Lecture 7 Flashcards

You may prefer our related Brainscape-certified flashcards:
1
Q

Natural Language Processing in Healthcare

A
  • Manual annotations take a long time, scales poorly
    o Want to automatically generate annotations using semisupervised learning
  • Can make use of specific markers described in the report
    o E.g. number of significant lesions -> extracted using regular expressions or predicted
    by NLP algorithms
  • Standard pipeline
    o Preprocessing: tokenisation, stemming, normalisation
    o Transformation: indexing, featurizing
    o Mining: NLP, information extraction
  • Text mining: recovering insights from unstructured data
    o Does the report mention presence of cancer?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Classical Text Mining methods

A
  • Regular expressions
    o Especially useful for highly structured texts
    o But often does not generalise well
  • Exact/approximate string matching
    o Implemented using some distance metric
    ▪ Jaccard similarity -> measures similarity of two sets, defined as IoU
  • “Obesity” -> “Obesety” -> “Obeset” -> “Obese” = 3
    ▪ Levenshtein distance -> edit distance, in how many insertions, deletions and
    substitutions can we transform some word into another word
  • “Obesity” = [O, b, e, s, i, t, y], “Obese” = [O, b, e, s, e]
  • Intersection: [O, b, e, s], union: [O, b, e, s, i, t, y], IoU = 4/7
    o Threshold to determine match
  • Lacks contextual information
  • Semantic similarity is not captured
  • Quickly becomes complex
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Classical Machine Learning

A
  • Aims to add semantic meaning
    o Word2vec
    o Similar words (e.g. male-female version of
    words) should have similar distances
  • Problems with contextual information and unseen words
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Modern Machine Learning Methods

A
  • Typically Large Language Models
  • Unsupervised training on massive corpora
  • BERT: masked language modelling
    o Predict a masked word given the context, cannot make new data
  • GPT: causal language modelling
    o Predict the next word given the context, cannot make new data
  • No problems with unseen words, adds contextual representations
  • Also able to be paralellised for training and inference
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Clinical NLP tasks

A
  • Classification
    o E.g. what is the diagnosis in the report?
  • Regression
    o E.g. what is the reported lesion size?
  • Named entity recognition
    o E.g. which specific areas are mentioned in the report?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Large Language Models

A
  • Pretraining: gather information from huge corpus
  • Finetuning: tune for output format to allow
    information extracting
    o Requires:
    ▪ Labelled dataset of input/output
    pairs
    ▪ Model weights of a foundation LLM or finetuning API
    o Update weights to minimise perplexity
    ▪ How likely a model is to generate the input text sequence
  • Reinforcement Learning from Human Feedback (RLHF): tune for human
    interaction/preferences
  • Must be aware of what we upload -> medical data is highly sensitive
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Vision Language Models

A
  • Contrastive Language-Image Pre-training
    (CLIP): predict which of the NxN possible
    (image,text) pairs in one batch actually
    occurred
  • Separate text and image embedding
  • Maximise cosine similarity between real
    pairs, for other pairs minimise it
  • Evaluation: zero-shot classification
    o Use large pre-trained models that classify without being
    trained on particular use case
  • Use name of all classes as potential text pair
    o Predict most probable (image, text) pair and use it as the predicted class
  • Bias is present, because we can add custom classes
    and the model will find results that match the class
  • Uses only global representations, but for medical
    images we may need subtle visual cues to
    distinguish between normal and abnormal
    o Add attention to network, jointly learn
    global and local representations
    o Complement information from both the
    full images and critical local region of
    interest
  • Temporal ambiguity: must be able to match image
    with report it belongs to
    o A report may report changes since previous report, but this is not captured in an
    image
    o Solution: use prior and current image and compare report to multiple sequential
    images
How well did you know this?
1
Not at all
2
3
4
5
Perfectly