ai-medical-chatbot-master/3-Modeling/README.md

# Part 3 - Modeling of Free Doctor with AI

[back](../README.md)

To provide a more accurate diagnosis for each patient, it is necessary to analyze the data flow. The standard procedure for a doctor is as follows:

1. Generating the comprehensive clinical history, including anamnesis.
2. Classifying the health problem based on the relevant medical area. If needed, a customized clinical history can be developed in greater detail.
3. Providing a complete patient description, including their symptoms and specific concerns.
4. Based on the patient's situation and the gathered information, a general medical diagnosis can be made.
5. If necessary, further evaluation for specific cases can be conducted, repeating step 4.

It is strongly recommended to maintain a clinical history for every patient treated within this program.

## Modeling of Doctor AI

The first step is the preprocessing of the data

### [3_1-Preproces.ipynb](https://github.com/ruslanmv/Free-Doctor-with-Artificial-Intelligence/blob/master/3-Modeling/3_1-Preproces.ipynb)

The second step is the clustering of the cases. Can be useful to understand better our data.

### [3_2-Clustering.ipynb](https://github.com/ruslanmv/Free-Doctor-with-Artificial-Intelligence/blob/master/3-Modeling/3_2-Clustering.ipynb)

The third  step is classical feature engineering, here we should create a syntenic clinical history for each visit of with the AI doctor.

For future releases our raw data must be included as a part of the model.

Due to lack of data we create a synthetic clinical history based on the description of the patient.

### [3_3-Features.ipynb](https://github.com/ruslanmv/Free-Doctor-with-Artificial-Intelligence/blob/master/3-Modeling/3_3-Features.ipynb)

Having the data well built.

Features + Description + Patient + Answer 

Is simply build a model to answer custom questions.

### [3_4-Generative.ipynb](https://github.com/ruslanmv/Free-Doctor-with-Artificial-Intelligence/blob/master/3-Modeling/3_4-Generative.ipynb)

# Additional Notes

## General clinical history 

A clinical history is an essential component of a patient's medical record and provides a concise overview of the patient's medical background, including their past illnesses, surgeries, medications, allergies, and family medical history. Here's a sample format for a clinical history:

```
[Patient Information]
- Full Name: [Patient's Full Name]
- Date of Birth: [Patient's Date of Birth]
- Gender: [Patient's Gender]
- Address: [Patient's Address]
- Phone Number: [Patient's Contact Number]

[Chief Complaint]
- [Description of the patient's main reason for seeking medical attention]

[Present Illness]
- [Detailed description of the current illness or symptoms, including their onset, duration, severity, and any relevant factors]

[Medical History]
- Past Medical Conditions:
  - [List any significant medical conditions the patient has had, including dates of diagnosis]
- Surgeries/Procedures:
  - [List any surgeries or medical procedures the patient has undergone, including dates]
- Medications:
  - [List current medications, dosages, and frequency]
- Allergies:
  - [List any allergies the patient has, including medication, food, or environmental allergies]
- Immunizations:
  - [Include information on relevant vaccinations and their dates]

[Family Medical History]
- [List any significant medical conditions that run in the patient's family, such as heart disease, diabetes, cancer, etc.]

[Social History]
- Occupation: [Patient's occupation]
- Tobacco Use: [Specify if the patient smokes or uses tobacco products]
- Alcohol Use: [Specify if the patient consumes alcohol and if so, how often and in what quantities]
- Drug Use: [Specify if the patient uses recreational drugs or has a history of drug use]
- Diet: [Provide information about the patient's dietary habits, including any special diets]
- Exercise: [Describe the patient's level of physical activity]

[Review of Systems]
- [List and briefly describe the patient's symptoms or concerns related to various body systems, including cardiovascular, respiratory, gastrointestinal, musculoskeletal, etc.]

[Social and Environmental History]
- [Include information about the patient's living situation, relationships, and any environmental factors that may be relevant to their health]

[Psychosocial History]
- [Note any significant mental health history or psychosocial stressors]

[Sexual History]
- [Include relevant sexual history information if applicable]

[Substance Use History]
- [Detail any history of alcohol or substance abuse, if applicable]

[Physical Examination Findings]
- [Summarize any relevant physical examination findings, including vital signs, general appearance, and specific organ system assessments]

[Assessment and Plan]
- [Provide a brief assessment of the patient's current medical condition and a plan for further evaluation and treatment]

[Provider's Name and Credentials]
- [Name of the healthcare provider]
- [Credentials, such as MD, DO, NP, PA]

[Date]
- [Date of the clinical history]

[Signature]
- [Signature of the healthcare provider]
```

This format can be customized to fit the specific requirements of a healthcare facility or the preferences of the healthcare provider. It should be thorough and comprehensive to ensure that all relevant information is documented accurately.

## How  to use Bert

We can install Sentence BERT using:

```
!pip install sentence-transformers
```

#### Step 1:

We will then load the pre-trained BERT model. There are many other pre-trained models available. You can find the full list of models [here.](https://github.com/UKPLab/sentence-transformers/blob/master/docs/pretrained-models/sts-models.md)

```python
from sentence_transformers import SentenceTransformer
sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')
```

#### Step 2:

We will then encode the provided sentences. We can also display the sentence vectors(just uncomment the code below)

```python
sentence_embeddings = model.encode(sentences)
#print('Sample BERT embedding vector - length', len(sentence_embeddings[0]))
#print('Sample BERT embedding vector - note includes negative values', sentence_embeddings[0])
```

####  

#### Step 3:

Then we will define a test query and encode it as well:

```python
query = "I had pizza and pasta"
query_vec = model.encode([query])[0]
```

#### Step 4:

We will then compute the cosine similarity using scipy. We will retrieve the similarity values between the sentences and our test query:

```python
for sent in sentences:
  sim = cosine(query_vec, model.encode([sent])[0])
  print("Sentence = ", sent, "; similarity = ", sim)
```

There you go, we have obtained the similarity between the sentences in our text and our test sentence. A crucial point to note is that SentenceBERT is pretty slow if you want to train it from scratch.