notes, consults, emergency department notes, pathology and cytology reports, ECG reports, assessment and plan notes, procedure-related notes, and discharge summaries. This large diversity in categories of notes also reflects that the corpus is more heterogeneous than publicly available clinical corpora. The complete list of the categories of notes in the training corpus and their frequencies can be referred to in the Supplementary Materials, Table S1.

Tokenization and vocabulary
Vocabulary size for subword tokenizers is a predetermined hyperparameter that corresponds to the upper bound on the number of tokens created from a corpus. After the vocabulary is generated, input words that do not map to the model vocabulary are split into subword tokens. As the vocabulary size increases, so do the number of parameters in language models, since the embedding layer is dependent on it. To account for the large size and diversity of our corpus, we have incorporated a larger custom clinical vocabulary of 64,000 cased tokens by training a BERT wordpiece tokenizer [28] on the UCSF corpus 2 , instead of 35,000-50,000 tokens that are frequently used. We hypothesized that a larger clinical vocabulary would make the model more suitable for processing long clinical documents by encoding domain-specific context more concisely.

Optimization specifics for model pretraining
Our model training was distributed across four 20GB NVIDIA GPUs within a single node of the Sun Grid Engine (SGE) high-performance computing cluster at UCSF. We additionally used Message Passing Interface (MPI) and Accelerated Linear Algebra (XLA) optimization via NVIDIA NGC singularity containers on this computing cluster. The training was completed in two phases with the Adam optimizer [29]: i) In the first phase, we trained the UCSF-BERT (base) model on sequence lengths of up to 128 tokens for 2/3rd of the training time of 500k steps. ii) In the second phase, we continued to train the same model, but with the maximum sequence length of 512 tokens, for the remaining nearly 1/3rd of the training time or 275k steps. This two-phased approach was critical to utilizing a large corpus in an academic setup. The complexity of training a transformers model is of the order N 2 in sequence length. Doing so in two phases speeds up the training process significantly, as the model converges to a good parameter distribution relatively faster when training on shorter sequences. In the first phase, the model efficiently learns the embeddings for the first 128 token positions and requires fewer steps for converging on longer sequences later. Across both phases, we iterated over our entire corpus nearly once, although we anticipate that training it for longer may further improve the performance on downstream tasks.
To approximate the gradients more accurately before backpropagation and reduce the number of steps required for convergence, we use gradient

Public benchmarks
In line with the prior literature [22], we have benchmarked our model on three popular publicly available clinical language processing datasets: 2010 i2b2 shared tasks for concept recognition and relation classification [30], 2012 i2b2 shared task for temporal entity extraction [31], and medical language inference (MedNLI) [32]. These tasks are described in further detail in Supplementary Materials, Section S-A. We have not evaluated the performance on prior biomedical tasks, such as those related to named entity recognition of chemicals, proteins, and genes. We instead focus exclusively on performing evaluations on clinical tasks that can be inferred from information in Electronic Health Records (EHRs). We have also excluded evaluation on the i2b2-2014 Protected Health Information (PHI) identification task [33] due to the inherent differences in the notation of PHI between the UCSF dataset and the MIMIC-III critical care dataset [15,16] that these tasks are based on.

UCSF-specific tasks
We have additionally evaluated the UCSF-BERT model on two clinical tasks on the UCSF data. These tasks consist of discharge reports for patient encounters at UCSF and labels related to the encounter. The text reports for these tasks are longer than the public benchmarks we have evaluated and provide a within-domain evaluation of the model's performance. For both tasks, we only retain discharge summaries longer than 2000 characters in length and exclude the discharge summaries recorded by nursing providers. If multiple discharge reports are found for the same encounter, we retain the longest report as the discharge summary pertaining to that encounter.
1) Top 50 ICD coding: In the first task, we implemented the popular setup of assigning International Classification of Diseases (ICD) diagnostic codes to discharge summaries from the list of 50 most frequent ICD codes in the dataset [34]. This is a multi-label classification task because every encounter can be assigned multiple ICD codes corresponding to multiple diagnoses. To reduce the complexity of mapping ICD-9 and ICD-10 codes, we restrict ourselves only to those encounters that contain a valid ICD-9 code and exclude ICD-10 codes from the current analysis. The complete list of the most frequent ICD-9 codes in our dataset can be found in Table S2 in Supplementary Materials. This task setup contains 28k discharge reports at UCSF. They have further been split into 8:1:1 train:dev:test sets by patient IDs such that the same patients do not repeat between different subsets. 2) Therapeutic class prediction: In this task, we classify the therapeutic classes of the medications ordered for or administered to a patient during an encounter. These therapeutic classes are inferred from the ingredients present in medications and are based on Anatomic Therapeutic Classification (ATC) [35]. These classes are already present as structured data in our EHR, and fall into one of the 50 broad categories of medications such as vitamins, antibiotics, antineoplastics, etc. The complete list of these therapeutic classes can be found in Table S3 in Supplementary Materials. Given that every patient could be administered multiple medications during a stay, this task is also a multi-label classification task. For this task, we obtain a dataset of 22k discharge reports at UCSF, which we have split in the same manner as the ICD-coding dataset.
Due to its focus on document-level classification, the UCSF-specific tasks contain instances that are an order of magnitude longer than the public benchmark tasks (all of which are sentence-level). The median length of UCSF discharge reports is at least 1k tokens, whereas public tasks contain a median instance length of at most 28 tokens. These numbers can be referred to in further detail in Table S4 in the Supplementary Materials.
We compare the performance of our UCSF-BERT model with several popular publicly-available biomedical and clinical language models: the BioBERT model [21], the clinical BERT model [17], the SciBERT model [18], the Biomed-RoBERTa model [20], the BioLM model [22], and the GatorTron-OG model [26] (See Section S-B in the Supplementary Materials for details). To ensure a fair comparison, we have only included the results from models of comparable sizes (in terms of the number of parameters), i.e, baseline (base) transformers models. In addition, for comparison, we evaluated the much larger GatorTron-OG model to illustrate the impact of a greatly increased parameter count (345M vs 135M) on the model performance.

Effect of sequence length
To analyze the impact of sequence length, we developed a second version of the UCSF-BERT model that supports the maximum sequence length of only 128 tokens. After an initial 500k steps of model training, we continue to train this model for an additional 275k steps to stop at the same iteration as the UCSF-BERT (512) model. We compare the performance of this 128-length model with the 512-length model to analyze the impact of sequence length on downstream tasks. Both models are trained for the same number of optimization steps. Hence, any differences in the obtained results would primarily be due to the differences in the processing limits of the two UCSF-BERT models.

RESULTS
In Table 2 and Figure 2, we present the results of named entity recognition and classification tasks. These results are computed as the median over 5 runs with different seeds. The confidence interval of the performance is indicated as the standard deviation over these 5 runs and can be analyzed in Figure 2.  coding and the therapeutic class prediction tasks on UCSF data. The confidence intervals are computed from results over 5 runs with different seeds. The UCSF-BERT model here supports a sequence length of up to 512 tokens.
We find that the UCSF-BERT (512) model performs at par with public models on all the public benchmark tasks and holds state-of-the-art performance on the internal UCSF tasks. This highlights the utility of this model for clinical language inference across a wide range of fundamental NLP tasks on EHR data. Moreover, the multi-label task of classifying the correct ICD-9 codes from discharge summaries is significantly more challenging than classifying the therapeutic classes for prescribed medications from these notes.
Furthermore, training language models on the same data source shows a significant benefit. All the publicly available benchmark tasks are based on text from the MIMIC-III corpus [15,16]. The same corpus is also included in the training data of the BioLM model and the GatorTron-OG model, which hold the best performance on these tasks 4 . The UCSF-BERT model that has been trained on clinical notes from the same institution performs the best on the internal evaluation tasks, comparable to the three-times larger GatorTron-OG model. Note that despite the seemingly larger median performance of the GatorTron-OG model, across multiple runs, it performed similarly to the UCSF-BERT model on these tasks ( Figure 2).
We additionally find that the performance of the UCSF-BERT model that uses only 128 tokens is lower than that of the model that uses 512 tokens. The differences are higher for tasks containing longer instances in the dataset, with the difference being the most significant (>17% micro-F1) in inferring diagnostic codes from long discharge summaries. Only marginal differences are observed on the i2b2 2010 and 2012 NER tasks, which contain the shortest sequences of text.  [6], which has also been reused by the BioBERT [21] and ClinicalBERT [17] models (Table 3). We additionally find that specific clinical procedures are better reflected in custom UCSF vocabulary than the PubMed vocabulary, although PubMed better covers biomedical terms for proteins and chemical compounds.

Impact of a custom vocabulary
To analyze the impact of this clinical vocabulary, we assess the difference in sequence lengths (in terms of the number of tokens) when different vocabularies are used to encode the same text. BERT and RoBERTa models both use tokenizers that split out-of-vocabulary terms into subword units. This circumvents the issue of out-of-vocabulary terms, but the tokenizer vocabulary itself becomes important in determining token sequences, and consequently the sequence length of examples. We compute the average sequence length in the dataset by encoding the examples in our evaluation datasets with different vocabularies of the baseline models. The length of an example is defined as the number of tokens in that example, dependent on the vocabulary in use. We present the mean and the median number of tokens in the datasets, when encoded with different vocabularies, in Table S5 in the Supplementary Materials. We find that the EHR-specific vocabulary created from the UCSF de-identified clinical notes corpus provides tokenized clinical datasets that are shorter by nearly 20% compared to the BERT vocabulary derived from Wikipedia and BooksCorpus. The difference in lengths is smaller, albeit still significant (nearly 5%), with the BioLM vocabulary created from PubMed data. Fewer tokens are split into subword units when using the UCSF-BERT vocabulary because it contains several long tokens specific to the clinical rhetoric, for example, hepatosplenomegaly, atherosclerotic, Hypercholesterolemia, retroperitoneal, hydrochlorothiazide, etc.

DISCUSSION
Our results highlight the UCSF-BERT model is useful for many clinical inference tasks. Significant performance gains are visible in a within-institution evaluation compared to most publicly available biomedical language models, which highlights the benefit of training language models on in-domain data. This suggests that the UCSF-BERT model can be improved further on publicly available benchmark tasks with further pretraining on publicly available data sources -biomedical abstracts and articles, and the MIMIC-III corpus.
One explanation why the ICD coding task is more complex than the therapeutic class prediction task is that ICD codes incorporate a complex and imbalanced hierarchy [36], and are also noisy [37]. However, a linear, lexical mapping exists between the medication names and their therapeutic classes, making this inference task more learnable.
Furthermore, the performance drop in the model that uses only 128 tokens long sequences, as compared to 512 tokens, reinforces our understanding that models supporting longer sequences are more suitable for real-world clinical use cases that often require document-level or multi-document inferences. We can benefit further by exploring strategies that support even longer sequences during model pretraining, such as the Longformer [10] or the BigBird models [14]. However, these models are also computationally more expensive to train, which often makes their training infeasible.
In addition, we find that encoding the datasets with an in-domain vocabulary allows us to process significantly longer contexts compared to using out-of-domain publicly available models and vocabularies. This strategy is particularly beneficial for circumventing the sequence length limits imposed by the computational complexity of transformers models and making inferences at the document-level for capturing longer distance dependencies.
Finally, since our language model is trained on the clinical notes from a single institution, they may contain biases specific to the institution such as the patient and disease distribution, EHR vendor, writing style, etc. The ability to include clinical notes from multiple centers, which is challenging due to privacy concerns, would allow us to train a more generalizable language model.

Named entity recognition
On analyzing the errors made by the UCSF-BERT model on the i2b2 2010 concept recognition task (examples presented in Table S6 the Supplementary Materials), we find that most of the errors occur because the modifiers are included as a part of the annotated concepts in the dataset. The model either excludes them, includes too many modifiers, or joins concepts across modifiers. Although these errors are not critical for most downstream applications, it highlights the influence of the annotation guidelines on the model outputs.
Additionally, we find that the model has difficulty in inferring terms that are highly domain-specific and contextual, such as abbreviations and acronyms, e.g. -nt, NGTD, "started on sips", "advanced to clears". Although training the UCSF-BERT model on a large clinical corpus reduces these errors, further research is needed to provide additional context to these terminologies, particularly in the setups that require cross-institution transfer.
Furthermore, we find that our concept detection model is highly contextual. If the context is sufficient to infer the type of the concept, it can be recognized as such even if the concept phrase itself is ambiguous (for example, the italicized phrase was recognized as treatment in "Baby was vigorous at birth and received routine DR Johnson"). This finding reinforces the contextual ability of the transformers models, especially when trained in the BIO setup.
Lastly, we also find that the model mixes up concepts whose type may be either too generic (e.g. problems or findings annotated as the concept "Problem") or ambiguous (e.g. incision that can either be a treatment or a finding annotated as problem, or C. difficile toxin that can either be a problem or a test). These concepts are sometimes inconsistently annotated in the corpus, which can make it challenging for the model to learn them. For example, further care is annotated as the concept "Treatment", but conservative management is not. Similarly, NG tubes have been annotated as "Treatment" although they are a method for delivering treatment instead of the treatment itself. However, we also find that the model sometimes misses identifying simpler concepts, for example, treatment-related concepts anticoagulated, daily transfusions, and pain control, and problem-related concepts like a previous trauma.

Classification
Limitations of the BioBERT model when making inferences in the clinical domain have been demonstrated earlier [38]. In a similar spirit, we have explored the limitations of the UCSF-BERT model fine-tuned on the MedNLI dataset by evaluating it on a range of manually curated NLI instances, which can be referred to in Table S7 in the Supplementary Materials. 1) Numeric inference: We created several instances to reflect inference relations between laboratory values and clinical conditions/diagnoses. Particularly, we analyzed the model's understanding of blood glucose levels, blood pressure, BMI, pulse rate, and hematocrit to infer whether they correspond to hyper/hypo-glycemia, hyper/hypo-tension/shock, obesity/overweight/healthy weight, tachycardia/bradycardia, and anemia respectively. We found that the UCSF-BERT model can reliably infer the clinical state for frequent lab tests (e.g., glucose measurements) and numeric results (e.g., 600, 30). However, the model is not reliable in inferring from relatively infrequent tests such as those about serum calcium and albumin levels. This is expected based on prior literature demonstrating limited numeric reasoning skills of language models [39]. This reiterates the benefit of training language models on large clinical corpora that mention numeric results, as well as emphasizes the need for integrating external knowledge sources to encode infrequent clinical evidence.
2) Inference between clinical description and the patient's state: We further curated several instances where the premise discusses clinical rhetoric describing a patient's state, and the hypothesis is the potential conclusion or differential diagnosis for the patient (for example relating lower abdomen pain or tenderness with appendicitis, blood culture findings with infectious diseases, and medications with corresponding diseases). We found that the UCSF-BERT model performs surprisingly well in these cases, potentially due to the correlations learned from the clinical notes. However, more complex inferences requiring an understanding of causation, for example, adverse drug events, need further research for reliable inference.
3) Temporal inference: We analyzed whether the UCSF-BERT model can make inferences over a sequence of events to infer the temporal ordering of different events from the clinical rhetoric. We, unfortunately, found that the UCSF-BERT model is not good at inferring temporal ordering without being explicitly trained for the task. The clinical text contains several references to the temporal sequence of events both before the clinical presentation and during treatment. To support better inferences over complex clinical timelines, self-supervised pre-training objectives that encode temporality would provide better inductive biases to language models to perform these inferences.

CONCLUSIONS
A transformers model trained on a large corpus of clinical notes from the same health system as the downstream applications performs better inference on downstream tasks than transferring knowledge from publicly available biomedical and clinical models of comparable sizes. Furthermore, these models can process longer contexts when trained with a custom, domain-specific vocabulary. The advantage of domain-specific data reduces when using significantly larger transformers models trained on similar, but not the same, data sources. The UCSF-BERT model is excellent at making contextual inferences from explicit mentions in the text However, clinical transformers models still struggle with abbreviation and acronym resolution, temporal sequence inference, implicit causal inference, and infrequent numeric inference. Future directions include further research on long sequence encoding, temporal and numerical inference bolstering, and out-of-domain generalization. Incorporating diverse corpora during model training -biomedical abstracts and articles, the MIMIC-III corpus, as well as clinical notes from multiple health systems -would potentially improve the generalizability of these models.

SUPPLEMENTARY MATERIALS
Section S-A: Different publicly available clinical inference tasks that we have evaluated our model on are described next: 1) i2b2 2010 named entity recognition (NER) [30]: This task addresses the fundamental question of concept recognition of types problems, treatments, and tests from clinical text. The dataset consists of sentences from 871 de-identified clinical reports annotated with these concepts. The task is set up as a named entity recognition task, where the goal is to identify the category of every token in text in terms of Begin-Inside-Out (BIO) tags for each of the 3 categories.

Error Category Example
Predicted as treatment but not annotated as such The patient was a 53-year-old male with a longstanding history of renal disease with multiple complications and problems related to vascular access and hypercoagulable state.
The patient had undergone two abdominal procedures following explantation .

(Potentially incorrect annotation)
Baby was vigorous at birth and received routine DR Johnson .

(Contextually correct, but unlear semantics of tokens)
They have opted for conservative management given the size .

(Potentially incorrect annotation)
The patient &apos;s electrolytes and fluids were monitored closely during his admission .
Not predicted as treatment For this reason , he was taken again to the operating room on 01/08/98 , at which time a gastrojejunostomy was performed with stapling across the gastroduodenal junction , introduction of five Jackson-Pratt drains into the retroperitoneal mass , cholecystotomy tube , gastrostomy tube , and jejunostomy tube .

(Potentially incorrect annotation)
He had daily transfusions of FFP and blood , and continued coagulopathy .
During the seizure , he bit his tongue , resulting in a large bleed from his tongue , which was sutured by the ENT Service , however , his continued coagulopathy resulted in bleeding from his nasopharynx , which could never be identified , and also upper gastrointestinal bleeding .
He was transferred to the newborn nursery for further care .
At this point CMED was consulted again and he was transferred to the CMED triage for further care . Mr. Liley was discharged in stable condition , ambulating and voiding independently , and with adequate pain control .
He was advanced to clears which he tolerated and was restarted on a low residue diet .
After catheter placement , the patient was started on sips .

Predicted as treatment instead of problem
The patient was found to have substantial auto PEEP with an elevated dead space.

Predicted as test instead of treatment
Tidal volume is 700 with a rate of 10 , 10 of pressure support , and 10 of PEEP , and 100% FiO2 , satting 100% .

Predicted as test but not annotated as such
The physiology at the time of transfer to the Medical Intensive Care Unit was consistent with a septic process .
The patient had recently prior to this admission , had a rise in his alpha feta protein and a biopsy of his liver had shown hepatocellular carcinoma .
Significant for normal head , eyes , ears , nose , throat exam with no scleral icterus .
Severe hypertension , angina , GERD , peptic ulcer disease , status post Billroth II , ovarian cancer , status post TAH-BSO in 1987 with a negative second look laparotomy in 1988 , chronic anemia , chronic left ear infection .

PULSE :
The patient &apos;s electrolytes and fluids were monitored closely during his admission .
Not predicted as test He had 3 specimens sent for C. difficile which were negative .

(Potentially incorrect annotation)
Predicted as problem but not annotated as such Accompanying this during this period were elevations in liver function studies and amylase and lipase consistent with pancreatic inflammation .
Extensive discussions were carried out with Mrs. Less condition was irretrievable .
His immunosuppression was OKT3 and Solu Medrol , and he underwent hemodialysis and ultra filtration to remove fluid .
Last week , she had copious amounts of stool in the range of 500 cc to 1000 cc of liquid stool per day .
Given these findings , she underwent a Sestamibi scan .
The operative report shows that there was an abnormal area below the trachea to the left which represented a cavity between the esophagus , trachea , aorta , and spine that was full of clot .
CT scan showed an anterior mediastinal mass compressing the trachea with air .
However , it did show an anterior mediastinal mass compressing the trachea with a density consistent with a hematoma .
Given these findings , she underwent a CT scan which showed a retrotracheal mass that appeared to obliterate the esophagus .
His bowel function resumed and in fact he had loose stool .
No active issues .
Not predicted as problem He remained pressor dependent , as well as dependent upon mechanical ventilatory assistance .
He had an ongoing requirement for high volume resuscitation and was persistently acidotic despite CV-VVH in the face of bicarbonate replenishment .
The patient also appeared to have stool from his Jackson-Pratt site and right oblique incision .
Mr. Villesatelkscurb is a fifty year old black gentleman with a long history of hepatitis C , believed to be contracted during transfusions in a previous trauma .
Large for gestational age appearance of an infant with tachypnea , grunting and flaring .
Stable , with active problems being respiratory failure , urinary tract infection , and bleeding diathesis .

(Potential annotation issue: generic term)
She had no problems with this .

(Potential annotation issue: generic term)
Her tracheostomy is functioning well , and we have had no problems with it .
PULM -CTAB no w / r / r ABD -nt / nd ; incision c / d / I ; discrete 3x3 cm slightly firm protuberant mass to the R of umbilicus ; no rebound , no guarding .

Similar concepts get integrated with intermediate words
Bloody drainage from his increasingly distended abdomen was noted .
Initially , the patient did well in the Intensive Care Unit , mentally alert , and oriented , however , he continued to be coagulopathic and was requiring large amounts of blood and FFP transfusions , with a resultant pulmonary edema .
-Seek medical attention for fevers ( temp &gt; 101.5 ) , worsening pain , drainage or excessive bleeding from incision , chest pain , shortness of breath , or any other symptoms of concern .
Modifiers included with concept It was felt that the patient had continued massive retroperitoneal contamination and probable multiple enteric fistulae from an ischemic bowel .

On admission medications included Lasix and iron .
However , he became progressively more mentally obtunded .
Her hematocrit continued to drop over the next couple of days despite normal platelet count , normal coags , and no obvious source of bleeding .
She initially presented to Brottlake Medical Center on 9/15/00 complaining of acute onset dysphasia to liquids and solids .

Modifiers not included in concept
While in the ICU he developed seizures , requiring large amounts of intravenous Valium to break his seizures , and he was maintained on Tegretol and Dilantin .
He was noted to be large for gestational age with a weight of 4525 grams .
We have given her Lovenox in the past , and she continues to bleed .
She underwent a parathyroid exploration with resection of a ruptured ectopic right upper parathyroid adenoma , a biopsy of a normal left and right lower parathyroids , resection of a left upper parathyroid , and tracheostomy .
The operative report shows that there was an abnormal area below the trachea to the left which represented a cavity between the esophagus , trachea , aorta , and spine that was full of clot .
Her anterior neck and chest had a 1 cm to 2 cm area of ecchymosis .
PULM -CTAB no w / r / r ABD -nt / nd ; incision c / d / I ; discrete 3x3 cm slightly firm protuberant mass to the R of umbilicus ; no rebound , no guarding .
Predicted as a different IOB tag but right type Endoscopy never revealed specific sources of bleeding , although he appeared to have a diffuse duodenitis .
2) 2+ E. coli sensitive to Ampicillin and Levofloxacin , Only subconcept recognized She had an anastomosis between the left internal mammary artery and the left anterior descending artery , saphenous vein graft to RCA , saphenous vein graft to OM .
It was believed that she was continuing to bleed in her chest . Blood pressure (BP) was considered as normal until the upper limit of 120; BP should be treated above systolic 130 vs 140 vs 150 (elderly) (controversial); serum albumin was considered as normal within the range 3.5-5.5 g/dL (35-55 g/L); Globulins, total was considered as normal within the range 2.5-3.5 g/dL (25-35 g/L); Epinephrine, plasma (supine) was considered as normal when less than 75 ng/L; Body Mass Index (BMI) within the range 18.5-24.9 was considered normal, within 25-29.9 range was considered as overweight, and ≥30 was considered to be obese; serum calcium was considered as normal within the range 9-10.5 mg/dl; low-density lipoprotein (LDL) was considered desirable when less than or equal to 130 mg/dL (3.36 mmol/L); Potassium, serum was considered as normal in the range 3.5-5.0 meq/L (3.5-5.0 mmol/L)

Premise Hypothesis Label
The patient has chest pain and ST segment elevation on ECG The patient may be having an MI Entailment The patient's blood glucose is 600 The patient is having hyperglycemia Entailment The patient's blood glucose is 600 The patient is having hypoglycemia Contradiction The patient's blood glucose is 30 The patient is having hypoglycemia Entailment The patient took his medication and then the patient got sick The patient got sick after taking medication Entailment The patient took his medication and then the patient got sick The patient got sick before taking medication Contradiction The patient has glucose of 25 The patient has low blood glucose Entailment The patient has glucose of 10 The patient has low blood glucose Entailment The patient has glucose of 500 The patient has high blood glucose Entailment The patient has glucose of 25 The patient has high blood glucose Contradiction The patient has glucose of 25 The patient has low blood glucose Entailment The patient has glucose of 25 The patient has hypoglycemia Entailment The patient has glucose of 25 The patient has low blood sugar Entailment The patient has blood glucose of 25 The patient has low blood sugar Entailment The patient's blood glucose is 25 The patient has low blood sugar Entailment The patient's blood glucose is 25 The patient has hypoglycemia Entailment The patient's blood glucose is 25 The patient has hyperglycemia Contradiction The patient's blood pressure is 180/110 The patient has hypertension Entailment The patient's blood pressure is 180/110 The patient has hypotension Contradiction The patient's blood pressure is 60/0 The patient has hypotension Entailment The patient's blood pressure is 60/0 The patient is in shock Entailment The patient's blood pressure is 60/0 The patient may be in shock Entailment The patient can not smell coffee The patient has anosmia Entailment The patient can not smell anything The patient has anosmia Entailment The patient can not hear anything The patient may be deaf Entailment The patient has septicemia The patient may have a cytokine storm Entailment The patient has septicemia The patient has bacteria growing in the blood stream Entailment The patient has a history of hypertension. Now the patient is short of breath with any exertion The patient may have CHF Entailment The patient has a history of hypertension. Now the patient is short of breath with any exertion and a reduced left ventricular ejection fraction The patient may have CHF Entailment The patient has a BMI of 20 The patient is obese Contradiction The patient has a BMI of 35 The patient is obese Entailment The patient has a BMI of 60 The patient is obese Entailment The patient has a BMI of 25 The patient has a healthy weight Contradiction The patient has a BMI of 25 The patient is overweight Entailment The patient's pulse is 120 The patient has tachycardia Entailment The patient's pulse is 40 The patient has bradycardia Entailment The patient's pulse is 120 The patient has bradycardia Contradiction The patient's has right lower quadrant pain and a high fever The patient may have a ruptured appendix Entailment The patient's has right lower quadrant pain and a high fever The patient may have appendicitis Entailment The patient's had the sudden onset of pain and tenderness on the right abdomen. The patient may have appendicitis Entailment The patient had the sudden onset of pain and tenderness on the right lower abdomen. The patient may have appendicitis Entailment The patient had the sudden onset of pain and tenderness on the right lower abdomen. The abdomen shows board-like rigidity The patient may have appendicitis Entailment The patient had the sudden onset of pain and tenderness on the right lower abdomen. The abdomen shows board-like rigidity. The patient has a low grade fever.

The patient may have appendicitis Entailment
The patient had the sudden onset of pain and tenderness on the right lower abdomen. The abdomen shows board-like rigidity. The patient has a low grade fever.

The patient may have an acute abdomen Entailment
The patient had the sudden onset of pain and tenderness on the right lower abdomen. The abdomen shows board-like rigidity. The patient has a low grade fever.
The patient should be evaluated by a surgeon Entailment The patient has positive blood cultures for e. coli The patient has bacteremia Entailment The patient has positive blood cultures for e. coli The patient has septicemia Neutral The patient is taking a blood pressure medicine. The patient gets dizzy when he suddenly stands up.
The patient may be having an adverse drug reaction to the blood pressure medicine Entailment The patient is taking a blood pressure medicine. The patient is hungry.
The patient may be having an adverse drug reaction to the blood pressure medicine Neutral The patient is taking a blood pressure medicine. The patient is in his usual state of good health.
The patient may be having an adverse drug reaction to the blood pressure medicine Contradiction The patient is taking an immune suppressant medication. The patient has an opportunistic infection The patient may be having an adverse drug reaction to the medication Entailment The patient is taking an immune suppressant medication. The patient has an infection with an opportunistic organism The patient may be immune compromised Entailment The patient has suicidal ideation and plans to kill himself The patient should have a consultation with a psychiatrist Entailment The patient has suicidal ideation and plans to kill himself The patient is at risk for suicide Entailment The patient denies suicidal ideation The patient is at risk for suicide Contradiction The patient is happy The patient is at risk for suicide Contradiction The patient is an adolescent, and began taking an SSRI 1 week ago The patient is at risk for suicide Entailment The patient is an adolescent, and began taking an SSRI 20 weeks ago The patient is at risk for suicide Neutral The patient is an adolescent The patient is at risk for suicide Neutral The patient's serum calcium is 7 The patient has hypocalcemia Entailment The patient's serum calcium is 6 The patient has hypocalcemia Entailment The patient's serum calcium is 8 The patient has hypocalcemia Entailment The patient's serum calcium is 9 The patient has hypocalcemia Contradiction The patient's serum calcium is 9 The patient has hypercalcemia Contradiction The patient's serum calcium is 9 The patient has normal serum calcium Entailment The patient's serum calcium is 10 The patient has normal serum calcium Entailment The patient's serum calcium is 11 The patient has normal serum calcium Contradiction The patient's serum calcium is 12 The patient has normal serum calcium Contradiction The patient's serum calcium is 13 The patient has normal serum calcium Contradiction The patient's serum albumin is 2.5 g/dL The patient has low albumin Entailment The patient's serum albumin is 4 g/dL The patient has low albumin Contradiction The patient's serum albumin is 5 g/dL The patient has low albumin Contradiction The patient's serum albumin is 10 g/dL The patient has low albumin Contradiction The patient's serum albumin is 50 g/dL The patient has low albumin Contradiction The patient's serum albumin is 100 g/dL The patient has low albumin Contradiction The patient's serum albumin is 100 g/dL The patient has hypoalbuminemia Contradiction The patient's serum albumin is 100 g/dL The patient has hyperalbuminemia Entailment The patient's serum globulins is 3.0 The patient has low globulins Contradiction The patient's LDL is 200 The patient has high LDL Entailment The patient's LDL is 100 The patient has high LDL Contradiction The patient's LDL is 150 The patient has high LDL Entailment The patient's LDL is 140 The patient has high LDL Entailment The patient's plasma epinephrine is 50 ng/L The patient's plasma epinephrine is high Contradiction The patient's plasma epinephrine is 100 ng/L The patient's plasma epinephrine is high Entailment The patient's plasma epinephrine is 200 ng/L The patient's plasma epinephrine is high Entailment The patient's serum potassium is 2.0 The patient has hypokalemia Entailment The patient's serum potassium is 6.0 The patient has hypokalemia Contradiction The patient's serum potassium is 6.0 The patient has hyperkalemia Entailment The patient's serum potassium is 6.0 The patient has hyponatremia Neutral The patient's serum potassium is 6.0 The patient has hypernatremia Neutral The patient's serum potassium is 6.0 The patient has high cholesterol Neutral The patient's hematocrit is 30 The patient is anemic Entailment The patient's hematocrit is 50 The patient is anemic Contradiction