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:
- Generating the comprehensive clinical history, including anamnesis.
- Classifying the health problem based on the relevant medical area. If needed, a customized clinical history can be developed in greater detail.
- Providing a complete patient description, including their symptoms and specific concerns.
- Based on the patient's situation and the gathered information, a general medical diagnosis can be made.
- 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.
The first step is the preprocessing of the data
The second step is the clustering of the cases. Can be useful to understand better our data.
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.
Having the data well built.
Features + Description + Patient + Answer
Is simply build a model to answer custom questions.
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.
We can install Sentence BERT using:
!pip install sentence-transformers
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.
from sentence_transformers import SentenceTransformer
sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')We will then encode the provided sentences. We can also display the sentence vectors(just uncomment the code below)
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])Then we will define a test query and encode it as well:
query = "I had pizza and pasta"
query_vec = model.encode([query])[0]We will then compute the cosine similarity using scipy. We will retrieve the similarity values between the sentences and our test query:
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.